Multi-scale superpixel spectral–spatial classification of hyperspectral images. Issue 20 (17th October 2016)

• Record Type: Journal Article
• Authors: Li, Shanshan; Ni, Li; Jia, Xiuping; Gao, Lianru; Zhang, Bing; Peng, Man
• Is Part Of: International journal of remote sensing. Volume 37:Issue 20(2016)
• Page Start: 4905
• Page End: 4922
• Publication Date: 2016-10-17
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2016.1225175
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: A novel multi-scale superpixel-based spectral–spatial classification (MS-SSC) approach is proposed for hyperspectral images in this study. Superpixels are considered as the basic processing units for spectral–spatial-based classification. The use of multiple scales allows the capturing of local spatial structures of various sizes. The proposed technique consists of three steps. In the first step, hierarchical superpixel segmentations are performed from fine to coarse scales for the original hyperspectral image and the spectral information of each superpixel is used for classification at each scale. In the second step, each single scale superpixel-based classification is improved by combining with the segmentations at a higher level. Finally, the multi-scale classification is achieved via decision fusion. Experimental results are presented for two hyperspectral images and compared with recently advanced pixel-wise and pixel-based spectral–spatial classification approaches. The experiments demonstrate that the proposed method works effectively on the homogeneous regions and is also able to preserve the small local spatial structures in the image.