Land use/land cover (LULC) classification using hyperspectral images: a review

Figures & data

Figure 1. Common frameworks for HSIs processing.

Figure 2. The number of recently published studies in the field of LULC using HSIs.

Table 1. Relevant information on the review of land cover/land use hyperspectral image classification in recent years.

Reference	Dataset	Main work	Limitation
(Li et al. 2019)	Houston data, University of Pavia (PU), and Salinas (SA)	A systematic review of deep learning-based HSI classification.	There are only three datasets mentioned in this review. If more HSI datasets are used for testing, it can better validate the relevant conclusions and make the review more rigorous.
(Moharram and Meena Sundaram 2023)	Indian Pines (IP), PU, SA, Botswana, and Kennedy Space Center (KSC)	A systematic review of dimensionality reduction methods for land cover/land use HSI classification	− This review explores Machine Learning (ML) and DL techniques for the HSI classification of LULC but does not discuss other promising classification techniques. − This review emphasizes dimensionality reduction strategies and does not delve into in-depth discussions on classification methods
(Gu et al. 2017)	IP, SA, PU, Bayview Park, and Recology	A systematic review of Multi-kernel Learning (MKL) methods for HSI classification	This review is limited to multi-kernel learning algorithms for HSI classification and does not explore other promising techniques for processing HSI classification.
(Bera, Shrivastava, and Chandra Satapathy 2022)	KSC, IP, PU, and SA	A systematic review of different convolutional neural network (CNN) architectures for HSI classification	It focuses on the impact of convolutional neural networks with different architectures on HSI classification, without exploring the impact of other promising technologies on HSI classification and analyzing the performance gap between these technologies.
(Wang, Liu et al. 2023)	IP, Salinas Valley (SV), PU, and WHU-Hi-LongKou (LK)	A systematic review on the topic of small sample HSI classification	It does not focus on research in a specific field, such as LULC hyperspectral classification.

Figure 3. Review directory.

Table 2. Specific introduction to literature related to hyperspectral image classification.

Reference	Sensor	Dataset	Model	Overall Accuracy	Remark
(Sellami et al. 2020)	AVIRIS	IP and SA	A fused 3D CNN model	96.98% and 97.65%	The fusion of 3-D CNN outperforms other advanced deep learning methods (PL, DS, CNN, GCN, MDL, 3DL, FST, MSC, and 3D-CNN) by more effectively identifying relevant spectral bands and preserving spectral-spatial features, resulting in higher classification accuracy.
(Wang, Tan, et al. 2023)	AVIRIS, ROSIS, Advanced HSI (AHSI), and CASI-1500	IP, PU, Kansas AHSI, and Houston CASI	capsule-vectored neural network (CVNN)	85.58%, 92.48%, 88.14%, and 89.16%	− CVNN effectively addresses deep learning modeling with limited labeled samples, achieving a balance between highlighting secondary features and reducing salt and pepper noise. − CVNN surpasses other methods (SVM, CapsNet-4, MSRN, A2S2ResNet, FPGA, UML, S3Net, CNN-4) in classification accuracy.
(Wan et al. 2022)	AVIRIS, AVIRIS, and Airborne Laser Mapping	IP, SA, and Houston University	Multi-level Graph Learning Network (MGLN)	94.27%, 98.39%, and 93.03%	MGLN outperformed other baseline methods (MFL, DR-CNN, CNN-PPF, HybridSN, A2S2K, SSAN, S2GCN, MDGCN, miniGCN) in efficiency and performance across various datasets, achieving an optimal balance between classification accuracy and runtime.
(Wang, Li, and Zhang 2021)	AVIRIS, ROSIS, and AVIRIS	IP, PU, and SA	Discriminative graph convolution networks (DGCN)	94.25%, 96.21%, and 99.66%	DGCN demonstrates superior performance over other deep learning methods (GCN, S^2GCN, MDGCN, CNN-PPF, CAD-GCN) and traditional machine learning methods (MBCTU, JSDF).
(Du, Tan, and Xing 2012)	OMIS II Hyperion AVIRIS	OMIS II (HSI of Changping, Beijing, China), EO-1 Hyperion image, and IP	Adaptive binary tree support vector machines (ABTSVM)	96.52%, 97.12%, and 97.92%	ABTSVM outshines common multi-SVM strategies (1-a-a SVM, 1-a-1 SVM, DDAG SVM, SBSVM, BBTSVM) and traditional classifiers (MDC, SAM) in overall classification accuracy.
(Senthil Kumar et al. 2010)	spectral library, and AVIRIS	a laboratory simulated image, and 92AC3C	A spectral matching approach by combining variable interval spectral average (VISA) and spectral curve matching (SCM)	99.71%, and 83.28%	− The VISA method, a variation of the time-averaged approach, merges Spectral Curve Matching (SCM) with VISA. This hybrid technique not only facilitates multi-resolution analysis of spectral features but also bolsters the digital correlation within the least squares fitting model. − When compared to methods like Spectral Angle Mapper (SAM), Spectral Distance Similarity (SDS), Spectral Correlation Similarity (SCS), and spectral similarity value (SSV), the combined VISA and SCM method shows superior performance.

Figure 4. Pseudo-color visualization of all hyperspectral datasets.

Table 3. The HSI datasets description and their sources.

#	Dataset	Size	Spectral Bands	classes	Spatial Resolution	Sources	Reference
1	Indian Pines	145 × 145	200	16	20m	https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes	(Carranza-García, García-Gutiérrez, and Riquelme 2019)
2	Salinas	512 × 217	204	16	3.7m	https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes
3	Pavia University	610 × 340	103	9	1.3m	https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes
4	Mississippi Gulfport	325 × 220	64	11	-	https://github.com/GatorSense/MUUFLGulfport	(Sun, Zheng, and Lu 2021)
5	HyRANK dataset	Dioni: 250 × 1376; Loukia: 249  ×  945	176	14	30m	https://pan.baidu.com/s/1RvzFl7eW8_CwajgMA03edA?pwd=1234	(Christovam et al. 2019)
6	Berlin	300 × 300	114	5	3.5m	http://www.dlr.de	(Akbari et al. 2016)
7	Washington DC Mall	1208 × 307	191	7	1.5m	https://engineering.purdue.edu/~biehl/
8	Quebec City	795 × 564	84	6	1m	http://www.telops.com/
9	Pavia Centre	481 × 291	102	9	1.3m	https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes	(Wang et al. 2020)
10	KSC dataset	512 × 614	176	13	18m	https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes	(Jun and Ghosh 2011)
11	Botswana	1476 × 256	145	14	30m	https://rslab.ut.ac.ir/data
12	WHU-Hi-LongKou data	550 × 400	270	9	0.463m	http://rsidea.whu.edu.cn/resource_WHUHi_sharing.htm	(Zhong et al. 2020)

Table 4. Examples of dimensionality reduction literature.

