[1]YOU Yaping,CHENG Yun,SU Songzhi,et al.Hyperspectral image classification based on spectral-spatial combination features and graph cut[J].CAAI Transactions on Intelligent Systems,2015,10(2):201-208.[doi:10.3969/j.issn.1673-4785.201410040]
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Hyperspectral image classification based on spectral-spatial combination features and graph cut

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 2 Page number: 201-208 Column: 学术论文—机器学习 Public date: 2015-04-25
Title:
Hyperspectral image classification based on spectral-spatial combination features and graph cut
Author(s):
YOU Yaping1; 3;  CHENG Yun2;  SU Songzhi1; 3;  CAO Donglin1; 3;  LI Shaozi1; 3
1. School of Information Science and Technology, Xiamen University, Xiamen 361005, China;
2. Department of Information Science and Engineering, Hunan University of Humanities, Science and Technology, Loudi 417000, China;
3. Fujian Key Laboratory of the Brain-Like Intelligent Systems, Xiamen 361005, China
Keywords:
hyperspectral; image classification; spectral feature; spatial feature; spectral-spatial combination feature; mean features; support vector machines; graph cut
CLC:
TP391.4
DOI:
10.3969/j.issn.1673-4785.201410040
Abstract:
The high-dimension of the feature vs. small-size of training set is an unsolved problem in the hyperspectral image classification task. To solve this problem a two-step classification method is proposed. Firstly, a preliminary classification is performed by the support vector machine (SVM) and the classification results are used to calculate the mean feature (MF) of each class. Secondly, a classification based on the graph cut theory is applied with the MFs as an input of the energy function. The experimental results showed that spatially nearby pixels have large possibilities of having the same label and similar features. Therefore, a new feature called spectral-spatial combination (SSC) is extracted that combines the spectral-based feature and spatial-based feature. The SSC feature contains the related spectral and spatial information of each pixel and provides better classification performance and robustness. Experiment results on the Indian Pine dataset and the Pavia University dataset demonstrated the effectiveness of the proposed method.
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