Hyperspectral Image Land Cover Classification Algorithm Based on Spatial-spectral Coordination Embedding

doi:10.11947/j.AGCS.2016.20150654

Abstract

Abstract: Aiming at the problem that in hyperspectral image land cover classification, the traditional classification methods just apply the spectral information while they ignore the relationship between the spatial neighbors, a new dimensionality algorithm called spatial-spectral coordination embedding (SSCE) and a new classifier called spatial-spectral coordination nearest neighbor (SSCNN) were proposed in this paper. Firstly, the proposed method defines a spatial-spectral coordination distance and the distance is applied to the neighbor selection and low-dimensional embedding. Then, it constructs a spatial-spectral neighborhood graph to maintain the manifold structure of the data set, and enhances the aggregation of data through raising weight of the spatial neighbor points to extract the discriminant features. Finally, it uses the SSCNN to classify the reduced dimensional data. Experimental results using PaviaU and Salinas data set show that the proposed method can effectively improve ground objects classification accuracy comparing with traditional spectral classification methods.

Key words: hyperspectral image, dimensionality reduction, spatial-spectral coordination, manifold structure, classification

CLC Number:

P237

HUANG Hong, ZHENG Xinlei. Hyperspectral Image Land Cover Classification Algorithm Based on Spatial-spectral Coordination Embedding[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(8): 964-972.

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