AI-Driven Hyperspectral Image Classification using Low-Rank Representation and Spatial-Spectral Information
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمند
1 - Assistant Professor, Department of Electrical Engineering, Khorm.C., Islamic Azad University, Khormoj, Iran
کلید واژه: تصویر ابر طیفی, طبقه بندی, نمایش رتبه پایین, اطلاعات مکانی,
چکیده مقاله :
Hyperspectral image (HSI) classification is one of the most important processes on these images, which artificial intelligence (AI) techniques have recently achieved significant success in this process. Data representation using a low-dimensional subspace is critical in classification task of HSI. By employing the Low-Rank Representation (LLR) approach, low-dimensional representations from data can be effectively extracted. Since this method neglects local information, the extracted features are not sufficiently rich and informative for classification. This paper proposes a machine learning method for hyperspectral image classification, which involves employing the Structure of the Data Regularized LLR with Dictionary Learning (SDLRRDL) model. Our AI-based model, SDLRRDL, presents an approach for learning data structures through a low-rank and sparse representation. Also, to leverage structural data information, a penalty is added to the low-rank representation model. The method can create similar features for data of the same class by combining image class signature and spectral-spatial information. Moreover, image samples are represented through a linear combination of dictionary atoms. Rich and informative features are extracted through the trained dictionary utilizing a training data set that better matches the training content. Then, extracted features are classified using the support vector machine with high accuracy. Simulation results demonstrate that the proposed method has a superior classification accuracy compared to state-of-the-art methods on three popular HSI datasets. The proposed method improves the classification accuracy of the state-of-the-art methods more than 2.13, 0.2, and 0.6 percent on the Indian Pines, Pavia University, and Salinas datasets, respectively.
Hyperspectral image (HSI) classification is one of the most important processes on these images, which artificial intelligence (AI) techniques have recently achieved significant success in this process. Data representation using a low-dimensional subspace is critical in classification task of HSI. By employing the Low-Rank Representation (LLR) approach, low-dimensional representations from data can be effectively extracted. Since this method neglects local information, the extracted features are not sufficiently rich and informative for classification. This paper proposes a machine learning method for hyperspectral image classification, which involves employing the Structure of the Data Regularized LLR with Dictionary Learning (SDLRRDL) model. Our AI-based model, SDLRRDL, presents an approach for learning data structures through a low-rank and sparse representation. Also, to leverage structural data information, a penalty is added to the low-rank representation model. The method can create similar features for data of the same class by combining image class signature and spectral-spatial information. Moreover, image samples are represented through a linear combination of dictionary atoms. Rich and informative features are extracted through the trained dictionary utilizing a training data set that better matches the training content. Then, extracted features are classified using the support vector machine with high accuracy. Simulation results demonstrate that the proposed method has a superior classification accuracy compared to state-of-the-art methods on three popular HSI datasets. The proposed method improves the classification accuracy of the state-of-the-art methods more than 2.13, 0.2, and 0.6 percent on the Indian Pines, Pavia University, and Salinas datasets, respectively.
[1]M. Hamed and F. Hajiani, "A method for segmenting remote sensing images using the Watershed algorithm and Fuzzy C-Means clustering," Journal of Communication Engineering, vol. 10, pp. 65-72, 2020. doi: 20.1001.1.29809231.1399.10.37.6.0
[2]D. Ma, S. Xu, Z. Jiang, and Y. Yuan, "Central Pixel-Based Dual-Branch Network for Hyperspectral Image Classification," Remote Sensing, vol. 17, p. 1255, 2025. doi: 10.3390/rs17071255
[3]Y. Xiao, H. Wang, and W. Xu, "Parameter selection of Gaussian kernel for one-class SVM," IEEE transactions on cybernetics, vol. 45, pp. 941-953, 2015.DOI: 10.1109/TCYB.2014.2340433.
