Computer Science ›› 2016, Vol. 43 ›› Issue (2): 89-94.doi: 10.11896/j.issn.1002-137X.2016.02.020

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Hyperspectral Image Classification Method Based on Watershed Segmentation and Sparse Representation

SHU Su and YANG Ming   

  • Online:2018-12-01 Published:2018-12-01

Abstract: In recent years,the classification has attracted wide attention.Many machine learning methods have been applied in hyperspectral image classification,such as SVM,neural network and decision tree.But in the hyperspectral image,different materials may have the same spectra and the same material in different locations may have different spectra,consequently bringing a challenge for accurate classification of hyperspectral image.So,we made use of the spatial information extracted from the watershed segementation and the sparse representation to get a more accurate classification results.Firstly,we extracted regional information from hyperspectral image by watershed segementation,then classificated all the samples in a region once.The effectiveness of our proposed method was evaluated via two images.And the results show that it exhibits state-of-the-art performance.

Key words: Hyperspectral image,Sparse representation,Classification,Watershed

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