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Research Article

Attention-based multiscale deep learning with unsampled pixel utilization for hyperspectral image classification

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Article: 2231428 | Received 03 Apr 2023, Accepted 26 Jun 2023, Published online: 25 Jul 2023

References

  • Abdulsamad T, Chen F, Xue Y, Wang Y, Yang L, Zeng D. 2021. Hyperspectral image classification based on spectral and spatial information using ResNet with channel attention. Opt Quant Electron. 53(3):1–20. doi:10.1007/s11082-020-02671-4.
  • Ahmad M, Shabbir S, Roy SK, Hong D, Wu X, Yao J, Khan AM, Mazzara M, Distefano S, Chanussot J. 2022. Hyperspectral image classification—traditional to deep models: a survey for future prospects. IEEE J Sel Top Appl Earth Observ Remote Sens. 15:968–999. doi:10.1109/JSTARS.2021.3133021.
  • Alsharrah SA, Bouabid R, Bruce DA, Somenahalli S, Corcoran PA. 2016. Use of shadow for enhancing mapping of perennial desert plants from high-spatial resolution multispectral and panchromatic satellite imagery. J Appl Remote Sens. 10(3):036008–036008. doi:10.1117/1.JRS.10.036008.
  • Borzov SM, Potaturkin OI. 2018. Spectral-spatial methods for hyperspectral image classification. review. OptoelectronInstrumentProc. 54(6):582–599. doi:10.3103/S8756699018060079.
  • Caballero D, Calvini R, Amigo JM. 2019. Hyperspectral imaging in crop fields: precision agriculture. In Data handling in science and technology.Vol. 32. Amsterdam, The Netherlands: Elsevier. p. 453–473.
  • Cai W, Liu B, Wei Z, Li M, Kan J. 2021. TARDB-Net: triple-attention guided residual dense and BiLSTM networks for hyperspectral image classification. Multimed Tools Appl. 80(7):11291–11312. doi:10.1007/s11042-020-10188-x.
  • Dang L, Weng L, Hou Y, Zuo X, Liu Y. 2023a. Double-branch feature fusion transformer for hyperspectral image classification. Sci Rep. 13(1):272. doi:10.1038/s41598-023-27472-z.
  • Dang W, Liao S, Yang B, Yin Z, Liu M, Yin L, Zheng W. 2023b. An encoder-decoder fusion battery life prediction method based on Gaussian process regression and improvement. J Energy Storage. 59:106469. doi:10.1016/j.est.2022.106469.
  • Ding Y, Guo Y, Chong Y, Pan S, Feng J. 2021. Global consistent graph convolutional network for hyperspectral image classification. IEEE Trans Instrum Meas. 70:1–16.‏ doi:10.1109/TIM.2021.3056750.
  • Dong Y, Liu Q, Du B, Zhang L. 2022. Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification. IEEE Trans Image Process. 31:1559–1572. doi:10.1109/TIP.2022.3144017.
  • Dong H, Zhang L, Zou B. 2019. Band attention convolutional networks for hyperspectral image classification. arXiv preprint arXiv:1906.04379
  • Duan P, Ghamisi P, Kang X, Rasti B, Li S, Gloaguen R. 2021. Fusion of dual spatial information for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 59(9):7726–7738. doi:10.1109/TGRS.2020.3031928.
  • Eskandari R, Mahdianpari M, Mohammadimanesh F, Salehi B, Brisco B, Homayouni S. 2020. Meta-analysis of unmanned aerial vehicle (UAV) imagery for agro-environmental monitoring using machine learning and statistical models. Remote Sens. 12(21):3511. doi:10.3390/rs12213511.
  • Fang B, Li Y, Zhang H, Chan JCW. 2019. Hyperspectral images classification based on dense convolutional networks with spectral-wise attention mechanism. Remote Sens. 11(2):159. doi:10.3390/rs11020159.
  • Feng J, Li D, Gu J, Cao X, Shang R, Zhang X, Jiao L. 2022. Deep reinforcement learning for semisupervised hyperspectral band selection. IEEE Trans Geosci Remote Sens. 60:1–19. doi:10.1109/TGRS.2021.3049372.
  • Gao H, Miao Y, Cao X, Li C. 2021. Densely connected multiscale attention network for hyperspectral image classification. IEEE J Sel Top Appl Earth Observ Remote Sens. 14:2563–2576. doi:10.1109/JSTARS.2021.3103176.