Reference	Sensor	Dataset	Model	Overall Accuracy	Remark
(Zhang et al. 2023)	Hyperspec VNIR A-series camera	Medical HSIs (MHSI) dataset 8–01 Medical HSIs (MHSI) dataset 12–01 Medical HSIs (MHSI) dataset 13–01	data gravitation and weak correlation-based ranking (DGWCR)	94.48% 98.50% 99.17%	− In tests using SVM, KNN, and RF classifiers with methods like MVPCA, VGBS, and others on three MHSI datasets, DGWCR consistently outperformed all in denoising effectiveness. − DGWCR excelled in average classification accuracy and had faster running times compared to deep learning methods like ACNN and BS-Net, proving its efficiency and suitability for clinical use.
(Yuan et al. 2022)	HySpex SWIR-320 m-e push-broom imaging spectrometer	HSIs of three peanut varieties, namely Huayu (HY), Silihong (SLH), and Xiaobaisha (XBS).	A point-centered CNN combined with embedded feature selection (PCNN-FS)	HP-STMP (Validation set) 96.67% HP-LTMP(Validation set) 98.32%	− PCNN-FS outperforms traditional methods (SPA-SVM, SPA-GLCM-SVM, CARS-SVM, CARS-GLCM-SVM), other CNN-based models (1-D CNN, Reshaped image CNN), and PCA-PCNN, JMD-PCNN, T indicator-PCNN in classification accuracy. Its performance is nearly on par with full-band results. − PCNN-FS, in comparison to full-band PCNN, simplifies the model while reducing time and memory requirements.
(Elmaizi et al. 2019)	AVIRIS ROSIS	Indiana dataset Pavia university dataset	Information gain-based selection filter(IGBS)	95.25% 96.83%	IGBS outperforms algorithms mentioned in the relevant work section (MRMR, MIFS, MIFB) in classification accuracy: it exceeds MRMR by 2%, MIFS, and MIFB by over 2.5% on the Indian dataset, and outperforms MIFB by 4%, MIFS, and MRMR by 1% on the Pavia database. Overall, IGBS’s superiority lies in its handling of redundancy, correlation, and complementarity.
(Dalal, Al-qaness, et al. 2023)	AVIRIS AVIRIS Hyperion	IP KSC Dioni (Di) Loukia (Lk)	Improving distribution analysis (IDA)	IDA+ (HybridCNN, HybridSN, FUSENet) For IP = (99.26%, 99.64%, 98.57%, 80.38%) For KSC = (99.57%, 98.96%, 98.92%, 96.52%) For Di = (99.54%, 99.48%, 99.39%, 98.54%) For Lk = (93.86%, 95.17%, 93.62%, 90.68%)	− Using the IP dataset, IDA outperformed ICA, SVD, UMAP, and PCA by reducing over 80% of outliers, significantly improving data distribution. − Across three classifiers (SVM, NN, 2D-CNN) and four datasets, IDA consistently achieved higher accuracy, particularly excelling on the complex KSC dataset and showing strong performance on the simpler Di and Lk datasets. − When paired with three models (HybridCNN, HybridSN, FUSENet) on four hyperspectral datasets, IDA not only expedited training and testing times but also notably enhanced spectral, spatial, and spectral-spatial features.
(Jin et al. 2022)	HSIs of high-quality wheat seed varieties in Henan, Shandong, and Shaanxi	HSIs of 20 wheat varieties in the Zhoumai, Zhengmai, Bainong, and other series	a wheat seed variety identification method based on spatial-spectral feature extraction	Train/Test data=2:1 97.55% Train/Test data=3:1 97.10% Train/Test data=4:1 97.54% Train/Test data=5:1 98.35%	− The proposed method, tested on three single-series datasets (Zhou Mai, Zheng Mai, Bainong), surpassed NN, KELM, VAE, DBN, KNN, and SVM-S in classification accuracy, particularly with fewer training samples, peaking at 99.02% accuracy as sample size increased. − In comparative tests, the study’s classification model maintained an accuracy above 98.5%, consistently outperforming six other methods.
(Zabalza et al. 2016)	AVIRIS ROSIS	92AV3C data set Pavia CA dataset	Segmented stacked autoencoder (S-SAE)	number of features = 20 80.66% number of features = 5 97.42%	− In comparative tests on the 92AV3C and Pavia CA hyperspectral datasets, S-SAE generally surpassed PCA, ICA, MNF, EMD, SSA, and SAE in classification accuracy. Although SSA had higher accuracy on 92AV3C, it required more features, while S-SAE consistently delivered superior accuracy across both datasets. − Complexity analysis of S-SAE on the same datasets showed a significant reduction in complexity—about 50% for Pavia CA and 67% for 92AV3C—owing to S-SAE’s effectiveness in avoiding irrelevant neural connections.

Table 5. Examples of supervised learning literature.

Reference	Sensor	Dataset	Model	Overall Accuracy	Remark
(Petropoulos, Arvanitis, and Sigrimis 2012)	A satellite hyperspectral sensor	Hyperion imagery	SVMs ANNs	89.26% 85.95%	The classification accuracy of SVMs and ANNs for the Hyperion dataset is not significantly different, with SVMs having a 3.31% higher overall accuracy than ANNs.
(Shah, Liu, and Hayat 2016)	AVIRIS	Indian Pines dataset	RoBOO	93.1780%	The RoBOO strategy, integrating multi-resolution segmentation (MRS), SVM, and nearest neighborhood algorithm, outperforms traditional SVM methods and the renowned random subspace SVM (RSSVM) algorithm. Its superior performance stems from a combination of random feature selection and PCA, enhancing diversity.
(Dahiya, Gupta, and Singh 2021)	Hyperion	Hyperion EO-1	RF NN minimum distance classifier(MDC)	85% 88% 83%	Three supervision methods were tested on the Hyperion EO-1 dataset, and the experimental results showed that the neural network had the best classification performance, followed by RF, and MDC had the worst performance.
(Tong and Zhang 2022)	the AISA Eagle II diffraction grating push-broom hyperspectral imager ROSIS	Hyperspectral images of a forest farm in Guangxi Province, China Pavia University	SSCMRF	97.50% 99.13%	1. Using a hyperspectral dataset from a Guangxi forest farm, the proposed method outperformed EAPs, IAPs, SuperPCA, MSuperPCA, 3D-CNN, and DCDRF, achieving the highest OA, AA, and Kappa. 2. Tested on the PU dataset, the proposed method, while not achieving the highest overall accuracy, reached a maximum classification accuracy of 99.36, surpassing other methods.
(Li et al. 2023)	ROSIS ITERS CASI-1500	Pavia University Houston	OSC-SCL	93.4±1.8% 87.6±2.2%	1. OSC-SCL outperforms CROSR, MDL4OW, and SSLR in classification performance on both PU and Houston datasets. 2. Despite its relatively longer training times of 137.96s on the PU dataset and 355.99 s on the Houston dataset compared to other methods, the proposed approach still demonstrates competitive performance.

Table 6. Examples of unsupervised learning literature.

