Hyperspectral image classification Based on weakened Laplacian pyramid and guided filtering

References

• Aghaei, N., G. Akbarizadeh, and A. Kosarian. 2022. “GreyWolflsm: An Accurate Oil Spill Detection Method Based on Level Set Method from Synthetic Aperture Radar Imagery.” European Journal of Remote Sensing 55 (1): 181–198. https://doi.org/10.1080/22797254.2022.2037468.
   Web of Science ®Google Scholar
• Akbarizadeh, G. 2012. “A New Statistical-Based Kurtosis Wavelet Energy Feature for Texture Recognition of SAR Images.” IEEE Transactions on Geoscience and Remote Sensing 50 (11): 4358–4368. https://doi.org/10.1109/TGRS.2012.2194787.
   Web of Science ®Google Scholar
• Akbarizadeh, G., G. A. Rezai-Rad, and S. B. Shokouhi. 2010. “A New Region-Based Active Contour Model with Skewness Wavelet Energy for Segmentation of SAR Images.” IEICE TRANSACTIONS on Information and Systems 93 (7): 1690–1699. https://doi.org/10.1587/transinf.E93.D.1690.
   Web of Science ®Google Scholar
• Andekah, Z. A., M. Naderan, and G. Akbarizadeh. 2017.”Semi-Supervised Hyperspectral Image Classification Using Spatial-Spectral Features and Superpixel-Based Sparse Codes.” In 2017 Iranian Conference on Electrical Engineering (ICEE), Iranian, 2229–2234.
   Google Scholar
• Bachmann, C. M., T. L. Ainsworth, and R. A. Fusina. 2006. “Improved Manifold Coordinate Representations of Large-Scale Hyperspectral Scenes.” IEEE Transactions on Geoscience and Remote Sensing 44 (10): 2786–2803. https://doi.org/10.1109/TGRS.2006.881801.
   Web of Science ®Google Scholar
• Bandos, T. V., L. Bruzzone, and G. Camps-Valls. 2009. “Classification of Hyperspectral Images with Regularized Linear Discriminant Analysis.” IEEE Transactions on Geoscience and Remote Sensing 47 (3): 862–873. https://doi.org/10.1109/TGRS.2008.2005729.
   Web of Science ®Google Scholar
• Bittencourt, H. R., D. A. de Oliveira Moraes, and V. Haertel. 2007.”A Binary Decision Tree Classifier Implementing Logistic Regression as a Feature Selection and Classification Method and Its Comparison with Maximum Likelihood.” In Paper presented at the 2007 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), Spain. https://doi.org/10.1109/IGARSS.2007.4423159.
   Google Scholar
• Chang, C. C., and C. J. Lin. 2011. “LIBSVM: A Library for Support Vector Machines.” ACM Transactions on Intelligent Systems and Technology (TIST) 2 (3): 1–27. https://doi.org/10.1145/1961189.1961199.
   Web of Science ®Google Scholar
• Chi, J., X. Shen, H. Yu, X. Shang, J. Chanussot, and Y. Shi. 2022. “Robust Linear Unmixing for Hyperspectral Remote Sensing Imagery Based on Enhanced Constraint of Classification.” In Paper presented at the IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), Malaysia. https://doi.org/10.1109/IGARSS46834.2022.9883935.
   Google Scholar
• Fu, H., G. Sun, J. Ren, A. Zhang, and X. Jia. 2022. “Fusion of PCA and Segmented-PCA Domain Multiscale 2-D-SSA for Effective Spectral-Spatial Feature Extraction and Data Classification in Hyperspectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing 60:1–14. https://doi.org/10.1109/TGRS.2020.3034656.
   Web of Science ®Google Scholar
• Guo, B., S. R. Gunn, R. I. Damper, and J. D. B. Nelson. 2008. “Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification.” IEEE Transactions on Image Processing 17 (4): 622–629. https://doi.org/10.1109/TIP.2008.918955.
   PubMed Web of Science ®Google Scholar
• Han, J., S. He, X. Qian, D. Wang, L. Guo, and T. Liu. 2013 . “An Object-Oriented Visual Saliency Detection Framework Based on Sparse Coding Representations.” IEEE Transactions on Circuits and Systems for Video Technology 23 (12): 2009–2021. https://doi.org/10.1109/TCSVT.2013.2242594.
