Classification of Hyperspectral Remote Sensing Image Based on S3VM Model

doi:10.13474/j.cnki.11-2246.2017.0376

测绘通报 ›› 2017, Vol. 0 ›› Issue (12): 43-47.doi: 10.13474/j.cnki.11-2246.2017.0376

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Classification of Hyperspectral Remote Sensing Image Based on S³VM Model

WEI Lifei^1,2, FENG Xiuqiang¹, LI Dandan³, MOU Ziwei^1,2

1. Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China;
2. Hubei Key Laboratory of Regional Development and Environmental Response, Wuhan 430062, China;
3. Key Laboratory of Agri-informatics, Ministry of Agriculture, P. R. China, Beijing 100081, China

Received:2017-03-15 Revised:2017-05-24 Online:2017-12-25 Published:2018-01-05

Abstract

Abstract: The traditional hyperspectral remote sensing image classification is limited by the number of training samples,so it is difficult to obtain the better classification results.This paper proposes a hyperspectral remote sensing image classification method based on semi-supervised support vector machine of clustering kernel.The method constructs a kernel matrix to obtain more excellent classifier by semi-supervised support vector machine and unlabeled sample,and improves classification accuracy based on small sample.The experimental results show that the classification accuracy of this method proposed in this paper is better than the traditional method,and has good stability.

Key words: hyperspectral remote sensing image, S³VM model, unlabeled sample, semi-supervised classification

CLC Number:

P237

WEI Lifei, FENG Xiuqiang, LI Dandan, MOU Ziwei. Classification of Hyperspectral Remote Sensing Image Based on S³VM Model[J]. 测绘通报, 2017, 0(12): 43-47.

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Classification of Hyperspectral Remote Sensing Image Based on S³VM Model

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