Reference	Sensor	Dataset	Model	Overall Accuracy	Remark
(Kang et al. 2017)	AVIRIS ROSIS AVIRIS	Indian Pines image Pavia University Salinas image	A novel PCA-based Edge-preserving features (PCA-EPFs)	The training samples account for 1% of the IP 83.57(3.60)% The training samples are 20 for each PU class 95.00(1.52)% Ten SA with 10 labeled samples per class as training set 97.06 (2.19)%	Compared with SVM, the edge-preserving filtering-based postprocessing method (EPFP), the multiple feature learning (MFL)-based method, LBPs-based method, the extended morphological attribute profiles (EMAP), the weighted Markov random fields (WMRF)-based method, and the image fusion and recursive filtering (IFRF)-based method, the PCA-EPFs method consistently achieved higher classification accuracy on three real hyperspectral datasets. However, this method requires manual parameter selection and lacks operability.
(Xie et al. 2018)	AVIRIS AVIRIS ROSIS	Indian pines Salinas Pavia University	ME-KSVD + multi-scale strategies	98.5% 98.4% 98.7%	ME-KSVD+Multiscale achieves superior classification accuracy compared to SVM, HMRF, MASR, SR+EMP, MFASR, KSVD+Pixelwise, KSVD+Singlescale, ME-KSVD + Pixelwise, and ME-KSVD + Singlescale.
(Chen et al. 2021)	AVIRIS ITERS CASI-1500 ROSIS Headwall Nano-Hyperspectral	Indian pines Houston 2013 Pavia University WHU-Hi-LongKou	PLG-KELM	Training set=10% 99.05% Training set=10% 97.14% Training set=10% 99.10% Training set=10% 97.85%	1. PLG-KELM outperforms SVM, BLS, PCA-CNN, CAE-CNN, and KELM in classification performance across four hyperspectral datasets. 2. PLG-KELM excels in extracting complex features from hyperspectral datasets and effectively mitigates the salt and pepper phenomenon.
(Zhao et al. 2022)	ROSIS ITRES CASI-1500	Pavia University Houston 2013	Contrastive self-supervised learning	Labeled Fraction 1% 97.69±0.73% 88.25±1.8%	Setting the training sample size to 1% of the total, the proposed method demonstrated the highest overall accuracy compared to SVM-RBF, 2D-CNN, HybridSN, and 3DOC-SSAN. The OA, AA, and Kappa coefficients consistently surpassed 90% on the PU dataset and ranged between 80% − 90% on the Houston 2013 dataset.
(Liu et al. 2023)	AVIRIS AVIRIS AVIRIS the Gaiasky mini2 VN HSI camera	Salinas SalinasA Indian Pines The self-collected HSI datasets	PSSA	71% 82% 49% 51% 82% 77% 64% 57%	This study evaluated three public datasets and five self-collected aviation hyperspectral datasets, comparing three unsupervised and two supervised methods. Results indicate that the proposed method excels among unsupervised approaches, with an overall classification accuracy difference of approximately 10–30% compared to supervised methods.

Table 7. Examples of semi-supervised learning literature.

Reference	Sensor	Dataset	Model	Overall Accuracy	Remark
(Yi et al. 2018)	AVIRIS AVIRIS	Indian Pines Salinas Botswana	SBLS	92.47% 96.14% 99.32%	1. Compared to traditional classifiers (SVM, ELM, State-preserving ELM), SSG, CNN-PPF, HiFi-We, and BLS, the SBLS method demonstrated the highest classification accuracy on three real hyperspectral datasets. 2. Despite longer training times of 420.02 s, 240.26 s, and 70.97 s on these datasets, the proposed methods still showcase competitive performance.
(Hong et al. 2019)	ITRES-CASI-1500. Headwall’s Hyperspec-VNIR-C. Multiple sensors	Houston Chikusei DFC2018	LeMA	LSVM :73.42% CCF : 76.35% LSVM : 75.11% CCF : 81.71% LSVM : 61.69% CCF : 79.98%	Introducing three datasets, this study employed LSVM and CCF as classifiers. Compared to the Baseline, GLP, SMA, S-SMA, CoSpace, and S-CoSpace methods, the proposed method, especially when combined with the CCF classifier, achieves the highest classification accuracy.
(Xi et al. 2021)	AVIRIS ROSIS	Indian Pines Pavia University	SSGPN	94.50% 93.77%	Involving public datasets (IP and PU), this study compared CNN-PPF, diverse region-CNN, GCN, and spectral-spatial GCN (S2GCN). Results show that the SSGPN method achieves the highest OA, AA, and Kappa due to minimal noise on the IP and PU datasets and clear edge regions on the classification map.
(Song et al. 2022)	AVIRIS ROSIS AVIRIS ITRES CASI-1500	IP PU. Salinas. Houston 2013	SSRNet	With 10 labeled samples: 81.65 ± 1.71% 89.38 ± 1.14% 93.47 ± 1.04% 83.93 ± 0.88%	1. Using four typical hyperspectral datasets, this study compared SVM, SSLSTM, CDCNN, 3DCAE, SSRN, HybridSN, and DBMA. 2. Despite its longer training times on the four datasets (211.84 s, 624.84 s, 795.61 s, and 257.09 s), the proposed method still showcases superior performance.
(Sellami, Farah, and Mura 2023)	AVIRIS ITRES CASI-1500	Indian Pines Houston	SSHCN	d = 9 spectral bands from the HSI dataset 96.84% d = 12 spectral bands from the HSI dataset 95.22%	1. Tested on IP and Houston datasets against seven comparison models (DSVM, F3DCNN, CEGCN, GCN, GAGCN, DGCN, and NGCN), the proposed method demonstrates superior classification accuracy. 2. The proposed method has training times of 284 s and 291 s on the two datasets, slightly longer than the shortest training times of 203 s and 213 s observed in other methods.

Table 8. Examples of transfer learning literature.

Reference	Sensor	Dataset	Model	Overall Accuracy	Remark
(Xie et al. 2021)	AVIRIS ROSIS AVIRIS	Indian Pines Pavia University Salinas	SP-CNN	94.45 ± 0.24% 93.18 ± 0.12% 95.99 ± 0.07%	1. Compared to ANN, CNN, CRNN, CNN-PPF, and SS-CNN, the proposed method achieves the highest overall classification accuracy. 2. Tested with no more than 30 labeled pixels in each class, the proposed method exhibits the shortest training time, at 366 s, 320 s, and 510 s, respectively.
(Zhang et al. 2021)	ITERS CASI-1500 Multi-sensor Hyperion EO-1 Hyperion	Houston 2013 Houston 2018 HyRANK The Shanghai and Hangzhou datasets	TSTnet	Using 5% labels from HOUSTON 2018 75.34±1.96% Using 5% labels from LOUKIA 63.31±3.05% Using 5% labels from SHANGHAI 90.36±1.35% Using 5% Labels from Houston 2018 for Houston 2013 88.38±2.35%	1. Tested on three datasets with 5% labeled samples, the proposed method outperforms other methods (SVM, CEGCN, DAN, DeepCoral, DAAN, MRAN, DSAN, and HTCNN), achieving the highest overall classification accuracy. 2. Conducted with the source scene as Houston 2018 and Houston 2013 as the target scene, the experiment confirms the proposed method’s superior classification performance.
(Hamza et al. 2022)	8mm focal length head wall nano hyperspectral imaging sensor. 17mm focal length head wall nano hyperspectral imaging sensor.	WHU-Hi-LongKou WHU-Hi-HanChuan	SSODTL-CC	99.23% 97.15%	1. The proposed method achieves higher classification accuracy compared to SVM, FNEA-OO, SVRFMC, CNN, and CNN-CRF methods. 2. On the WHU-Hi-LongKou dataset, using all samples, the proposed method attains a maximum average classification accuracy of 99.10%, and with 70% of samples, it reaches 99.19%. In the WHU-Hi-HanChuan dataset, the proposed method achieves a maximum average accuracy of 97.13% with all samples and the same accuracy with 70% of samples.
(Zhang, Zhang, Z. Li et al. 2023)	ITERS CASI-1500 Multi-sensor ROSIS Gaofen-2 satellite	Houston 2013 Houston 2018 Pavia Center GID Dataset (GID-WH)	LDGnet	The Target Scene Houston 2018 Data 80.34±1.74%. The Target Scene Pavia Center Data 82.53±1.24%. The Target Scene GID-WH Data 77.83±1.16%	1. Tested on three datasets, the proposed method outperforms DAAN, MRAN, DSAN, HTCNN, PDEN, LDSDG, and Sag-Net, achieving the highest overall classification accuracy. 2. In a one-epoch time test, the proposed method generally exhibits longer training times, measured at 17.84 s, 68.21 s, and 19.53 s, respectively, compared to other methods.

