For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Extreme Learning Machine with Superpixel-Guided Composite Kernels for SAR Image Classification
Dongdong GUAN Xiaoan TANG Li WANG Junda ZHANG
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2018/06/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Pattern Recognition
extreme learning machine (ELM), superpixel, composite kernels (CK), SAR image classification,
Full Text: PDF(809.2KB)>>
Synthetic aperture radar (SAR) image classification is a popular yet challenging research topic in the field of SAR image interpretation. This paper presents a new classification method based on extreme learning machine (ELM) and the superpixel-guided composite kernels (SGCK). By introducing the generalized likelihood ratio (GLR) similarity, a modified simple linear iterative clustering (SLIC) algorithm is firstly developed to generate superpixel for SAR image. Instead of using a fixed-size region, the shape-adaptive superpixel is used to exploit the spatial information, which is effective to classify the pixels in the detailed and near-edge regions. Following the framework of composite kernels, the SGCK is constructed base on the spatial information and backscatter intensity information. Finally, the SGCK is incorporated an ELM classifier. Experimental results on both simulated SAR image and real SAR image demonstrate that the proposed framework is superior to some traditional classification methods.