Spectral clustering with anchor graph based on set-to-set distances for large-scale hyperspectral images. Issue 7 (3rd April 2022)

• Record Type: Journal Article
• Authors: Qin, Yao; Quan, Sinong; Wei, Chongyang; Ni, Weiping; Li, Kun; Dong, Xiaohu; Ye, Yuanxin
• Is Part Of: International journal of remote sensing. Volume 43:Issue 7(2022)
• Page Start: 2438
• Page End: 2460
• Publication Date: 2022-04-03
• Subjects: Hyperspectral image -- fast spectral clustering -- set-to-set distance -- local covariance matrix representation -- anchor graph; Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2022.2061317
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Since labelled samples of hyperspectral images (HSIs) may be unavailable in practical remote sensing applications, large-scale HSI clustering is very important. Due to the huge amount of data brought by the rich spectral and spatial information in large-scale HSIs, HSI clustering is still a challenging task. Among the methods designed for large-scale HSIs clustering, the anchor graph-based methods simultaneously inherit the merits of graph-based clustering and reduce the computational complexity by introducing anchor samples to graph construction. However, the affinity matrix computed by inaccurate distances between anchor samples and other HSI samples can hardly obtain satisfactory clustering performance. To solve this problem, we propose a novel approach for large-scale HSI clustering, namely, spectral clustering with anchor graph based on set-to-set distances (SCAG-SSD) derived from local covariance matrix representation (LCMR). First, superpixels and LCMR features of HSI are obtained via the entropy rate superpixel algorithm and maximum noise fraction, respectively. Second, pure and anomalous samples of each superpixel are distinguished via the distances of LCMR features, and then anchor samples are selected via statistics of the distances between pure samples in each superpixel. In this way, selected anchor samples are representative enough to link all the HSI samples. Third, pure samples in each superpixel, anomalous and anchor samples with their corresponding nearest neighbouring samples are all regarded as different sets. The set-to-set distance is achieved by weighting the LCMR-based distances between samples of two sets. Finally, fast anchor graph clustering is conducted based on set-to-set distances to obtain final clustering maps. Extensive experiments conducted on three publicly available benchmark HSIs demonstrate that the proposed method achieves state-of-the-art clustering accuracy with comparable efficiency.