Hyperspectral Pixel Unmixing Combined with the Compressive Sensing and the Universal Gravitation Model

doi:10.13485/j.cnki.11-2089.2014.0171

Abstract

Abstract:

Hyperspectral imagery has the characteristic of high spectral resolution, but the low spatial resolution makes the mixed pixels exist ubiquitously in them. Pixel unmixing is the

?important content in the field of hyperspectral remote sensing application, including abundance extracting and abundance inversing. Based on the compressive sensing (CS)

theory, combined with neural network technology, a novel hyperspectral endmember extracting model based on the compressive sensing theory is proposed. After that,

applied the classic Universal Gravitation Model (UGM) into abundance inversing, an abundance inversing algorithm based on the universal gravitation model is put forward.

Finally, the model and the algorithm are realized in MATLAB with the Hyperion hyperspectral image and the accuracy of the endmember is assessed and analyzed

according to the results. Experimental results show that the proposed extracting model and inversing algorithm have a certain degree of feasibility in both theory and practice,

at the same time the computational accuracy meets the requirements.

Key words: compressive sensing, universal gravitation model, pixel unmixing, endmember extracting, abundance inversing

CLC Number:

TP 7

YANG Keming WANG Linwei LIU Shiwen LIU Fei SHI Gangqiang ZHAO Siliang. Hyperspectral Pixel Unmixing Combined with the Compressive Sensing and the Universal Gravitation Model[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(10): 1068-1074.

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