HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON MANIFOLD DATA ANALYSIS AND SPARSE SUBSPACE PROJECTION
Keywords:Hyperspectral Image, Classification, Sparse Representation, Manifold Learning, Subspace Projection
Aiming at the problem of "dimension disaster" in hyperspectral image classification, a method of dimension reduction based on manifold data analysis and sparse subspace projection (MDASSP) is proposed. The sparse coefficient matrix is established by the new method, and the sparse subspace projection is carried out by the optimization method. To keep the geometric structure of the manifold, the objective function is regularized by the manifold learning method. The new method combines sparse coding and manifold learning to generate features with better classification ability. The experimental results show that the new method is better than other methods in the case of small samples.
Azadeh Kianisarkaleh, Hassan Ghassemian (2016). Spatial-spectral Locality Preserving Projection for Hyperspectral Image Classification with Limited Training Samples, International Journal of Remote Sensing, 37(21):5045-5059. Retrieved from https://doi.org/10.1080/01431161.2016.1226523 DOI: https://doi.org/10.1080/01431161.2016.1226523
Deng S , Xu Y , He Y (2015), A hyperspectral Image Classification Framework and Its Application, Information Sciences, 299(1):379-393. Retrieved from https://doi.org/10.1016/j.ins.2014.12.025 DOI: https://doi.org/10.1016/j.ins.2014.12.025
Dong S , Quan Y , Feng W (2021), A Pixel Cluster CNN and Spectral-Spatial Fusion Algorithm for Hyperspectral Image Classification With Small-Size Training Samples, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP(99):1-1. Retrieved from https://doi.org/10.1109/JSTARS.2021.3068864 DOI: https://doi.org/10.1109/JSTARS.2021.3068864
Dongyang Wu, Li M A (2018), Multi-manifold LE Algorithm for Dimension Reduction and Classification of Multitemporal Hyperspectral Image, Remote Sensing for Land & Resources, 30(2): 80-86.
Feng Z, Yang S, Wang M (2019), Learning Dual Geometric Low-Rank Structure for Semisupervised Hyperspectral Image Classification, IEEE Transactions on Cybernetics, 10(1):1-13.
Fuding Xie, Cunkuan Lei, Fangfei Li (2019), Unsupervised Hyperspectral Feature Selection based on Fuzzy c-means and Grey Wolf Optimizer, International Journal of Remote Sensing, 40(9):3344-3367.Retrieved from https://doi.org/10.1080/01431161.2018.1541366 DOI: https://doi.org/10.1080/01431161.2018.1541366
Gao L , Yu H , Zhang B (2016), Locality-preserving Sparse Representation-based Classification in Hyperspectral Imagery, Journal of Applied Remote Sensing, 10(4): 42-54. Retrieved from https://doi.org/10.1117/1.JRS.10.042004 DOI: https://doi.org/10.1117/1.JRS.10.042004
Hairong Wang, Turgay Celik (2018). Sparse Representation-based Hyperspectral Image Classification, Signal Image & Video Processing, 12(5):1009-1017. Retrieved from https://doi.org/10.1007/s11760-018-1249-1 DOI: https://doi.org/10.1007/s11760-018-1249-1
Jayaprakash C , Damodaran B B , Viswanathan S (2020), Randomized Independent Component Analysis and Linear Discriminant Analysis Dimensionality Reduction Methods for Hyperspectral Image Classification, Journal of Applied Remote Sensing, 14(3). Retrieved from https://doi.org/10.1117/1.JRS.14.036507 DOI: https://doi.org/10.1117/1.JRS.14.036507
Lv M , Zhao X , Liu L (2017), Discriminant Collaborative Neighborhood Preserving Embedding for Hyperspectral Imagery, Journal of Applied Remote Sensing, , 11(4):1-17. Retrieved from https://doi.org/10.1117/1.JRS.11.046004 DOI: https://doi.org/10.1117/1.JRS.11.046004
