Novel method for hyperspectral unmixing: fuzzy c-means unmixing

Abstract

Purpose This paper aims to effectively achieve endmembers and relative abundances simultaneously in hyperspectral image unmixing yield. Hyperspectral unmixing, which is an important step before image classification and recognition, is a challenging issue because of the limited resolution of image sensors and the complex diversity of nature. Unmixing can be performed using different methods, such as blind source separation and semi-supervised spectral unmixing. However, these methods have disadvantages such as inaccurate results or the need for the spectral library to be known a priori. Design/methodology/approach This paper proposes a novel method for hyperspectral unmixing called fuzzy c-means unmixing, which achieves endmembers and relative abundance through repeated iteration analysis at the same time. Findings Experimental results demonstrate that the proposed method can effectively implement hyperspectral unmixing with high accuracy. Originality/value The proposed method present an effective framework for the challenging field of hyperspectral image unmixing.