  • Gao H, Yang Y, Yao D, Li C. 2019. Hyperspectral image classification with pre-activation residual attention network. IEEE Access. 7:176587–176599. doi:10.1109/ACCESS.2019.2957163.
  • Gong Z, Zhong P, Yu Y, Hu W, Li S. 2019. A CNN with multiscale convolution and diversified metric for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 57(6):3599–3618. doi:10.1109/TGRS.2018.2886022.
  • Hamida AB, Benoit A, Lambert P, Amar CB. 2018. 3-D deep learning approach for remote sensing image classification. IEEE Trans Geosci Remote Sens. 56(8):4420–4434. doi:10.1109/TGRS.2018.2818945.
  • Hang R, Li Z, Ghamisi P, Hong D, Xia G, Liu Q. 2020. Classification of hyperspectral and LiDAR data using coupled CNNs. IEEE Trans Geosci Remote Sens. 58(7):4939–4950. doi:10.1109/TGRS.2020.2969024.
  • Hang R, Li Z, Liu Q, Ghamisi P, Bhattacharyya SS. 2021. Hyperspectral image classification with attention-aided CNNs. IEEE Trans Geosci Remote Sens. 59(3):2281–2293. doi:10.1109/TGRS.2020.3007921.
  • He L, Li J, Liu C, Li S. 2017. Recent advances on spectral–spatial hyperspectral image classification: an overview and new guidelines. IEEE Trans Geosci Remote Sens. 56(3):1579–1597. doi:10.1109/TGRS.2017.2765364.
  • Imani M, Ghassemian H. 2020. An overview on spectral and spatial information fusion for hyperspectral image classification: current trends and challenges. Inform Fusion. 59:59–83. doi:10.1016/j.inffus.2020.01.007.
  • Jijón-Palma ME, Kern J, Amisse C, Centeno JAS. 2021. Improving stacked-autoencoders with 1D convolutional-nets for hyperspectral image land-cover classification. J Appl Rem Sens. 15(02):026506. doi:10.1117/1.JRS.15.026506.
  • Khan MJ, Khan HS, Yousaf A, Khurshid K, Abbas A. 2018. Modern trends in hyperspectral image analysis: a review. IEEE Access. 6:14118–14129. doi:10.1109/ACCESS.2018.2812999.
  • Khan A, Vibhute AD, Mali S, Patil CH. 2022. A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications. Ecol Inf. 69:101678. doi:10.1016/j.ecoinf.2022.101678.
  • LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature. 521(7553):436–444. doi:10.1038/nature14539.
  • Li J, Cui R, Li B, Li Y, Mei S, Du Q. 2019. Dual 1D-2D spatial-spectral cnn for hyperspectral image super-resolution. In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium; IEEE. p. 3113–3116.
  • Liu R, Cai W, Li G, Ning X, Jiang Y. 2021. Hybrid dilated convolution guided feature filtering and enhancement strategy for hyperspectral image classification. IEEE Geosci Remote Sens Lett. 19:1–5. doi: 10.1109/LGRS.2021.3100407.
  • Liu H, Li W, Xia XG, Zhang M, Gao CZ, Tao R. 2022. Central attention network for hyperspectral imagery classification. IEEE Trans Neural Netw Learn Syst. doi:10.1109/TNNLS.2022.3155114.
  • Makantasis K, Karantzalos K, Doulamis A, Doulamis N. 2015, July. Deep supervised learning for hyperspectral data classification through convolutional neural networks. In 2015 IEEE international geoscience and remote sensing symposium (IGARSS); IEEE. p. 4959–4962. doi:10.1109/IGARSS.2015.7326945.
  • Mei X, Pan E, Ma Y, Dai X, Huang J, Fan F, Du Q, Zheng H, Ma J. 2019. Spectral-spatial attention networks for hyperspectral image classification. Remote Sens. 11(8):963. doi:10.3390/rs11080963.
  • Mohan A, Venkatesan M. 2020. HybridCNN based hyperspectral image classification using multiscale spatiospectral features. Infrared Phys Technol. 108:103326. doi:10.1016/j.infrared.2020.103326.
  • Pande S, Banerjee B. 2021. Adaptive hybrid attention network for hyperspectral image classification. Pattern Recog Lett. 144:6–12. doi:10.1016/j.patrec.2021.01.015.
  • Paoletti ME, Haut JM, Plaza J, Plaza A. 2019. Deep learning classifiers for hyperspectral imaging: a review. ISPRS J Photogramm Remote Sens. 158:279–317. doi:10.1016/j.isprsjprs.2019.09.006.