   Web of Science ®Google Scholar
• Hao, Q., S. Li, L. Fang, and X. Kang. 2020. “Multiscale Feature Extraction with Gaussian Curvature Filter for Hyperspectral Image Classification.” In Paper presented at the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), USA. https://doi.org/10.1109/IGARSS39084.2020.9323640.
   Google Scholar
• He, N., M. E. Paoletti, J. M. Haut, L. Fang, S. Li, A. Plaza, and J. Plaza. 2019. “Feature Extraction with Multiscale Covariance Maps for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 57 (2): 755–769. https://doi.org/10.1109/TGRS.2018.2860464.
   Web of Science ®Google Scholar
• Kang, X., S. Li, and J. A. Benediktsson. 2013. “Spectral–Spatial Hyperspectral Image Classification with Edge-Preserving Filtering.” IEEE Transactions on Geoscience and Remote Sensing 52 (5): 2666–2677. https://doi.org/10.1109/TGRS.2013.2264508.
   Web of Science ®Google Scholar
• Kang, X., S. Li, and J. A. Benediktsson. 2014. “Feature Extraction of Hyperspectral Images with Image Fusion and Recursive Filtering.” IEEE Transactions on Geoscience and Remote Sensing 52 (6): 3742–3752. https://doi.org/10.1109/TGRS.2013.2275613.
   Web of Science ®Google Scholar
• Liang, H., W. Bao, B. Lei, J. Zhang, and K. Qu. 2020. “Adaptive Neighborhood Strategy Based Generative Adversarial Network for Hyperspectral Image Classification.” In Paper presented at the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), USA. https://doi.org/10.1109/IGARSS39084.2020.9324088.
   Google Scholar
• Liang, Y., L. Wei, and Q. Lu. 2021. “Multiple Feature Fusion for Fine Classification of Crops in UAV Hyperspectral Imagery.” In Paper presented at the 2021 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), Belgium. https://doi.org/10.1109/IGARSS47720.2021.9553490.
   Google Scholar
• Li, J., J. M. Bioucas-Dias, and A. Plaza. 2010. “Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression with Active Learning.” IEEE Transactions on Geoscience and Remote Sensing 48 (11): 4085–4098. https://doi.org/10.1109/TGRS.2010.2060550.
   Web of Science ®Google Scholar
• Li, J., J. M. Bioucas-Dias, and A. Plaza. 2011. “Spectral–Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields.” IEEE Transactions on Geoscience and Remote Sensing 50 (3): 809–823. https://doi.org/10.1109/TGRS.2011.2162649.
   Web of Science ®Google Scholar
• Li, W., C. Chen, H. Su, and Q. Du. 2015. “Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification.” IEEE Transactions on Geoscience and Remote Sensing 53 (7): 3681–3693. https://doi.org/10.1109/TGRS.2014.2381602.
   Web of Science ®Google Scholar
• Li, S., Q. Hao, X. Kang, and J. A. Benediktsson. 2018. “Gaussian Pyramid Based Multiscale Feature Fusion for Hyperspectral Image Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (9): 3312–3324. https://doi.org/10.1109/JSTARS.2018.2856741.
   Web of Science ®Google Scholar
• Liu, L., C. L. P. Chen, X. You, Y. Y. Tang, Y. Zhang, and S. Li. 2017. “Mixed Noise Removal via Robust Constrained Sparse Representation.” IEEE Transactions on Circuits and Systems for Video Technology 28 (9): 2177–2189. https://doi.org/10.1109/TCSVT.2017.2722232.
   Web of Science ®Google Scholar
• Ma, K. Y., and I. C. Chang. 2022. “Kernel-Based Constrained Energy Minimization for Hyperspectral Mixed Pixel Classification.” IEEE Transactions on Geoscience & Remote Sensing 60:1–23. https://doi.org/10.1109/TGRS.2021.3085801.
   Web of Science ®Google Scholar
• Marpu, P. R., M. Pedergnana, M. D. Mura, J. A. Benediktsson, and L. Bruzzone. 2013. “Automatic Generation of Standard Deviation Attribute Profiles for Spectral-Spatial Classification of Remote Sensing Data.” IEEE Geoscience & Remote Sensing Letters 10 (2): 293–297. https://doi.org/10.1109/LGRS.2012.2203784.