Figure 5. Structure diagram of traditional convolutional neural networks.

Table 9. Examples of CNNs literature.

Reference	Sensor	Dataset	Model	Overall Accuracy	Remark
(Sarker et al. 2020)	AVIRIS	Indian Pines	RSVD-MCNN	99.99±0.01%	1.Compared to SVM, RF, MLP, 2D-CNN, and 3D-CNN methods, the RSVD-MCNN method has higher accuracy because it can extract high-level features from low-dimensional space to better perform complex representation learning. 2. However, compared to other methods, the proposed method requires longer training time.
(Yu et al. 2021)	AVIRIS AVIRIS ROSIS	Purdue Indiana Indian Pines KSC PU	FADCNN	97.95±0.15% 99.10±0.13% 99.19±0.29%	1. Tested on three hyperspectral datasets, the proposed method shows superior classification performance compared to nine comparison methods (EPF, ITCIMC, MFASR, CNNDH, PCA3-F, PCA5-F, FADC, Bi-RNN, and Res-Net34). 2. In terms of training time, the proposed method takes roughly 270 s, 320 s, and 280 s, which is not the lowest among the compared methods (FADC, PCA3-F, and PCA5-F).
(Ding et al. 2022)	ROSIS AVIRIS ITRES CASI-1500	Pavia University Salinas Houston 2013	MFGCN	99.49±0.16% 99.28±0.37% 95.24±0.51%	This study utilized three HSI datasets and six comparative methods (MSDN, CRNN, S2GCN, MSAGE-CAL, MBCTU, and JSDF) with consistent hyperparameters (superpixel scale, epochs, and learning rate set at 100, 600, and 0.001, respectively). Results demonstrate that the proposed method achieves the highest classification accuracy.
(Arun Solomon and Akila Agnes 2023)	AVIRIS ROSIS	Indian Pines Pavia University	A novel 3D CNN approach	100.00% 98.24%	The experiment was tested on two benchmark HSI datasets, and the overall classification accuracy of the proposed method was 100% and 98.24%, which was superior to the compared KNN, SVM, SAE, and 2DCNN methods.
(Alkhatib et al. 2023)	ROSIS AVIRIS University of Southern Mississippi’s-Gulfpark Campus	Pavia University Salinas Mississippi Gulfport	Tri-CNN	With 1% labeled training samples 92.66±0.24%. With 1% labeled training samples 96.68±2.11%. With 1% labeled training samples 92.96±1.39%	1. In the first experiment with 1% labeled samples for training, the proposed method outperformed six comparison methods (SVM, 2DCNN, 3DCNN, PMI-CNN, HybridSN, and MSCNN) in classification accuracy. 2. In the second experiment with varying percentages of labeled sample data (1%, 2%, 3%, 4%, and 5%) for training, all methods generally improved with an increase in training samples. The proposed method consistently demonstrated the best classification accuracy across all proportions of sample training on the three datasets.

Figure 6. Structure diagram of a deep belief network, where H refers to a hidden layer, and V to a visible layer.

Table 10. Information related to DBN literature.

Reference	Sensor	Dataset	Model	Overall Accuracy	Remark
(Arsa et al. 2016)	AVIRIS	Indian Pines	Based on DBN as a dimensionality reduction method	89.9097% ± 2.1820%	This study uses DBN as a dimensionality reduction method to process the dimensionality of hyperspectral images and compares the dimensionality reduction performance of this technique with that of PCA. The results show that DBN has higher classification accuracy and stability.
(Zhong et al. 2017)	AVIRIS ROSIS	Indian Pines Pavia University	A new diversified DBN model(DBN-S-S、D-DBN-P、D-DBN-PF)	91.07 ± 0.12% 91.55 ± 0.13% 91.03 ± 0.12% 92.83 ± 0.07% 92.05 ± 0.07% 93.11 ± 0.06%	Compared to SVM with the polynomial kernel (SVM-POLY), CNN, DBN, and Ginzburg–Landau (GL) methods, D-DBN-P and D-DBN-PF methods have higher classification accuracy on both IP and PU datasets than DBN, with D-DBN-PF achieving the best classification results on the PU dataset.
(Chen, Ma, and Ren 2020)	ROSIS Gaofen-5 (GF-5) satellite	University of Pavia Yellow River Delta coastal wetland	CGDBN	PRP DBN: 97.21% FR DBN: 97.31% PRP DBN-softmax: 97.80% FR DBN-softmax: 97.86% FR-DBN(Yellow River Delta coastal wetland): 98.63%	1. Using two datasets, the proposed method was validated against seven comparison methods on the PU dataset: 2D-CNN, DBN-softmax, DBN, DBN-CRF-S-S, JSSC-DBN, WI-DL, and recurrent-3D-CNN. Results reveal the proposed method’s superior classification performance, particularly when a softmax classifier is added for improved accuracy. 2. Tested on an actual scene of coastal wetlands in the Yellow River Delta, the proposed method outperforms NN, 2D-CNN, DBN, and DBN-softmax methods, achieving the highest classification accuracy.
(Chintada, Prakash Yalla, and Uriti 2021)	ROSIS	Pavia University	DBN-Logistic regression (DBN-LR)	95%	This study used the PU dataset for testing, and the experiment showed that the proposed method achieved a classification accuracy of 95%, which is a good result.
(Li et al. 2022)	AVIRIS AVIRIS Hyperion sensor	Indian Pines Salinas Botswana	MMDBN	80 and 100 samples/class for training: 77.35 ± 0.69% 78.25 ± 1.39%. 90.11 ± 0.52% 90.48 ± 0.38%. 96.35 ± 0.52% 97.35 ± 0.28%	The experimental results show that the overall accuracy of MMDBN on IP and SA datasets is superior to other deep learning models, but on the Botswana dataset, when the number of training samples per class is 80, the overall accuracy of the CNN model is the highest. However, the training time of MMDBN is significantly less than that of other deep learning models.

Figure 7. Structure diagram of the recurrent neural network, where X refers to the input, h(t) refers to a hidden state at time t, and L refers to the output. U, V, and W refer to the weights.

Table 11. Examples of RNN literature.