  • Paul A, Bhoumik S, Chaki N. 2021. SSNET: an improved deep hybrid network for hyperspectral image classification. Neural Comput Appl. 33(5):1575–1585. doi:10.1007/s00521-020-05069-1.
  • Petropoulos GP, Arvanitis K, Sigrimis N. 2012. Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping. Expert Syst Appl. 39(3):3800–3809. doi:10.1016/j.eswa.2011.09.083.
  • Peyghambari S, Zhang Y. 2021. Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review. J Appl Rem Sens. 15(03):031501–031501. doi:10.1117/1.JRS.15.031501.
  • Rao M, Tang L, Tang P, Zhang Z. 2019. ES-CNN: an end-to-end Siamese convolutional neural network for hyperspectral image classification. In: 2019 Joint Urban Remote Sensing Event (JURSE). IEEE. p. 1–4. doi:10.1109/JURSE.2019.8808991.
  • Roy SK, Krishna G, Dubey SR, Chaudhuri BB. 2020. HybridSN: exploring 3-D–2-D CNN feature hierarchy for hyperspectral image classification. IEEE Geosci Remote Sens Lett. 17(2):277–281. doi:10.1109/LGRS.2019.2918719.
  • Shao Y, Lan J, Liang Y, Hu J. 2021. Residual networks with multi-attention mechanism for hyperspectral image classification. Arab J Geosci. 14(4):1–19. doi:10.1007/s12517-021-06516-6.
  • Shi C, Liao D, Zhang T, Wang L. 2022. Hyperspectral image classification based on 3D coordination attention mechanism network. Remote Sens. 14(3):608. doi:10.3390/rs14030608.
  • Stuart MB, McGonigle AJ, Willmott JR. 2019. Hyperspectral imaging in environmental monitoring: a review of recent developments and technological advances in compact field deployable systems. Sensors. 19(14):3071. doi:10.3390/s19143071.
  • Sudharsan S, Hemalatha R, Radha S. 2019. A survey on hyperspectral imaging for mineral exploration using machine learning algorithms. In: 2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET). IEEE. p. 206–212. doi:10.1109/WiSPNET45539.2019.9032740.
  • Sun Y, Liu B, Yu X, Yu A, Gao K, Ding L. 2022. Spectral–spatial feature tokenization transformer for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 60:1–17. doi:10.1109/TGRS.2022.3221534.
  • Sun G, Zhang X, Jia X, Ren J, Zhang A, Yao Y, Zhao H. 2020. Deep fusion of localized spectral features and multi-scale spatial features for effective classification of hyperspectral images. Int J Appl Earth Obs Geoinf. 91:102157. ‏ doi:10.1016/j.jag.2020.102157.
  • Sun H, Zheng X, Lu X, Wu S. 2019. Spectral–spatial attention network for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 58(5):3232–3245. doi: 10.1109/TGRS.2019.2951160.
  • Tulapurkar H, Banerjee B, Mohan BK. 2020. Effective and efficient dimensionality reduction of hyperspectral image using CNN and LSTM network. In: 2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS). IEEE.‏ p. 213–216. doi:10.1109/InGARSS48198.2020.9358957.
  • Wang Z, Du B, Shi Q, Tu W. 2019. Domain adaptation with discriminative distribution and manifold embedding for hyperspectral image classification. IEEE Geosci Remote Sens Lett. 16(7):1155–1159. doi:10.1109/LGRS.2018.2889967.
  • Wang Y, Li K, Xu L, Wei Q, Wang F, Chen Y. 2021. A depthwise separable fully convolutional ResNet with region growing for semi-supervised hyperspectral image classification. IEEE J Sel Top Appl Earth Observ Remote Sens. 14:4621–4632. doi:10.1109/JSTARS.2021.3073661.
  • Wang Q, Shen F, Cheng L, Jiang J, He G, Sheng W, Jing N, Mao Z. 2021. Ship detection based on fused features and rebuilt YOLOv3 networks in optical remote-sensing images. Int J Remote Sens. 42(2):520–536. doi:10.1080/01431161.2020.1811422.
  • Wang X, Tan K, Du P, Pan C, Ding J. 2022. A unified multiscale learning framework for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 60:1–19. doi:10.1109/TGRS.2022.3147198.
  • Wang J, Zhou J, Huang W. 2019. Attend in bands: hyperspectral band weighting and selection for image classification. IEEE J Sel Top Appl Earth Observ Remote Sens. 12(12):4712–4727. doi:10.1109/JSTARS.2019.2955097.
