Acta Photonica Sinica, Volume. 44, Issue 12, 1228001(2015)
Classification of Hyperspectral Remote Sensing Images Based on Supervised Sparse Manifold Embedding
Sparse Manifold Clustering and Embedding (SMCE) can adaptively select nearby points that lie in the same manifold based on sparse representation. However, there is no explicit project matrix in SMCE, and the unsupervised nature restricts its discriminating capability. Supervised Sparse Manifold Embedding (SSME) was proposed for dimensionality reduction of hyperspectral data. At first, the SSME method finds the sparse coefficients in affine subspace by solving a sparse optimization problem. It constructs the similarity weight matrix using the sparse coefficients, and naturally incorporates the label information into the weights. Then, it tries to extract discriminative features by increasing the compactness between homogeneous data in a low-dimensional embedding space. The experiments show that the SSME method not only inherits the merits of the sparsity property but also improves the severability of data points from different classes.
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HUANG Hong, YANG Ya-qiong, LUO Fu-lin, FENG Hai-liang. Classification of Hyperspectral Remote Sensing Images Based on Supervised Sparse Manifold Embedding[J]. Acta Photonica Sinica, 2015, 44(12): 1228001
Received: May. 26, 2015
Accepted: --
Published Online: Dec. 23, 2015
The Author Email: Hong HUANG (hhuang@cqu.edu.cn)