Acta Optica Sinica, Volume. 39, Issue 5, 0528004(2019)
Hyperspectral Image Classification via Multiple-Feature-Based Improved Sparse Representation
A multiple-feature-based improved sparse representation (MFISR) method is proposed herein for the classification of hyperspectral images. The spectral feature, Gabor feature, and local binary pattern (LBP) feature are extracted from the hyperspectral image; subsequently, the sparse coefficients are solved and a 2-paradigm constraint is added. These obtained coefficients are used to determine the final class label of each test pixel. The experimental results demonstrate that the proposed MSIFR method exhibits excellent results for the detection of small samples, and its classification performance is stable and good.
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Feiyan Li, Hongtao Huo, Jing Li, Jie Bai. Hyperspectral Image Classification via Multiple-Feature-Based Improved Sparse Representation[J]. Acta Optica Sinica, 2019, 39(5): 0528004
Category: Remote Sensing and Sensors
Received: Aug. 13, 2018
Accepted: Feb. 19, 2019
Published Online: May. 10, 2019
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