  • Wang J, Zhou J, Huang W, Chen JF. 2019. Attention networks for band weighting and selection in hyperspectral remote sensing image classification. In: IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE. p. 3820–3823.
  • Woo S, Park J, Lee JY, Kweon IS. 2018. Cbam: convolutional block attention module. In Proceedings of the European Conference on Computer Vision (ECCV). p. 3–19.
  • Wu Z, Zhu W, Chanussot J, Xu Y, Osher S. 2019. Hyperspectral anomaly detection via global and local joint modeling of background. IEEE Trans Signal Process. 67(14):3858–3869. doi: 10.1109/TSP.2019.2922157.
  • Xue Z, Yu X, Liu B, Tan X, Wei X. 2021. HResNetAM: hierarchical residual network with attention mechanism for hyperspectral image classification. IEEE J Sel Top Appl Earth Observ Remote Sens. 14:3566–3580. doi:10.1109/JSTARS.2021.3065987.
  • Yang L, Yang Y, Yang J, Zhao N, Wu L, Wang L, Wang T. 2022. FusionNet: a convolution–transformer fusion network for hyperspectral image classification. Remote Sens. 14(16):4066. doi:10.3390/rs14164066.
  • Yu H, Xu Z, Zheng K, Hong D, Yang H, Song M. 2022. MSTNet: a multilevel spectral–spatial transformer network for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 60:1–13. doi:10.1109/TGRS.2022.3186400.
  • Zhang P, Bai Y, Wang D, Bai B, Li Y. 2020. Few-shot classification of aerial scene images via meta-learning. Remote Sens. 13(1):108. doi:10.3390/rs13010108.
  • Zhang Y, Li W, Sun W, Tao R, Du Q. 2023. Single-source domain expansion network for cross-scene hyperspectral image classification. IEEE Trans Image Process. 32:1498–1512. doi:10.1109/TIP.2023.3243853.
  • Zhang Y, Li W, Tao R, Peng J, Du Q, Cai Z. 2021. Cross-scene hyperspectral image classification with discriminative cooperative alignment. IEEE Trans Geosci Remote Sens. 59(11):9646–9660. doi:10.1109/TGRS.2020.3046756.
  • Zhang Y, Li W, Zhang M, Qu Y, Tao R, Qi H. 2021. Topological structure and semantic information transfer network for cross-scene hyperspectral image classification. IEEE Trans Neural Netw Learn Syst.
  • Zhang Y, Li W, Zhang M, Wang S, Tao R, Du Q. 2022. Graph information aggregation cross-domain few-shot learning for hyperspectral image classification. IEEE Trans Neural Netw Learn Syst. doi:10.1109/TNNLS.2022.3185795.
  • Zhang H, Meng L, Wei X, Tang X, Tang X, Wang X, … Yao W. 2019. 1D-convolutional capsule network for hyperspectral image classification. arXiv preprint arXiv:1903.09834.‏
  • Zhang J, Wei F, Feng F, Wang C. 2020. Spatial–spectral feature refinement for hyperspectral image classification based on attention-dense 3D-2D-CNN. Sensors. 20(18):5191. doi:10.3390/s20185191.
  • Zhang Y, Wu L, Ren H, Liu Y, Zheng Y, Liu Y, Dong J. 2020. Mapping water quality parameters in urban rivers from hyperspectral images using a new self-adapting selection of multiple artificial neural networks. Remote Sens. 12(2):336. doi:10.3390/rs12020336.
  • Zhang J, Zhang X, Deng W, Guo L, Yang Q. 2021. A dense spatial–spectral attention network for hyperspectral image band selection. Remote Sens Lett. 12(10):1025–37. doi:10.1080/2150704X.2021.1875143.
  • Zhang Y, Zhang M, Li W, Wang S, Tao R. 2023. Language-aware domain generalization network for cross-scene hyperspectral image classification. IEEE Trans Geosci Remote Sens. 61:1–12. doi:10.1109/TGRS.2022.3233885.
  • Zhao L, Zeng Y, Liu P, He G. 2020. Band selection via explanations from convolutional neural networks. IEEE Access. 8:56000–56014. doi:10.1109/ACCESS.2020.2981475.
  • Zhao Y, Zhai D, Jiang J, Liu X. 2020. Adrn: attention-based deep residual network for hyperspectral image denoising. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. p. 2668–2672.
  • Zheng X, Sun H, Lu X, Xie W. 2022. Rotation-invariant attention network for hyperspectral image classification. IEEE Trans Image Process. 31:4251–4265. doi:10.1109/TIP.2022.3177322.