Reference	Sensor	Dataset	Model	Overall Accuracy	Remark
(Shi and Pun 2018)	ROSIS ROSIS AVIRIS	Pavia University Pavia Center Scene Salinas	MHRNNs	1% of the labeled samples 98.11% only 0.1% of the labeled samples 96.32% 1% of the labeled samples 98.20%	This experiment introduces three methods: LDA, RNNs, SAEs, 3D CNNs, SSFC, and MCNN. The proposed method is tested on three hyperspectral data sets. The experimental results show that the proposed method has the highest overall accuracy and Kappa coefficient. Moreover, the Pavia Center data set only uses 0.1% labeled samples, and the proposed method still has high classification accuracy, which further demonstrates the superiority of the method.
(Hang et al. 2019)	AVIRIS ROSIS	Indian Pines Pavia University	Cascaded RNN model (CasRNN、CasRNN-F、CasRNN-O and SSCasRNN)	73.49% 75.85% 74.60% 91.79% 78.03% 79.56% 80.86% 90.30%	1. The article introduces SVM, 1-D-CNN, 2-D-CNN, and initial RNN to validate the proposed method. SSCasRNN, a spectral space cascade model, achieves an overall classification index above 90%, surpassing all other models. 2. Tested on the PU dataset, experimental results indicate that CasRNN outperforms RNN, and both CasRNN-F and CasRNN-O outperform the CasRNN model. However, the overall classification performance of the SSCasRNN model remains the best.
(Zhou et al. 2021)	AVIRIS ROSIS AVIRIS	Indian Pines Pavia University Salinas	Multiscanning strategy-based RNN (Scheme-1: multiscanning-based model; Scheme-2: the framework with Bi-LSTM)	95.13% 95.38% 98.99% 99.18% 97.19% 97.92%	This study compared Spectral CNN, Deep-RNN, 3-D CNN, FCN, M3D-DCNN, and SSAN. Scheme 2 outperformed other models in overall accuracy, while SSAN had the highest average accuracy. However, SSAN contains more parameters and its efficiency is not ideal. The proposed method uses relatively fewer parameters, has lower computational efficiency, and correspondingly requires shorter training time.
(Xu et al. 2022)	AVIRIS ROSIS AVIRIS	Indian Pine University of Pavia Kennedy Space Center	GBiLSTM-MFCT	GBiLSTM: 70.43±0.81% 88.52±0.43% 88.95±0.66% MFCT: 94.28±0.60% 98.51±0.35% 99.34±0.60% GBiLSTM-MFCT: 96.46±0.52% 99.24±0.18% 99.49±0.25%	1. Compared to 3DCNN, M3DCNN, Spectral-spatial unified network (SSUN), SSRN, HybridSN, multiscale capsule network with octave convolution (MSOctCaps), A2S2K-ResNet, and SpectralFormer methods, the proposed method has the best overall classification performance. 2. Test the proposed methods on different proportions of training samples. We tested the IP and KSC datasets using training samples with proportions of 2%, 3%, 6%, 9%, and 12%, respectively. Training samples with proportions of 0.5%, 1%, 2%, 3%, and 4% were tested on the PU dataset. The experimental results show that the proposed method can achieve higher classification accuracy than the method used in all proportions of training samples.
(Ablin and Prabin 2023)	UAV-carrying sensor ROSIS two pushbroom cameras from Specim Ltd	UAV-HSI-Crop Hyperspectral dataset Pavia University HSI dataset	Light CERG Network	Percentage of training samples is 20% nearly 94% Percentage of training samples is 20% nearly 92% Percentage of training samples is 20% nearly 93%	1. Using three datasets with training sample proportions of 5%, 10%, 15%, and 20%, the experiment reveals that the proposed method consistently achieves higher overall accuracy than all comparison methods (ITCIMC, HFAA, CFW-HSIC, and HSFSC) across these datasets. Notably, the overall accuracy of the proposed method remains above 90% even with a 20% training sample proportion. 2. Furthermore, the proposed methods exhibit the greatest fluctuations in the range of training sample ratios of 8 and 12.

Figure 8. Structure diagram of GAN.

Table 12. Examples of GAN literature.

Reference	Sensor	Dataset	Model	Overall Accuracy	Remark
(Gao et al. 2019)	AVIRIS ROSIS AVIRIS	Indian Pines Pavia University Salinas	MDGANs	95.73% 94.67% 96.19%	Against KNN, NN, SVM, and CNN methods, MDGANs demonstrate higher overall classification accuracy on both IP and SA datasets. While on the PU dataset, the proposed method shows a slight 0.12% dip compared to CNN, its overall performance retains an advantage.
(Zhao et al. 2019)	ROSIS	Pavia University	CCGAN	92.48%	This experiment introduces 1D-CNN, 2D-CNN, 3D-CNN, and GAN for comparative testing. The experiment shows that the overall classification accuracy of the proposed method is higher than these traditional classification methods.
(Liang et al. 2021)	AVIRIS ROSIS AVIRIS	Indian Pines Pavia University Kennedy Space Center	SSAT-GAN	Using 500 unlabeled samples and 500 labeled samples: 96.60±1.63% 98.09±0.42% 97.72±1.11%	1. The proposed method outperforms EPF-SVM, SSRN, 3D-Conv-Capsule, HSI-BERT, 3D-GAN, GAN-CRF, AD-GAN, Spatial-Attention-GAN, and Spatial-Spectral-Attention-GAN, achieving the highest classification accuracy across all three datasets. 2. The proposed method’s training times are 1151.59 s, 1095.48 s, and 990.91 s on three datasets, respectively. However, it exhibits a relatively longer training time compared to other methods.
(He et al. 2022)	ITRES CASI-1500 AVIRIS the airborne HYSPEX hyperspectral camera	Houston 2013 Indian Pines 2010 Xuzhou 2014	HyperViTGAN	89.04% 89.50% 93.46% Ten samples were randomly selected from each class to form the training set 72.15% 81.56% 93.46%	1. In Experiment 1, the proposed method, tested against KNN, SAE, CNN, SSRN, RNN, ViT, SpectralFormer, External Classifier-GAN, and ViTGAN, demonstrates the superior overall classification performance with randomly selected 10 samples from each class. 2. In Experiment 2, the proposed method maintains strong classification performance across three datasets compared to other methods. However, it exhibits a relatively longer running time, measured at 8457.88s 40,59168s, and 4840.37s on the respective datasets.
(Hao, Xia, and Ye 2023)	AVIRIS ROSIS	Indian Pines Pavia University	TRUG	97.85% 99.83%	Compared with SVM, 3D CNN, TransGAN, HybridSN, Deep Pyramidal Residual Networks (DPRN), Transformer, and ViT methods, the proposed method exhibits the highest overall classification accuracy on the IP and PU datasets.

Figure 9. Structure diagram of transformer.

Table 13. Examples of transformer literature.

