An Unsupervised Binary and Multiple Change Detection Approach for Hyperspectral Imagery Based on Spectral Unmixing

Abstract

Over the last decade, developments in hyperspectral sensors have caused an increase in the use of hyperspectral images (HSIs) due to fine spectral resolution, which has lead to the precise extraction and identification of the materials of the observed scene. Change detection (CD) is one of the most fundamental applications of remote sensing, which provides timely change information about Earth's surface. This article presents unsupervised binary and multiple CD approaches using bitemporal HSIs based on spectral unmixing (SU) and proposes a new formulation for CD without using threshold selection methods. SU is a powerful technique for analyzing hyperspectral data. CD, by incorporating unmixing, has the potential to obtain subpixel-level information from data. The proposed framework is organized in the context of four steps: Step 1) estimating the number of endmembers, extracting the endmember's signature, calculating abundances, and applying similarity assessment to identify corresponding endmembers; Step 2) discriminating pure changed pixels; Step 3) discriminating mixed changed pixels, and combining results by pure changed pixels to produce a binary change map (BCM); and Step 4) labeling changed pixels based on the proposed formulation and producing a multiple change map. This article presents new strategies for binary and multiple CD, which provide a better figure of merit as they do not need the threshold selection methods and are less time-consuming. The experiments on real hyperspectral datasets revealed that both CD accuracy and computational performance benefited from these strategies. Experimental results proved the high performance of the proposed approaches for detecting changes in both binary and multiple cases.