[4] A. Farhadi, M. Mirzarezaee, A. Sharifi, and M. Teshnehlab, "Unsupervised domain adaptation for image classification based on deep neural networks," Intelligent Multimedia Processing and Communication Systems (IMPCS), vol. 4, pp. 27-37, 2023. doi: 10.71856/impcs.2023.903575
[5]Z. Chen, H. Yang, Q. Liu, Y. Liu, M. Zhu, and X. Liang, "Deep Learning for Hyperspectral Image Classification: A Critical Evaluation via Mutation Testing," Remote Sensing, vol. 16, p. 4695, 2024. doi: 10.3390/rs16244695
[6] M. Hariri, N. Hydarzadeh, " Face Recognition in Images Using Viola-Jones Method and Image Texture Analysis," Intelligent Multimedia Processing and Communication Systems (IMPCS), vol. 4, pp. 1-10, 2023.doi: 10.71856/impcs.2023.903604
[7]S. G. Azar, S. Meshgini, T. Y. Rezaii, and S. Beheshti, "Hyperspectral image classification based on sparse modeling of spectral blocks," Neurocomputing, vol. 407, pp. 12-23, 2020. doi: 10.1016/j.neucom.2020.04.138
[8]F. Hajiani and A. Mahmoodzadeh, "Hyperspectral Image Segmentation using Homogeneous Area Limiting and Shortest Path Algorithm," International Journal of Advanced Computer Science and Applications, vol. 8, 2017. doi: 10.14569/IJACSA.2017.080929
[9]K. Bhardwaj and S. Patra, "Spectral Spatial Classification of Hyperspectral Image by Exploiting Extended Threshold-Free Attribute Profile and Transformer-Encoder Based Network," in 2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), 2024, pp. 1-4.
[10]Z. Zhong, Y. Li, L. Ma, J. Li, and W.-S. Zheng, "Spectral–spatial transformer network for hyperspectral image classification: A factorized architecture search framework," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2021. doi: 10.1109/TGRS.2021.3115699
[11] Z. Zhao, X. Xu, S. Li, and A. Plaza, "Hyperspectral image classification using groupwise separable convolutional vision transformer network," IEEE Transactions on Geoscience and Remote Sensing, 2024. doi: 10.1109/TGRS.2024.3377610
[12]M. Ahmad, M. H. F. Butt, A. M. Khan, M. Mazzara, S. Distefano, M. Usama, et al., "Spatial–spectral morphological mamba for hyperspectral image classification," Neurocomputing, vol. 636, p. 129995, 2025. doi: https:0.1016/j.neucom.2025.129995
[13]C. Li, D. Zhu, C. Wu, B. Du, and L. Zhang, "Global overcomplete dictionary-based sparse and nonnegative collaborative representation for hyperspectral target detection," IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-14, 2024. doi: doi.org/10.1109/TGRS.2024.3381719
[14] C. Li, Y. Ma, X. Mei, C. Liu, and J. Ma, "Hyperspectral image classification with robust sparse representation," IEEE Geoscience and Remote Sensing Letters, vol. 13, pp. 641-645, 2016. doi: 10.1109/LGRS.2016.2532380
[15]M. Cui and S. Prasad, "Class-dependent sparse representation classifier for robust hyperspectral image classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 53, pp. 2683-2695, 2015. doi: 10.1109/TGRS.2014.2363582.
[16]G. Liu and S. Yan, "Latent low-rank representation for subspace segmentation and feature extraction," in Proceedings of the 2011 international conference on computer vision, ed Barcelona, Spain: IEEE, 2011, pp. 1615-1622. doi: 10.1109/TGRS.2014.2363582.
[17]X. Cheng, Y. Zhu, J. Song, G. Wen, and W. He, "A novel low-rank hypergraph feature selection for multi-view classification," Neurocomputing, vol. 253, pp. 115-121, 2017. doi: 10.1109/TGRS.2014.2363582.
