Hyperspectral image classification based on adaptive‐weighted LLE and clustering‐based FSVMs. Issue 6 (1st June 2018)

• Record Type: Journal Article
• Authors: Ge, Haimiao; Wang, Liguo; Liu, Yanzhong; Li, Cheng; Chen, Ruixin
• Is Part Of: IET image processing. Volume 12:Issue 6(2018)
• Page Start: 941
• Page End: 947
• Publication Date: 2018-06-01
• Subjects: hyperspectral imaging -- image classification -- support vector machines -- fuzzy set theory -- feature extraction; hyperspectral image classification -- adaptive‐weighted LLE -- clustering‐based FSVM -- supervised locally linear embedding -- supervised feature extraction algorithm; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0987
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : An improved version of supervised locally linear embedding is proposed. In this algorithm, the weight factors of the supervised method are adaptively achieved. This method can simplify the supervised feature extraction algorithm by reducing parameters. To improve classification accuracy, a clustering‐based fuzzy support vector machine (FSVM) is proposed. Different from traditional FSVMs, the proposed method constructs the fuzzy weights by inner‐class clusters. In the proposed method, loose density is defined to express the compactness of the inner‐class clusters. The proposed algorithm can restrain the noise and outliers by exploiting the method of endowing with smaller weight for big loose density and bigger weight for the small loose density of samples in the clusters. To inspect the performance of the proposed methods, we conduct experiments on two hyper‐spectral images. Results show that the two methods are competitive among the competitors.