Linear feature detection for hyperspectral subpixel mapping

doi:10.11947/j.AGCS.2019.20180221

Abstract

Abstract: The ignorance of the spatial structure of subpixel is one of the factors that influence the accuracy of hyperspectral subpixel mapping. In order to effectively solve this problem, a subpixel mapping algorithm is proposed that based on linear feature detection in mixed pixels. Firstly, the mixed pixels of typical linear feature classes are determined by spectral unmixing. Then, based on the maximum linear index method of the complete straight-line set, the linear features of remaining mixed pixels are determined. The template matching method is used in conjunction with the pixel attraction to determine the classes of linear subpixels. Finally, the subpixel categories of the remaining mixed pixels are iteratively determined based on the linear optimization method. The experimental results of real data and simulation data show that the proposed method can effectively improve the precision of subpixel mapping.

Key words: image processing, subpixel mapping, spatial correlation, linear feature detection, template matching

CLC Number:

P237

LIU Zhaoxin, ZHAO Liaoying, LI Xiaorun, CHEN Shuhan. Linear feature detection for hyperspectral subpixel mapping[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(11): 1464-1474.

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