Reference	Sensor	Dataset	Model	Overall Accuracy	Remark
(Hong et al. 2021)	AVIRIS ROSIS ITRES CASI-1500	Indian Pines Pavia University Houston2013	SpectralFormer	pixel-wise: 78.55% 87.94% 86.14% patch-wise: 81.76% 91.07% 88.01%	The SpectralFormer model, introduced in this study, exhibits versatility by accommodating both pixel-wise and patch-wise inputs. Two experiments were conducted at these levels, comparing against traditional classifiers (KNN, RF, SVM), classical backbone networks (1-D CNN, 2-D CNN, RNN, mini-batchGCN), and ViT. Results reveal that the proposed method achieves the highest overall classification performance, with patch-wise accuracy generally surpassing pixel-wise accuracy by 2%-5%.
(Ayas and Tunc-Gormus 2022)	AVIRIS ROSIS	Indian Pines Pavia University	A novel spectral-swin transformer (SpectralSWIN)	93.74% 88.72%	This experiment evaluates machine learning models (KNN, RF, SVM), deep learning models (1-D CNN, 2-D CNN, RNN, miniGCN), and transformer-based models (ViT, SpectralFormer), comparing them with the proposed SpectralSWIN model on IP and PU hyperspectral datasets. Results demonstrate the superior classification performance of the proposed model, except the SpectralFormer model, which achieves a 2.35% higher overall accuracy on the PU dataset.
(Wang et al. 2022)	AVIRIS. ROSIS-03. HySpex SWIR-384 and HySpex VNIR-1600 imaging spectrometers. an 8 mm focal length head-wall nano-Hyperspec imaging sensor.	Indian Pines Pavia University Xuzhou dataset WHU-Hi-LongKou	An attention-based adaptive spectral–spatial kernel combined with an improved ViT network architecture.	98.81±0.32% 99.76±0.06% 99.80±0.02% 99.89±0.03%	Comparing the proposed method with the classical method RBF-SVM and deep learning-based methods (CNN, HybridSN, pyramidal-ResNet(PyResNet), SSRN, spectral–spatial feature tokenization transformer (SSFTT), and A2S2K-ResNet), the results show that the proposed method has the highest overall classification accuracy on the four hyperspectral data sets used. For example, in the IP dataset, the overall accuracy of the proposed method is 98.81 ± 0.32%, while the best classification result among other methods is 98.51 ± 0.26%.
(Zhang et al. 2023)	AVIRIS ROSIS onboard sensors in downtown Houston, Texas, USA	Indian Pines Pavia University Data Fusion Contest 2018 (DFC 2018)	TransHSI	87.75±0.35% 89.03±1.72% 86.36±0.65%	This experiment used the IP dataset with low resolution and small sample size, as well as the PU and DFC 2018 datasets with high resolution and large sample size. The TransHSI method was tested with SVM, RF, 2D CNN, 3D CNN, HybridSN, SSRN, InternImage, ViT, Next-ViT, SSFTT, and spectral-spatial transformation network (SSTN) methods on these datasets. The experiment showed that on the three datasets, TransHSI achieved the best overall accuracy.
(Yao et al. 2023)	ITRES CASI-1500 imaging sensor. a simulated EnMAP HS image and SAR data that comes from Sentinel-1 payload. MDAS	Houston2013 HS-LiDAR Dataset. Berlin HS-SAR Dataset Augsburg HS-SAR-LiDAR Dataset	ExViT	90.62% 71.44% 97.17%	1. This study compared various models, including Stack-nearest neighbors, Stack-RF, CoSpace, LeMA, Co-CNN, CCR-Net, Co-ViT, Co-SF, and DHViT. The proposed model outperformed all on the Houston2013 dataset. 2. Despite the complexity of the Berlin dataset, resulting in lower general accuracy, the proposed method still demonstrated the best performance. 3. In testing on the Augsburg dataset, the proposed model showcased the highest classification performance.

Li, S., W. Song, L. Fang, Y. Chen, P. Ghamisi, and J. Atli Benediktsson. 2019. “Deep Learning for Hyperspectral Image Classification: An Overview.” IEEE Transactions on Geoscience & Remote Sensing 57 (9): 6690–6709.

 Web of Science ®Google Scholar

Moharram, M. A., and D. Meena Sundaram. 2023. “Dimensionality Reduction Strategies for Land Use Land Cover Classification Based on Airborne Hyperspectral Imagery: A Survey.” Environmental Science and Pollution Research 30 (3): 5580–5602.

 PubMed Web of Science ®Google Scholar

Gu, Y., J. Chanussot, X. Jia, and J. Atli Benediktsson. 2017. “Multiple Kernel Learning for Hyperspectral Image Classification: A Review.” IEEE Transactions on Geoscience & Remote Sensing 55 (11): 6547–6565.

 Web of Science ®Google Scholar

Bera, S., V. K. Shrivastava, and S. Chandra Satapathy. 2022. “Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review.” Cmes-Computer Modeling in Engineering & Sciences 133 (2).

 Web of Science ®Google Scholar

Wang, X., J. Liu, W. Chi, W. Wang, and Y. Ni. 2023. “Advances in Hyperspectral Image Classification Methods with Small Samples: A Review.” Remote Sensing 15 (15): 3795.

 Web of Science ®Google Scholar

Sellami, A., A. B. Abbes, V. Barra, and I. R. Farah. 2020. “Fused 3-D Spectral-Spatial Deep Neural Networks and Spectral Clustering for Hyperspectral Image Classification.” Pattern recognition letters 138: 594–600. https://doi.org/10.1016/j.patrec.2020.08.020.

 Web of Science ®Google Scholar

Wang, X., K. Tan, P. Du, B. Han, and J. Ding. 2023. “A Capsule-Vectored Neural Network for Hyperspectral Image Classification.” Knowledge-Based Systems 268: 110482.

 Web of Science ®Google Scholar

Wan, S., S. Pan, S. Zhong, J. Yang, J. Yang, Y. Zhan, and C. Gong. 2022. “Multi-Level Graph Learning Network for Hyperspectral Image Classification.” Pattern recognition 129: 108705. https://doi.org/10.1016/j.patcog.2022.108705.

 Web of Science ®Google Scholar

Wang, Z., J. Li, and T. Zhang. 2021. “Discriminative Graph Convolution Networks for Hyperspectral Image Classification.” Displays 70: 102114. https://doi.org/10.1016/j.displa.2021.102114.

 Web of Science ®Google Scholar

Du, P., K. Tan, and X. Xing. 2012. “A Novel Binary Tree Support Vector Machine for Hyperspectral Remote Sensing Image Classification.” Optics Communications 285 (13): 3054–3060. https://doi.org/10.1016/j.optcom.2012.02.092.

 Web of Science ®Google Scholar

Senthil Kumar, A., V. Keerthi, A. S. Manjunath, H. van der Werff, and F. van der Meer. 2010. “Hyperspectral Image Classification by a Variable Interval Spectral Average and Spectral Curve Matching Combined Algorithm.” International Journal of Applied Earth Observation and Geoinformation 12 (4): 261–269. https://doi.org/10.1016/j.jag.2010.03.004.

 Web of Science ®Google Scholar

Carranza-García, M., J. García-Gutiérrez, and J. C. Riquelme. 2019. “A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks.” Remote Sensing 11 (3): 274. https://doi.org/10.3390/rs11030274.

 Web of Science ®Google Scholar

Sun, H., X. Zheng, and X. Lu. 2021. “A Supervised Segmentation Network for Hyperspectral Image Classification.” IEEE Transactions on Image Processing 30: 2810–2825. https://doi.org/10.1109/TIP.2021.3055613.

 PubMed Web of Science ®Google Scholar

Christovam, L. E., G. G. Pessoa, M. H. Shimabukuro, and M. L. B. T. Galo. 2019. “Land Use and Land Cover Classification Using Hyperspectral Imagery: Evaluating the Performance of Spectral Angle Mapper, Support Vector Machine and Random Forest.” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42: 1841–1847.

 Google Scholar

Akbari, D., S. Homayouni, A. Safari, and N. Mehrshad. 2016. “Mapping Urban Land Cover Based on Spatial-Spectral Classification of Hyperspectral Remote-Sensing Data.” International Journal of Remote Sensing 37 (2): 440–454.

 Web of Science ®Google Scholar

Wang, H., W. Li, W. Huang, J. Niu, and K. Nie. 2020. “Research on Land Use Classification of Hyperspectral Images Based on Multiscale Superpixels.” Mathematical Biosciences and Engineering: MBE 17 (5): 5099–5119.

