17 December 2015 An approach for hyperspectral image classification utilization spatial-spectral combined kernel SVM
Author Affiliations +
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hailei Wang, Hailei Wang, Bingyun Sun, Bingyun Sun, Yuanmiao Gui, Yuanmiao Gui, Yanping Chen, Yanping Chen, Dongbo Zhou, Dongbo Zhou, Xuelian Wu, 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); doi: 10.1117/12.2203636; https://doi.org/10.1117/12.2203636
PROCEEDINGS
7 PAGES


SHARE
Back to Top