Journal
ADVANCES IN SPACE RESEARCH
Volume 59, Issue 4, Pages 954-967Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.asr.2016.11.006
Keywords
Hyperspectral imagery classification (HIC); Area median filtering (AMF); Spectral-spatial kernel (SSK); Support vector machine (SVM)
Categories
Funding
- Graduate Innovation Foundation of Jiangsu Province [KYLX16_0781, CXZZ13_0239]
- 111 Project [B12018]
- PAPD of Jiangsu Higher Education Institutions
Ask authors/readers for more resources
Spectral-based classification methods have gained increasing attention in hyperspectral imagery classification. Nevertheless, the spectral cannot fully represent the inherent spatial distribution of the imagery. In this paper, a spectral-spatial kernel-based method for hyperspectral imagery classification is proposed. Firstly, the spatial feature was extracted by using area median filtering (AMF). Secondly, the result of the AMF was used to construct spatial feature patch according to different window sizes. Finally, using the kernel technique, the spectral feature and the spatial feature were jointly used for the classification through a support vector machine (SVM) formulation. Therefore, for hyperspectral imagery classification, the proposed method was called spectral-spatial kernel-based support vector machine (SSF-SVM). To evaluate the proposed method, experiments are performed on three hyperspectral images. The experimental results show that an improvement is possible with the proposed technique in most of the real world classification problems. (C) 2016 COSPAR. Published by Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available