Qi Wang, Zhaotie Meng, Xuelong Li. (2017) Locality Adaptive Discriminant Analysis for Spectral-Spatial Classification of Hyperspectral Images, IEEE Geoscience & Remote Sensing Letters, 14(11):2077-2081. Retrieved from https://doi.org/10.1109/LGRS.2017.2751559 DOI: https://doi.org/10.1109/LGRS.2017.2751559
Qiao L , Chen S , Tan X (2010), Sparsity Preserving Projections with Applications to Face Recognition, Pattern Recognition, 43(1):331-341. Retrieved from https://doi.org/10.1016/j.patcog.2009.05.005 DOI: https://doi.org/10.1016/j.patcog.2009.05.005
Ren R, Bao W. (2019),Hyperspectral Image Classification Based on Belief Propagation with Multi-features and Small Sample Learning, Journal of the Indian Society of Remote Sensing, 47(5):1-10.Retrieved from https://doi.org/10.1007/s12524-018-00934-y DOI: https://doi.org/10.1007/s12524-018-00934-y
Tabejamaat M , Mousavi A (2017), Manifold Sparsity Preserving Projection for Face and Palmprint Recognition, Multimedia Tools and Applications, 77(16):1-26. Retrieved from https://doi.org/10.1007/s11042-017-4881-9 DOI: https://doi.org/10.1007/s11042-017-4881-9
Uddin, Md. Palash, Mamun, Md. Al, Hossain, Md. Ali. (2019), Effective Feature Extraction Through Segmentation-based Folded-PCA for Hyperspectral Image Classification, International Journal of Remote Sensing, 40(18): 7190-7220. Retrieved from https://doi.org/10.1080/01431161.2019.1601284 DOI: https://doi.org/10.1080/01431161.2019.1601284
Wan Li, Liangpei Zhang, Lefei Zhang (2017), GPU Parallel Implementation of Isometric Mapping for Hyperspectral Classification, IEEE Geoscience & Remote Sensing Letters, 14(9): 1532 - 1536. Retrieved from https://doi.org/10.1109/LGRS.2017.2720778 DOI: https://doi.org/10.1109/LGRS.2017.2720778
Wang A, Wang Y, Chen Y (2019). Hyperspectral Image Classification based on Convolutional Neural Network and Random Forest, Remote Sensing Letters, 10(11):1086-1094. Retrieved from https://doi.org/10.1080/2150704X.2019.1649736 DOI: https://doi.org/10.1080/2150704X.2019.1649736
Xiangpo Wei, Xuchu Yu, Bing Liu (2019), Convolutional Neural Networks and Local Binary Patterns for Hyperspectral Image Classification , European Journal of Remote Sensing, 52(1):448-462.Retrieved from https://doi.org/10.1080/22797254.2019.1634980 DOI: https://doi.org/10.1080/22797254.2019.1634980
Yc A, Hl A, Liang Y A (2021), Hyperspectral Image Classification With Discriminative Manifold Broad Learning System, Neurocomputing.
Yongguang Zhai, Lifu Zhang, Nan Wang (2016), A Modified Locality-Preserving Projection Approach for Hyperspectral Image Classification, IEEE Geoscience & Remote Sensing Letters, 13(8):1059-1063. Retrieved from https://doi.org/10.1109/LGRS.2016.2564993 DOI: https://doi.org/10.1109/LGRS.2016.2564993
Yuan Y, Wang C, Jiang Z (2021). Proxy-Based Deep Learning Framework for Spectral-Spatial Hyperspectral Image Classification: Efficient and Robust, IEEE Transactions on Geoscience and Remote Sensing, PP(99):1-15. Retrieved from https://doi.org/10.1109/TGRS.2021.3054008 DOI: https://doi.org/10.1109/TGRS.2021.3054008
How to Cite
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- That it is not under consideration for publication elsewhere.
- That its release has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Authors who publish with International Journal of Engineering Technologies and Management Research agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or edit it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
For More info, please visit CopyRight Section