   Web of Science ®Google Scholar
• Melgani, F., and L. Bruzzone. 2004. “Classification of Hyperspectral Remote Sensing Images with Support Vector Machines.” IEEE Transactions on Geoscience and Remote Sensing 42 (8): 1778–1790. https://doi.org/10.1109/TGRS.2004.831865.
   Web of Science ®Google Scholar
• Mertens, T., J. Kautz, and F. V. Reeth. 2007. “Exposure Fusion.” In 15th Pacific Conference on Computer Graphics and Applications (PG’07), USA. https://doi.org/10.1109/PG.2007.17.
   Google Scholar
• Mishra, P., T. Feller, M. Schmuck, A. Nicol, and A. Nordon. 2019. “Early Detection of Drought Stress in Arabidopsis Thaliana Utilsing a Portable Hyperspectral Imaging Setup.” In Paper presented at the 2019 10th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Netherlands. https://doi.org/10.1109/WHISPERS.2019.8921077.
   Google Scholar
• Mohite, J., S. Sawant, R. Agarwal, A. Pandit, and S. Pappula. 2022. “Detection of Crop Water Stress in Maize Using Drone Based Hyperspectral Imaging.” In Paper presented at the IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), Malaysia. https://doi.org/10.1109/IGARSS46834.2022.9884686.
   Google Scholar
• Ozturk, S., Y. Artan, Y. E. Esin, and O. Ozdil. 2018. “A Novel 2-Stage Approach for Co2 Gas Detection Using Hyperspectral Imagery.” In Paper presented at the 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Netherlands. https://doi.org/10.1109/WHISPERS.2018.8747079.
   Google Scholar
• Pan, B., Z. Shi, and X. Xu. 2018. “MugNet: Deep Learning for Hyperspectral Image Classification Using Limited Samples.” ISPRS Journal of Photogrammetry and Remote Sensing 145:108–119. https://doi.org/10.1016/j.isprsjprs.2017.11.003.
   Web of Science ®Google Scholar
• Prasad, S., and L. M. Bruce. 2008. “Limitations of Principal Components Analysis for Hyperspectral Target Recognition.” IEEE Geoscience and Remote Sensing Letters 5 (4): 625–629. https://doi.org/10.1109/LGRS.2008.2001282.
   Web of Science ®Google Scholar
• Samadi, F., G. Akbarizadeh, and H. Kaabi. 2019. “Change Detection in SAR Images Using Deep Belief Network: A New Training Approach Based on Morphological Images.” IET Image Processing 13 (12): 2255–2264. https://doi.org/10.1049/iet-ipr.2018.6248.
   Web of Science ®Google Scholar
• Shang, X., S. Han, and M. Song. 2022. “Iterative Spatial-Spectral Training Sample Augmentation for Effective Hyperspectral Image Classification.” IEEE Geoscience and Remote Sensing Letters 19:1–5. https://doi.org/10.1109/LGRS.2021.3131373.
   Google Scholar
• Su, H., Y. Yu, Z. Wu, and Q. Du. 2021. “Random Subspace-Based K-Nearest Class Collaborative Representation for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 59 (8): 6840–6853. https://doi.org/10.1109/TGRS.2020.3029578.
   Web of Science ®Google Scholar
• Tirandaz, Z., G. Akbarizadeh, and H. Kaabi. 2020. “PolSar Image Segmentation Based on Feature Extraction and Data Compression Using Weighted Neighborhood Filter Bank and Hidden Markov Random Field-Expectation Maximization.” Measurement 153:107432. https://doi.org/10.1016/j.measurement.2019.107432.
   Web of Science ®Google Scholar
• Uz, S. S., T. J. Ames, N. Memarsadeghi, S. M. McDonnell, N. V. Blough, A. V. Mehta, and J. R. McKay. 2020. “Supporting Aquaculture in the Chesapeake Bay Using Artificial Intelligence to Detect Poor Water Quality with Remote Sensing.” In Paper presented at the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), USA. https://doi.org/10.1109/IGARSS39084.2020.9323465.
