Work

Spatial-Spectral Hyperspectral Endmember Extraction Using a Spatial Energy Prior Constrained Maximum Simplex Volume Approach

Year: 2020

Type: article

Abstract: Endmember extraction algorithms (EEAs) are among the most commonly discussed types of hyperspectral image processing in the past three decades. This article proposes a spatial energy prior constrained maximum simplex volume (SENMAV) approach for spatial-spectral endmember extraction of hyperspectral images. SENMAV investigates the spatial information from the perspective of the spatial energy prior of a Markov random field (MRF), which is used as a regularization term of the traditional maximum volume simplex model to simultaneously constrain the selection of the endmembers in both the spatial and spectral viewpoints. This article sheds new light on spatial-spectral-based EEAs, as SENMAV well balances the tradeoff between endmember extraction accuracy and spatial attribute requirements of endmembers. Based on the spectral angle distance and root-mean-square error, experimental results on both synthetic and real hyperspectral datasets indicate that the proposed approach significantly improves the endmember extraction performance over current state-of-the-art spatial-spectral-based EEAs.

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI: 10.1109/jstars.2020.2981402

Authors: Xiangfei Shen, Wenxing Bao, Kewen Qu

Institutions: North Minzu University, Hefei University of Technology

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,750

Funders: National Natural Science Foundation of China, North Minzu University