Abstract The curse of dimensionality can be caused by directly using representation learning to classify hyperspectral image due to high dimensionality, high correlation between bands and limited samples of the hyperspectral image. For the hyperspectral image, not all spectral bands are available for specific classification tasks. Therefore, spatial aware collaborative representation based on augmented spatial spectral features network is proposed in this paper. A hierarchical spatial spectral features network is built according to the low dimensional manifolds inherent in the hyperspectral image. Features of high dimensional data are extracted by training network. Spatial aware collaborative representation algorithms are utilized for classification. Experiments on two hyperspectral remote sensing datasets, Indian Pines and Pavia University, verify the effectiveness of the proposed algorithm.
Fund:National Natural Science Foundation of China(No.61772252), Natural Science Foundation of Liaoning Province(No.2019-MS-216), Program for Liaoning Innovative Talents
Corresponding Authors:
ZHANG Yong, Ph.D., professor. His research interests include data mining and intelligent computing.
About author:: LIU Shuang, master student. Her research interests include machine learning and intelligent computing.
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2021, Vol. 34 
