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Fusion of Graph Embedding and Sparse Representation for Feature Extraction and Classification of Hyperspectral Imagery
Ist Teil von
Photogrammetric engineering and remote sensing, 2017-01, Vol.83 (1), p.37-46
Ort / Verlag
American Society for Photogrammetry and Remote Sensing
Erscheinungsjahr
2017
Link zum Volltext
Quelle
EZB Electronic Journals Library
Beschreibungen/Notizen
The graph embedding algorithms have been widely applied for feature extraction ( FE ) of hyperspectral imagery ( HSI ). These methods need to construct a similarity graph with k-nearest neighbors or ∈-radius ball. However, the neighborhood
size is difficult to select in real-world applications. To solve the problem, we propose a new unsupervised FE method, called sparsity preserving analysis ( SPA ), based on sparse representation and graph embedding. The proposed algorithm
utilizes sparse representation to obtain the sparse coefficients of data. Then, it constructs a new graph with the sparse coefficients that reveals the sparse properties of data. Finally, the structure of the graph is preserved in low-dimensional space to obtain a transformation matrix for
FE . In addition, a new classification method, termed sparse neighborhood classifier ( SNC ), is designed using the sparse representation-based methodology. It uses the sparse coefficients of a new sample to obtain the similarity weights in
each class. Then, the label information of the new sample is obtained by the weights. The classification accuracies of SPA with SNC reach to 86.9 percent and 80.6 percent on PaviaU and Urban HSI data sets, respectively.
The results demonstrate that SPA with SNC can effectively extract low-dimensional features and improve the discriminating power for HSI classification.