[18]Y. Wang, J. Mei, L. Zhang, B. Zhang, A. Li, Y. Zheng, et al., "Self-supervised low-rank representation (SSLRR) for hyperspectral image classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 56, pp. 5658-5672, 2018. doi: 10.1109/TGRS.2018.2823750
[19]F. Hajiani, "P, Naser, and A. Keshavarz,“Hyperspectral Image Classification Using Low-Rank Representation and Spectral-Spatial Information,”" Journal of Southern Communication Engineering, vol. 11, pp. 27-38, 2022. doi: 10.30495/jce.2022.689206
[20] F. Hajiani, N. Parhizgar, and A. Keshavarz, "Hyperspectral image classification using cluster based graph regularized low rank representation and dictionary learning," Neurocomputing, vol. 462, pp. 208-220, 2021. doi: 10.1016/j.neucom.2021.07.075
[21]Z. He, L. Liu, R. Deng, and Y. Shen, "Low-rank group inspired dictionary learning for hyperspectral image classification," Signal Processing, vol. 120, pp. 209-221, 2016. doi: 10.1016/j.sigpro.2015.09.004
[22] S. Yang, Y. Zhang, Y. Ding, and D. Hong, "Superpixelwise low-rank approximation-based partial label learning for hyperspectral image classification," IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023. doi: 10.1109/LGRS.2023.3279985
[23]A. Goel and A. Majumdar, "Semi-Supervised Graphical Deep Dictionary Learning for Hyperspectral Image Classification From Limited Samples," in 2024 IEEE International Conference on Image Processing (ICIP), 2024, pp. 2108-2114.
[24]R. Gribonval, "Fast matching pursuit with a multiscale dictionary of Gaussian chirps," IEEE Transactions on signal Processing, vol. 49, pp. 994-1001, 2001 .doi: 10.1109/78.917803
[25] S. G. Mallat and Z. Zhang, "Matching pursuits with time-frequency dictionaries," IEEE Transactions on signal processing, vol. 41, pp. 3397-3415, 1993. doi: 10.1109/78.258082
[26] S. Fischer, G. Cristóbal, and R. Redondo, "Sparse overcomplete Gabor wavelet representation based on local competitions," IEEE Transactions on Image Processing, vol. 15, pp. 265-272, 2006. doi: 10.1109/TIP.2005.860614
[27] W. Fu, S. Li, L. Fang, and J. A. Benediktsson, "Contextual online dictionary learning for hyperspectral image classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 56, pp. 1336-1347, 2017. doi: 10.1109/TGRS.2017.2761893
[28] A. Soltani-Farani, H. R. Rabiee, and S. A. Hosseini, "Spatial-aware dictionary learning for hyperspectral image classification," IEEE Transactions on geoscience and remote sensing, vol. 53, pp. 527-541, 2015. doi: 10.1109/TGRS.2014.2325067.
[29] M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation," IEEE Transactions on signal processing, vol. 54, pp. 4311-4322, 2006. doi: 10.1109/TSP.2006.881199
[30] A. Foi, V. Katkovnik, and K. Egiazarian, "Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images," IEEE transactions on image processing, vol. 16, pp. 1395-1411, 2007. doi: 10.1109/TIP.2007.891788
[31] Z. Lin, A. Ganesh, J. Wright, L. Wu, M. Chen, and Y. Ma, "Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix," Coordinated Science Laboratory Report no. UILU-ENG-09-2214, DC-246, 2009. .doi: 09-2214, DC-246, 2009
[32]J.-F. Cai, E. J. Candès, and Z. Shen, "A singular value thresholding algorithm for matrix completion," SIAM Journal on optimization, vol. 20, pp. 1956-1982, 2010. doi: https: 10.1137/ 080738970
[33]J. Nocedal and S. Wright, Numerical optimization, Second ed. New York: Springer-Verlag, 2006.
[34]Z. Lin, R. Liu, and Z. Su, "Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation," Advances in Neural Information Processing Systems, vol. 24, pp. 612-620, 2011.
[35] Z. Wen, D. Goldfarb, and W. Yin, "Alternating direction augmented Lagrangian methods for semidefinite programming," Mathematical Programming Computation, vol. 2, pp. 203-230, 2010/12/01 2010. doi: 10.1007/s12532-010-0017-1.
[36]J. Mairal, F. Bach, J. Ponce, and G. Sapiro, "Online learning for matrix factorization and sparse coding," Journal of Machine Learning Research, vol. 11, 2010.
[37] G. Camps-Valls, L. Gomez-Chova, J. Muñoz-Marí, J. Vila-Francés, and J. Calpe-Maravilla, "Composite kernels for hyperspectral image classification," IEEE geoscience and remote sensing letters, vol. 3, pp. 93-97, 2006. doi: 10.1109/LGRS.2005.857031
[38]M. Graña, M. A. Veganzons, and B. Ayerdi, "Hyperspectral remote sensing scenes," ed. Accessed Jun 1, 2018 . http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes, 2018.