Linear spectral unmixing using endmember coexistence rules and spatial correlation. Issue 11 (3rd June 2018)

• Record Type: Journal Article
• Authors: Ma, Tianxiao; Li, Runkui; Svenning, Jens-Christian; Song, Xianfeng
• Is Part Of: International journal of remote sensing. Volume 39:Issue 11(2018)
• Page Start: 3512
• Page End: 3536
• Publication Date: 2018-06-03
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2018.1444288
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Mixed pixels are often formed when surface materials are smaller than the spatial resolution of a sensor, or two or more ground features fall within a pixel. Spectral unmixing, decomposing a mixed pixel into a set of endmembers and their corresponding abundance fractions, is an important method for extracting the underlying spectral and spatial information from remote sensing images. Recent studies have shown that it is difficult to increase the accuracy of unmixing using single pixel processing. Here, we suggest combining information on the fundamental interrelations of ground components and a priori knowledge on how ground components co-exist or exclude each other according to general geographic and geomorphic relations with spectral information may allow improved unmixing. Therefore, we propose a novel spectral unmixing method to estimate endmember abundances based on linear spectral mixing model with endmember coexistence rules and spatial correlation (LSMM-R&C). This method was implemented by incorporating endmember coexistence rules along with spatial correlation into a weighted least square method. Experiments with both synthetic and real satellite images were carried out to verify the proposed method, and its performance was also evaluated in comparison to the commonly used LSMM (linear spectral mixture method), LAU (local adaptive unmixing), ISU (iterative spectral unmixing) and ISMA (iterative spectral mixture analysis) methods. LSMM-R&C showed the smallest error, and was more effective at revealing the detailed spatial distribution of endmembers' abundance, showing high potential for solving the problem of spatial heterogeneity among neighbouring pixels.