Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image

TL;DR

This paper introduces SSCAG, an unsupervised clustering method for hyperspectral images that effectively handles high dimensionality and spatial structures using anchor graphs and a new similarity metric.

Contribution

The paper presents a novel spatial-spectral clustering approach with anchor graphs, reducing complexity and embedding spatial information for hyperspectral image clustering.

Findings

Outperforms state-of-the-art methods on three datasets

Effectively exploits spatial-spectral information

Reduces computational complexity with anchor graphs

Abstract

Hyperspectral image (HSI) clustering, which aims at dividing hyperspectral pixels into clusters, has drawn significant attention in practical applications. Recently, many graph-based clustering methods, which construct an adjacent graph to model the data relationship, have shown dominant performance. However, the high dimensionality of HSI data makes it hard to construct the pairwise adjacent graph. Besides, abundant spatial structures are often overlooked during the clustering procedure. In order to better handle the high dimensionality problem and preserve the spatial structures, this paper proposes a novel unsupervised approach called spatial-spectral clustering with anchor graph (SSCAG) for HSI data clustering. The SSCAG has the following contributions: 1) the anchor graph-based strategy is used to construct a tractable large graph for HSI data, which effectively exploits all data points and reduces the computational complexity; 2) a new similarity metric is presented to embed the spatial-spectral information into the combined adjacent graph, which can mine the intrinsic property structure of HSI data; 3) an effective neighbors assignment strategy is adopted in the optimization, which performs the singular value decomposition (SVD) on the adjacent graph to get solutions efficiently. Extensive experiments on three public HSI datasets show that the proposed SSCAG is competitive against the state-of-the-art approaches.