An approach for hyperspectral image classification utilization spatial-spectral combined kernel SVM

Hailei Wang; Bingyun Sun; Yuanmiao Gui; Yanping Chen; Dongbo Zhou; Xuelian Wu

doi:10.1117/12.2203636

17 December 2015 An approach for hyperspectral image classification utilization spatial-spectral combined kernel SVM

Hailei Wang, Bingyun Sun, Yuanmiao Gui, Yanping Chen, Dongbo Zhou, Xuelian Wu

Proceedings Volume 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis; 98110H (2015) https://doi.org/10.1117/12.2203636
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

Hyperspectral images belong to high-dimensional data having a lot of redundancy information when they are directly used to classification. Support vector machine (SVM) can be employed to map hyperspectral data to high dimensional space effectively and make them linearly separable. In this paper, spectral and spatial information of hyperspectral images were used to construct SVM kernel function respectively. This paper proposed a hyperspectral image classification method utilization spatial-spectral combined kernel SVM in order to improve classification accuracy. The proposed method was used to classify AVIRIS hyperspectral images. The results demonstrated that the proposed SVM method can achieve 96.13% overall accuracy for the single category classification and 84.81% overall accuracy for multi-class classification only using ten percent of the total samples as the training samples. That is to say, the proposed method can make full use of the spectral information and spatial information of hyperspectral data, and effectively distinguish different categories compared with the traditional SVM for classification.

Citation Download Citation

Hailei Wang, Bingyun Sun, Yuanmiao Gui, Yanping Chen, Dongbo Zhou, and Xuelian Wu "An approach for hyperspectral image classification utilization spatial-spectral combined kernel SVM", Proc. SPIE 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis, 98110H (17 December 2015); https://doi.org/10.1117/12.2203636

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