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Research article

Superpixel linear independent preprocessing for endmember extraction

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 6698-6715 | Received 01 Jun 2023, Accepted 09 Oct 2023, Published online: 09 Nov 2023

References

  • Alam, F. I., J. Zhou, and A. W.-C. Liew. 2019. “Abundance-Guided Superpixels and Recurrent Neural Network for Hyperspectral Image Classification.” In 2019 Digital Image Computing: Techniques and Applications, DICTA 2019. https://doi.org/10.1109/DICTA47822.2019.8946060.
  • Alkhatib, M. Q., and M. Velez-Reyes. 2019. “Improved Spatial-Spectral Superpixel Hyperspectral Unmixing.” Remote Sensing 11 (20): 2374. https://doi.org/10.3390/rs11202374.
  • Bendoumi, M. A., T. Benlefki, and R. Saadi. 2019. “Pansharpening Multispectral Images Based on Unconstrained Least Square Spectral Unmixing.” In 2018 International Conference on Signal, Image, Vision and Their Applications, SIVA 2018. https://doi.org/10.1109/SIVA.2018.8660988.
  • Dian, R., S. Li, L. Fang, and Q. Wei. 2019. “Multispectral and Hyperspectral Image Fusion with Spatial-Spectral Sparse Representation.” Information Fusion 49:262–270. https://doi.org/10.1016/j.inffus.2018.11.012.
  • Erturk, A., S. Erturk, and A. Plaza. 2016. “Unmixing with SLIC Superpixels for Hyperspectral Change Detection.” In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 3370–3373. IEEE. https://doi.org/10.1109/IGARSS.2016.7729871.
  • Fang, B., Y. Bai, and Y. Li. 2020. “Combining Spectral Unmixing and 3D/2D Dense Networks with Early-Exiting Strategy for Hyperspectral Image Classification.” Remote Sensing 12 (5): 779. https://doi.org/10.3390/rs12050779.
  • Gao, D., Z. Hu, and R. Ye. 2018. “Self-Dictionary Regression for Hyperspectral Image Super-Resolution.” Remote Sensing 10 (10): 1574. https://doi.org/10.3390/rs10101574.
  • Huang, Y., J. Li, L. Qi, Y. Wang, and X. Gao. 2020. “Spatial-Spectral Autoencoder Networks for Hyperspectral Unmixing.” In International Geoscience and Remote Sensing Symposium (IGARSS), 2396–2399. https://doi.org/10.1109/IGARSS39084.2020.9324696.
  • Hussain, M., D. Chen, A. Cheng, H. Wei, and D. Stanley. 2013. “Change Detection from Remotely Sensed Images: From Pixel-Based to Object-Based Approaches.” Isprs Journal of Photogrammetry & Remote Sensing. 80:91–106. International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). https://doi.org/10.1016/j.isprsjprs.2013.03.006.
  • IDEAM. 2014. “Metodología Corine Land Cover.” http://www.ideam.gov.co/web/ecosistemas/metodologia-corine-land-cover.
  • Khajehrayeni, F., and H. Ghassemian. 2020. “Hyperspectral Unmixing Using Deep Convolutional Autoencoders in a Supervised Scenario.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:567–576. https://doi.org/10.1109/JSTARS.2020.2966512.
  • Kokaly, R. F., R. N. Clark, G. A. Swayze, K. Eric Livo, T. M. Hoefen, N. C. Pearson, R. A. Wise, et al. 2017. “USGS Spectral Library Version 7.” US Geological Survey Data Series 1035. https://doi.org/10.3133/DS1035.
  • Lillesand, T. M., R. W. Kiefer, and J. W. Chipman. 2015. Remote Sensing and Image Interpretation. 7th ed. Vol. 81. Photogrammetric Engineering & Remote Sensing. https://doi.org/10.14358/pers.81.8.615.
  • Luo, X., J. Yin, X. Luo, and X. Jia. 2019. “A Novel Adversarial Based Hyperspectral and Multispectral Image Fusion.” Remote Sensing 11 (5): 492. https://doi.org/10.3390/rs11050492.
  • Nascimento, J. M. P., and J. M. B. Dias. 2005. “Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data.” Î IEEE Transactions on Geoscience and Remote Sensing” 43 (4): 898–910. https://doi.org/10.1109/TGRS.2005.844293.
  • Nixon, M. S., and A. S. Aguado. January 2020. “Region-Based Analysis.” in Feature Extraction and Image Processing for Computer Vision, 399–432. Academic Press. https://doi.org/10.1016/B978-0-12-814976-8.00008-7.
