Nonlinear shape prior-based object extraction in high resolution remote sensing images

doi:10.7523/j.issn.2095-6134.2014.05.013

Abstract

Abstract:

In this study,given shape templates of some object,we model them by using kernel principal component analysis and then propose a new object extraction method for high resolution remote sensing images, which integrates shape prior and several image appearance information,including edge,color, and shadow. Based on active contour model,a new energy function is constructed and minimized through an iterative global optimization method to get the accurate segmentation results. Experimental results show that our method has high efficiency, high accuracy, and the robustness with respect to various background disturbances.

Key words: object extraction, shape prior, active contour model, global optimization

CLC Number:

TP751.1

LIU Ge, HU Yanfeng, SUN Xian, WANG Hongqi. Nonlinear shape prior-based object extraction in high resolution remote sensing images[J]. , 2014, 31(5): 671-677.

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