 PubMed Web of Science ®Google Scholar

Jun, G., and J. Ghosh. 2011. “Spatially Adaptive Classification of Land Cover with Remote Sensing Data.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 49 (7): 2662–2673. https://doi.org/10.1109/TGRS.2011.2105490.

 Web of Science ®Google Scholar

Zhong, Y., X. Hu, C. Luo, X. Wang, J. Zhao, and L. Zhang. 2020. “WHU-Hi: UAV-Borne Hyperspectral with High Spatial Resolution (H2) Benchmark Datasets and Classifier for Precise Crop Identification Based on Deep Convolutional Neural Network with CRF.” Remote Sensing of Environment 250: 112012.

 Web of Science ®Google Scholar

Zhang, C., Z. Zhang, D. Yu, Q. Cheng, S. Shan, M. Li, L. Mou, X. Yang, and X. Ma. 2023. “Unsupervised Band Selection of Medical Hyperspectral Images Guided by Data Gravitation and Weak Correlation.” Computer Methods and Programs in Biomedicine 240: 107721.

 PubMed Web of Science ®Google Scholar

Yuan, D., J. Jiang, Z. Gong, C. Nie, and Y. Sun. 2022. “Moldy Peanuts Identification Based on Hyperspectral Images and Point-Centered Convolutional Neural Network Combined with Embedded Feature Selection.” Computers and Electronics in Agriculture 197: 106963.

 Web of Science ®Google Scholar

Elmaizi, A., H. Nhaila, E. Sarhrouni, A. Hammouch, and C. Nacir. 2019. “A Novel Information Gain Based Approach for Classification and Dimensionality Reduction of Hyperspectral Images.” Procedia Computer Science 148: 126–134. https://doi.org/10.1016/j.procs.2019.01.016.

 Google Scholar

Dalal, A.-A., M. A. Al-qaness, Z. Cai, and E. Ahmed Alawamy. 2023. “IDA: Improving Distribution Analysis for Reducing Data Complexity and Dimensionality in Hyperspectral Images.” Pattern Recognition 134: 109096.

 Web of Science ®Google Scholar

Jin, S., W. Zhang, P. Yang, Y. Zheng, J. An, Z. Zhang, P. Qu, and X. Pan. 2022. “Spatial-Spectral Feature Extraction of Hyperspectral Images for Wheat Seed Identification.” Computers & Electrical Engineering 101: 108077.

 Web of Science ®Google Scholar

Zabalza, J., J. Ren, J. Zheng, H. Zhao, C. Qing, Z. Yang, P. Du, and S. Marshall. 2016. “Novel Segmented Stacked Autoencoder for Effective Dimensionality Reduction and Feature Extraction in Hyperspectral Imaging.” Neurocomputing 185: 1–10. https://doi.org/10.1016/j.neucom.2015.11.044.

 Web of Science ®Google Scholar

Petropoulos, G. P., K. Arvanitis, and N. Sigrimis. 2012. “Hyperion Hyperspectral Imagery Analysis Combined with Machine Learning Classifiers for Land Use/Cover Mapping.” Expert Systems with Applications 39 (3): 3800–3809.

 Web of Science ®Google Scholar

Shah, M., Y. Liu, and H. Hayat. 2016. “Rotation-Based Object-Oriented Ensemble in Land Use Land Cover Classification of Hyperspectral Data.” In 2016 Sixth International Conference on Innovative Computing Technology (INTECH), Dublin, Ireland, 24-26 August 2016.

 Google Scholar

Dahiya, N., S. Gupta, and S. Singh. 2021. “Quantitative Analysis of Different Land-Use and Land-Cover Classifiers Using Hyperspectral Dataset.” In 2021 Sixth International Conference on Image Information Processing (ICIIP).

 Google Scholar

Tong, F., and Y. Zhang. 2022. “Spectral–Spatial and Cascaded Multilayer Random Forests for Tree Species Classification in Airborne Hyperspectral Images.” IEEE Transactions on Geoscience & Remote Sensing 60: 1–11.

 Google Scholar

Li, Z., K. Bi, Y. Wang, Z. Fang, and J. Zhang. 2023. “Supervised Contrastive Learning for Open-Set Hyperspectral Image Classification.” In IEEE Geoscience and Remote Sensing Letters.

 Google Scholar

Kang, X., X. Xiang, S. Li, and J. Atli Benediktsson. 2017. “PCA-Based Edge-Preserving Features for Hyperspectral Image Classification.” IEEE Transactions on Geoscience & Remote Sensing 55 (12): 7140–7151.

 Web of Science ®Google Scholar

Xie, M., Z. Ji, G. Zhang, T. Wang, and Q. Sun. 2018. “Mutually Exclusive-KSVD: Learning a Discriminative Dictionary for Hyperspectral Image Classification.” Neurocomputing 315: 177–189.

 Web of Science ®Google Scholar

Chen, H., F. Miao, Y. Chen, Y. Xiong, and T. Chen. 2021. “A Hyperspectral Image Classification Method Using Multifeature Vectors and Optimized KELM.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 14: 2781–2795.

 Google Scholar

Zhao, L., W. Luo, Q. Liao, S. Chen, and J. Wu. 2022. “Hyperspectral Image Classification with Contrastive Self-Supervised Learning Under Limited Labeled Samples.” IEEE Geoscience & Remote Sensing Letters 19: 1–5.

 Google Scholar

Liu, Q., D. Xue, Y. Tang, Y. Zhao, J. Ren, and H. Sun. 2023. “PSSA: PCA-Domain Superpixelwise Singular Spectral Analysis for Unsupervised Hyperspectral Image Classification.” Remote Sensing 15 (4): 890.

 Web of Science ®Google Scholar

Yi, K., X. Wang, Y. Cheng, and C. L. Philip Chen. 2018. “Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System.” Remote Sensing 10 (5): 685.

 Web of Science ®Google Scholar

Hong, D., N. Yokoya, N. Ge, J. Chanussot, and X. Xiang Zhu. 2019. “Learnable Manifold Alignment (LeMA): A Semi-Supervised Cross-Modality Learning Framework for Land Cover and Land Use Classification.” Isprs Journal of Photogrammetry & Remote Sensing 147: 193–205.

 PubMed Web of Science ®Google Scholar

Xi, B., J. Li, Y. Li, and Q. Du. 2021. “Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification.” In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

 Google Scholar

Song, L., Z. Feng, S. Yang, X. Zhang, and L. Jiao. 2022. “Self-Supervised Assisted Semi-Supervised Residual Network for Hyperspectral Image Classification.” Remote Sensing 14 (13): 2997.

 Web of Science ®Google Scholar

Sellami, A., M. Farah, and M. D. Mura. 2023. “SHCNet: A Semi-Supervised Hypergraph Convolutional Networks Based on Relevant Feature Selection for Hyperspectral Image Classification.” Pattern Recognition Letters 165: 98–106.

 Web of Science ®Google Scholar

Xie, F., Q. Gao, C. Jin, and F. Zhao. 2021. “Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning.” Remote Sensing 13 (5): 930.

 Web of Science ®Google Scholar

Zhang, Y., W. Li, M. Zhang, Y. Qu, R. Tao, and H. Qi. 2021. “Topological Structure and Semantic Information Transfer Network for Cross-Scene Hyperspectral Image Classification.” IEEE Transactions on Neural Networks and Learning Systems.