  • Palsson, B., J. R. Sveinsson, and M. O. Ulfarsson. 2019. “Spectral-Spatial Hyperspectral Unmixing Using Multitask Learning.” Institute of Electrical and Electronics Engineers Access 7:148861–148872. https://doi.org/10.1109/ACCESS.2019.2944072.
  • Palsson, B., M. O. Ulfarsson, and J. R. Sveinsson. 2021. “Convolutional Autoencoder for Spectral-Spatial Hyperspectral Unmixing.” IEEE Transactions on Geoscience and Remote Sensing 59 (1): 535–549. https://doi.org/10.1109/TGRS.2020.2992743.
  • Panuju, D. R., D. J. Paull, and A. L. Griffin. 2020. “Change Detection Techniques Based on Multispectral Images for Investigating Land Cover Dynamics.” Remote Sensing 12 (11): 1–36. https://doi.org/10.3390/rs12111781.
  • Qi, L., J. Li, Y. Wang, Y. Huang, and X. Gao. 2020. “Spectral-Spatial-Weighted Multiview Collaborative Sparse Unmixing for Hyperspectral Images.” IEEE Transactions on Geoscience & Remote Sensing 58 (12): 8766–8779. https://doi.org/10.1109/TGRS.2020.2990476.
  • Richards, J. A., and X. Jia. 2013. “Remote Sensing Digital Image Analysis.” Remote Sensing Digital Image Analysis. https://doi.org/10.1007/978-3-662-03978-6.
  • Rousseeuw, P. J. 1987. “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis.” Journal of Computational and Applied Mathematics 20:53–65. https://doi.org/10.1016/0377-0427(87)90125-7.
  • Shah, D., T. Zaveri, Y. N. Trivedi, and A. Plaza. 2020a. “Entropy-Based Convex Set Optimization for Spatial-Spectral Endmember Extraction from Hyperspectral Images.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 13:4200–4213. https://doi.org/10.1109/JSTARS.2020.3008939.
  • Shah, D., T. Zaveri, Y. N. Trivedi, and A. Plaza. 2020b. “Entropy-Based Convex Set Optimization for Spatial-Spectral Endmember Extraction from Hyperspectral Images.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 13:4200–4213. https://doi.org/10.1109/JSTARS.2020.3008939.
  • Shen, X., W. Bao, and K. Qu. 2018. “Clustering Based Spatial Spectral Preprocessing for Hyperspectral Unmxing.” In ACM International Conference Proceeding Series, 313–316. https://doi.org/10.1145/3290420.3290475.
  • Sun, Y., and X. Zhang. 2018. “Composite Kernel Classification Using Spectral-Spatial Features and Abundance Information of Hyperspectral Image.” In Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. Vol. 2018 Septe. https://doi.org/10.1109/WHISPERS.2018.8747073.
  • Teng, Y., Y. Zhang, C. Ti, and J. Zhang. 2018. “Hyperspectral Image Resolution Enhancement Approach Based on Local Adaptive Sparse Unmixing and Subpixel Calibration.” Remote Sensing 10 (4): 592. https://doi.org/10.3390/rs10040592.
  • Wang, C.-J., H. Li, and Y.-Y. Tang. 2019. “Hyperspectral Unmixing Using Deep Learning.” In International Conference on Wavelet Analysis and Pattern Recognition. Vol. 2019 July. https://doi.org/10.1109/ICWAPR48189.2019.8946465.
  • Wang, P., M. D. Mura, J. Chanussot, and G. Zhang. 2019. “Soft-Then-Hard Super-Resolution Mapping Based on Pansharpening Technique for Remote Sensing Image.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (1): 334–344. https://doi.org/10.1109/JSTARS.2018.2885793.
  • Wang, P., Y. Wang, L. Zhang, and K. Ni. 2021. “Subpixel Mapping Based on Multisource Remote Sensing Fusion Data for Land-Cover Classes.” IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2021.3072943.
  • Wang, K., Y. Wang, X.-L. Zhao, J. C.-W. Chan, Z. Xu, and D. Meng. 2020. “Hyperspectral and Multispectral Image Fusion via Nonlocal Low-Rank Tensor Decomposition and Spectral Unmixing.” IEEE Transactions on Geoscience and Remote Sensing 58 (11): 7654–7671. https://doi.org/10.1109/TGRS.2020.2983063.
  • Wang, L., Z. Xing, D. Zhao, Y. Li, X. Yang, C. Hou, P. Li, and X. Zhao. 2018. “High Spatial-Spectral Resolution Image Fusion Algorithm Based on Spectral Mixture Analysis.” In Proceedings of 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference, ITOEC 2018, 408–411. https://doi.org/10.1109/ITOEC.2018.8740545.
  • Wang, M., B. Zhang, X. Pan, and S. Yang. 2018. “Group Low-Rank Nonnegative Matrix Factorization with Semantic Regularizer for Hyperspectral Unmixing.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (4): 1022–1029. https://doi.org/10.1109/JSTARS.2018.2805779.
