Abstract
| Language | English |
|---|---|
| Number of pages | 12 |
| Journal | IEEE Transactions on Geoscience and Remote Sensing |
| Early online date | 2 Jul 2015 |
| DOIs | |
| Publication status | Published - 2015 |
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Keywords
- hyperspectral image
- superpixel
- multiple kernels
- spectral-spatial image classification
- support vector machines
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Classification of hyperspectral images by exploiting spectral-spatial information of superpixel via multiple kernels. / Fang, Leyuan; Li, Shutao; Duan, Wuhui; Ren, Jinchang; Benediktsson, Jon Atli.
In: IEEE Transactions on Geoscience and Remote Sensing, 2015.Research output: Contribution to journal › Article
TY - JOUR
T1 - Classification of hyperspectral images by exploiting spectral-spatial information of superpixel via multiple kernels
AU - Fang, Leyuan
AU - Li, Shutao
AU - Duan, Wuhui
AU - Ren, Jinchang
AU - Benediktsson, Jon Atli
N1 - (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
PY - 2015
Y1 - 2015
N2 - For the classification of hyperspectral images (HSIs), this paper presents a novel framework to effectively utilize the spectral-spatial information of superpixels via multiple kernels, termed as superpixel-based classification via multiple kernels (SC-MK). In HSI, each superpixel can be regarded as a shape-adaptive region which consists of a number of spatial-neighboring pixels with very similar spectral characteristics. Firstly, the proposed SC-MK method adopts an over-segmentation algorithm to cluster the HSI into many superpixels. Then, three kernels are separately employed for the utilization of the spectral information as well as spatial information within and among superpixels. Finally, the three kernels are combined together and incorporated into a support vector machines classifier. Experimental results on three widely used real HSIs indicate that the proposed SC-MK approach outperforms several well-known classification methods.
AB - For the classification of hyperspectral images (HSIs), this paper presents a novel framework to effectively utilize the spectral-spatial information of superpixels via multiple kernels, termed as superpixel-based classification via multiple kernels (SC-MK). In HSI, each superpixel can be regarded as a shape-adaptive region which consists of a number of spatial-neighboring pixels with very similar spectral characteristics. Firstly, the proposed SC-MK method adopts an over-segmentation algorithm to cluster the HSI into many superpixels. Then, three kernels are separately employed for the utilization of the spectral information as well as spatial information within and among superpixels. Finally, the three kernels are combined together and incorporated into a support vector machines classifier. Experimental results on three widely used real HSIs indicate that the proposed SC-MK approach outperforms several well-known classification methods.
KW - hyperspectral image
KW - superpixel
KW - multiple kernels
KW - spectral-spatial image classification
KW - support vector machines
U2 - 10.1109/TGRS.2015.2445767
DO - 10.1109/TGRS.2015.2445767
M3 - Article
JO - IEEE Transactions on Geoscience and Remote Sensing
T2 - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
SN - 0196-2892
ER -