   Google Scholar
• Wang, W., H. Sheng, S. Liu, Y. Chen, J. Wan, and J. Mao. 2019. “An Edge-Preserving Active Contour Model with Bilateral Filter Based on Hyperspectral Image Spectral Information for Oil Spill Segmentation.” In Paper presented at the 2019 10th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Netherlands. https://doi.org/10.1109/WHISPERS.2019.8921042.
   Google Scholar
• Wang, Z., L. Wei, C. He, and Q. Lu. 2021. “Ammonia Nitrogen Monitoring of Urban Rivers with UAV-Borne Hyperspectral Remote Sensing Imagery.” In Paper presented at the 2021 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), Belgium. https://doi.org/10.1109/IGARSS47720.2021.9554632.
   Google Scholar
• Wu, S., J. Zhang, C. Shi, and W. Li. 2018. “Multiscale Spectral-Spatial Hyperspectral Image Classification with Adaptive Filtering.” In Paper presented at the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), Spain. https://doi.org/10.1109/IGARSS.2018.8518518.
   Google Scholar
• Xia, J., P. Ghamisi, N. Yokoya, and A. Iwasaki. 2017. “Random Forest Ensembles and Extended Multiextinction Profiles for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 56 (1): 202–216. https://doi.org/10.1109/TGRS.2017.2744662.
   Web of Science ®Google Scholar
• Xu, S., and J. Li. 2018. “Combining Contextual Information for Subspace Based Hyperspectral Image Classification.” In Paper presented at the 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Netherlands. https://doi.org/10.1109/WHISPERS.2018.8747132.
   Google Scholar
• Yan, Q., J. Zhang, and J. Feng. 2020. “Spectral-Spatial Classification of Hyperspectral Image Using PCA and Gabor Filtering.” In Paper presented at the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), USA. https://doi.org/10.1109/IGARSS39084.2020.9324555.
   Google Scholar
• Yin, B., and B. Cui. 2021. “Multi-Feature Extraction Method Based on Gaussian Pyramid and Weighted Voting for Hyperspectral Image Classification.” In Paper presented at the 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), China. https://doi.org/10.1109/ICCECE51280.2021.9342473.
   Google Scholar
• Yuan, Y., Q. Wang, and G. Zhu. 2014. “Fast Hyperspectral Anomaly Detection via High-Order 2-D Crossing Filter.” IEEE Transactions on Geoscience and Remote Sensing 53 (2): 620–630. https://doi.org/10.1109/TGRS.2014.2326654.
   Web of Science ®Google Scholar
• Yu, C., J. Huang, M. Song, Y. Wang, and C. I. Chang. 2022. “Edge-Inferring Graph Neural Network with Dynamic Task-Guided Self-Diagnosis for Few-Shot Hyperspectral Image Classification.” IEEE Transactions on Geoscience & Remote Sensing 60:1–13. https://doi.org/10.1109/TGRS.2022.3196311.
   Google Scholar
• Yu, H., H. Zhang, Y. Liu, K. Zheng, Z. Xu, and C. Xiao. 2022. “Dual-Channel Convolution Network with Image-Based Global Learning Framework for Hyperspectral Image Classification.” IEEE Geoscience and Remote Sensing Letters 19:1–5. https://doi.org/10.1109/LGRS.2021.3139358.
   Web of Science ®Google Scholar
• Zhang, T., N. Ai, L. Wang, J. Wang, and J. Peng. 2017. “Spectral-Spatial Hyperspectral Image Classification Based on Sparse Representation and Edge Preserving Filtering.” In Paper presented at the 2017 International Conference on the Frontiers and Advances in Data Science (FADS), China. https://doi.org/10.1109/FADS.2017.8253220.
   Google Scholar
• Zhao, Y., L. Ha, H. Wang, and X. Ma. 2022. “Few-Shot Class Incremental Learning for Hyperspectral Image Classification Based on Constantly Updated Classifier.” In Paper presented at the IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), Malaysia. https://doi.org/10.1109/IGARSS46834.2022.9883599.
   Google Scholar
• Zhou, Q., W. Bao, X. Zhang, and X. Ma. 2021. “A Joint Spatial-Spectral Representation Based Capsule Network for Hyperspectral Image Classification.” In Paper presented at the 2021 11th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Netherlands. https://doi.org/10.1109/WHISPERS52202.2021.9484019.
   Google Scholar