  • Wei, J., and X. Wang. 2020. “An Overview on Linear Unmixing of Hyperspectral Data.” Mathematical Problems in Engineering. 2020:1–12. 2020. https://doi.org/10.1155/2020/3735403.
  • Xiong, F., J. Chen, J. Zhou, and Y. Qian. 2018. “Superpixel-Based Nonnegative Tensor Factorization for Hyperspectral Unmixing.” In International Geoscience and Remote Sensing Symposium (IGARSS), 2018 July:6392–6395. https://doi.org/10.1109/IGARSS.2018.8518642.
  • Xiong, F., K. Qian, J. Lu, J. Zhou, and Y. Qian. 2020. “Nonlocal Low-Rank Nonnegative Tensor Factorization for Hyperspectral Unmixing.” In International Geoscience and Remote Sensing Symposium (IGARSS), 2157–2160. https://doi.org/10.1109/IGARSS39084.2020.9324663.
  • Xiong, F., Y. Qian, J. Zhou, and Y. Y. Tang. 2019. “Hyperspectral Unmixing via Total Variation Regularized Nonnegative Tensor Factorization.” IEEE Transactions on Geoscience and Remote Sensing 57 (4): 2341–2357. https://doi.org/10.1109/TGRS.2018.2872888.
  • Xue, J., Y.-Q. Zhao, Y. Bu, W. Liao, J. C.-W. Chan, and W. Philips. 2021. “Spatial-Spectral Structured Sparse Low-Rank Representation for Hyperspectral Image Super-Resolution.” IEEE Transactions on Image Processing 30:3084–3097. https://doi.org/10.1109/TIP.2021.3058590.
  • Xu, X., J. Li, Y. Zhang, and S. Li. 2018. “A Subpixel Spatial-Spectral Feature Mining for Hyperspectral Image Classification.” In International Geoscience and Remote Sensing Symposium (IGARSS), 2018 July:8476–8479. https://doi.org/10.1109/IGARSS.2018.8518605.
  • Xu, C., Z. Wu, F. Li, S. Zhang, C. Deng, and Y. Wang. 2021. “Spectral-Spatial Joint Sparsity Unmixing of Hyperspectral Images Based on Framelet Transform.” Infrared Physics and Technology 112:112. https://doi.org/10.1016/j.infrared.2020.103564.
  • Yan, Y., W. Hua, X. Liu, Z. Cui, and D. Diao. 2019. “Spatial–Spectral Preprocessing for Spectral Unmixing.” International Journal of Remote Sensing 40 (4): 1357–1373. https://doi.org/10.1080/01431161.2018.1524590.
  • Yao, J., D. Hong, L. Xu, D. Meng, J. Chanussot, and Z. Xu. 2021. “Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing.” IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2021.3069845.
  • Yi, C., Y.-Q. Zhao, and J. C.-W. Chan. 2018. “Hyperspectral Image Super-Resolution Based on Spatial and Spectral Correlation Fusion.” IEEE Transactions on Geoscience and Remote Sensing 56 (7): 4165–4177. https://doi.org/10.1109/TGRS.2018.2828042.
  • Zhang, S., J. Li, H.-C. Li, C. Deng, and A. Plaza. 2018. “Spectral-Spatial Weighted Sparse Regression for Hyperspectral Image Unmixing.” IEEE Transactions on Geoscience & Remote Sensing 56 (6): 3265–3276. https://doi.org/10.1109/TGRS.2018.2797200.
  • Zhang, X., Y. Sun, and W. Qi. 2018. “Hyperspectral Image Classification Based on Extended Morphological Attribute Profiles and Abundance Information.” In Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. Vol. 2018 Septe. https://doi.org/10.1109/WHISPERS.2018.8747090.
  • Zhang, X., Y. Sun, J. Zhang, P. Wu, and L. Jiao. 2018. “Hyperspectral Unmixing via Deep Convolutional Neural Networks.” IEEE Geoscience and Remote Sensing Letters 15 (11): 1755–1759. https://doi.org/10.1109/LGRS.2018.2857804.
  • Zhang, S., G. Zhang, C. Deng, J. Li, S. Wang, J. Wang, and A. Plaza. 2020. “Spectral-Spatial Weighted Sparse Nonnegative Tensor Factorization for Hyperspectral Unmixing.” In International Geoscience and Remote Sensing Symposium (IGARSS), 2177–2180. https://doi.org/10.1109/IGARSS39084.2020.9323926.
  • Zhang, S., G. Zhang, F. Li, C. Deng, S. Wang, A. Plaza, and J. Li. 2021. “Spectral-Spatial Hyperspectral Unmixing Using Nonnegative Matrix Factorization.” IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2021.3074364.
  • Zhuang, L. 2022. “Parameter-Free Hyperspectral Unmixing NMF-QMV.” https://github.com/LinaZhuang/NMF-QMV_demo/releases/tag/v1.0.

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