 Google Scholar

Hamza, M. A., F. Alrowais, J. S. Alzahrani, H. Mahgoub, N. M. Salem, and R. Marzouk. 2022. “Squirrel Search Optimization with Deep Transfer Learning-Enabled Crop Classification Model on Hyperspectral Remote Sensing Imagery.” Applied Sciences 12 (11): 5650.

 Google Scholar

Sarker, Y., S. R. Fahim, M. S. Hosen, S. K. Sarker, M. N. Islam Mondal, and S. K. Das. 2020. “Regularized Singular Value Decomposition Based Multidimensional Convolutional Neural Network for Hyperspectral Image Classification.” In 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh, 05-07 June 2020.

 Google Scholar

Yu, C., R. Han, M. Song, C. Liu, and C.-I. Chang. 2021. “Feedback Attention-Based Dense CNN for Hyperspectral Image Classification.” IEEE Transactions on Geoscience & Remote Sensing 60: 1–16.

 Google Scholar

Ding, Y., Z. Zhang, X. Zhao, D. Hong, W. Cai, C. Yu, N. Yang, and W. Cai. 2022. “Multi-Feature Fusion: Graph Neural Network and CNN Combining for Hyperspectral Image Classification.” Neurocomputing 501: 246–257.

 Web of Science ®Google Scholar

Arun Solomon, A., and S. Akila Agnes. 2023. “Land-Cover Classification with Hyperspectral Remote Sensing Image Using CNN and Spectral Band Selection.” Remote Sensing Applications: Society & Environment 31: 100986. https://doi.org/10.1016/j.rsase.2023.100986.

 Google Scholar

Alkhatib, M. Q., M. Al-Saad, N. Aburaed, S. Almansoori, J. Zabalza, S. Marshall, and H. Al-Ahmad. 2023. “Tri-CNN: A Three Branch Model for Hyperspectral Image Classification.” Remote Sensing 15 (2): 316.

 Web of Science ®Google Scholar

Arsa, D. M. S., G. Jati, A. J. Mantau, and I. Wasito. 2016. “Dimensionality Reduction Using Deep Belief Network in Big Data Case Study: Hyperspectral Image Classification.” In 2016 International Workshop on Big Data and Information Security (IWBIS), Jakarta, Indonesia, 18-19 October 2016.

 Google Scholar

Zhong, P., Z. Gong, S. Li, and C.-B. Schönlieb. 2017. “Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience & Remote Sensing 55 (6): 3516–3530.

 Web of Science ®Google Scholar

Chen, C., Y. Ma, and G. Ren. 2020. “Hyperspectral Classification Using Deep Belief Networks Based on Conjugate Gradient Update and Pixel-Centric Spectral Block Features.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 13: 4060–4069.

 Google Scholar

Chintada, K. R., S. Prakash Yalla, and A. Uriti. 2021. “A Deep Belief Network Based Land Cover Classification.” In 2021 Innovations in Power and Advanced Computing Technologies (i-PACT).

 Google Scholar

Li, Z., H. Huang, Z. Zhang, and G. Shi. 2022. “Manifold-Based Multi-Deep Belief Network for Feature Extraction of Hyperspectral Image.” Remote Sensing 14 (6): 1484.

 Web of Science ®Google Scholar

Shi, C., and C.-M. Pun. 2018. “Multi-Scale Hierarchical Recurrent Neural Networks for Hyperspectral Image Classification.” Neurocomputing 294: 82–93.

 Web of Science ®Google Scholar

Hang, R., Q. Liu, D. Hong, and P. Ghamisi. 2019. “Cascaded Recurrent Neural Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience & Remote Sensing 57 (8): 5384–5394.

 Web of Science ®Google Scholar

Zhou, W., S.-I. Kamata, Z. Luo, and H. Wang. 2021. “Multiscanning Strategy-Based Recurrent Neural Network for Hyperspectral Image Classification.” IEEE Transactions on Geoscience & Remote Sensing 60: 1–18.

 Web of Science ®Google Scholar

Xu, Q., C. Yang, J. Tang, and B. Luo. 2022. “Grouped Bidirectional LSTM Network and Multistage Fusion Convolutional Transformer for Hyperspectral Image Classification.” IEEE Transactions on Geoscience & Remote Sensing 60: 1–14.

 Web of Science ®Google Scholar

Ablin, R., and G. Prabin. 2023. “An Optimal Model Using Hybrid LCNN-GRU for Efficient Hyperspectral Image Classification.” International Journal of Information Technology 15 (6): 3335–3342.

 Google Scholar

Gao, H., D. Yao, M. Wang, C. Li, H. Liu, Z. Hua, and J. Wang. 2019. “A Hyperspectral Image Classification Method Based on Multi-Discriminator Generative Adversarial Networks.” Sensors 19 (15): 3269.

 PubMed Web of Science ®Google Scholar

Zhao, W., X. Chen, Y. Bo, and J. Chen. 2019. “Semisupervised Hyperspectral Image Classification with Cluster-Based Conditional Generative Adversarial Net.” IEEE Geoscience & Remote Sensing Letters 17 (3): 539–543.

 Web of Science ®Google Scholar

Liang, H., W. Bao, X. Shen, and X. Zhang. 2021. “Spectral–Spatial Attention Feature Extraction for Hyperspectral Image Classification Based on Generative Adversarial Network.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 14: 10017–10032.

 Google Scholar

He, Z., K. Xia, P. Ghamisi, Y. Hu, S. Fan, and B. Zu. 2022. “Hypervitgan: Semisupervised Generative Adversarial Network with Transformer for Hyperspectral Image Classification.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 15: 6053–6068.

 Google Scholar

Hao, S., Y. Xia, and Y. Ye. 2023. “Generative Adversarial Network with Transformer for Hyperspectral Image Classification.” In IEEE Geoscience and Remote Sensing Letters.

 Google Scholar

Hong, D., Z. Han, J. Yao, L. Gao, B. Zhang, A. Plaza, and J. Chanussot. 2021. “SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers.” IEEE Transactions on Geoscience & Remote Sensing 60: 1–15.

 Google Scholar

Ayas, S., and E. Tunc-Gormus. 2022. “SpectralSWIN: A Spectral-Swin Transformer Network for Hyperspectral Image Classification.” International Journal of Remote Sensing 43 (11): 4025–4044.

 Web of Science ®Google Scholar

Wang, A., S. Xing, Y. Zhao, H. Wu, and Y. Iwahori. 2022. “A Hyperspectral Image Classification Method Based on Adaptive Spectral Spatial Kernel Combined with Improved Vision Transformer.” Remote Sensing 14 (15): 3705.

 Web of Science ®Google Scholar

Zhang, P., H. Yu, P. Li, and R. Wang. 2023. “TransHSI: A Hybrid CNN-Transformer Method for Disjoint Sample-Based Hyperspectral Image Classification.” Remote Sensing 15 (22): 5331.

 Web of Science ®Google Scholar

Yao, J., B. Zhang, C. Li, D. Hong, and J. Chanussot. 2023. “Extended Vision Transformer (ExViT) for Land Use and Land Cover Classification: A Multimodal Deep Learning Framework.” In IEEE Transactions on Geoscience and Remote Sensing.

 Google Scholar

Data availability statement

This is a review paper. No data was used in this study.