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Journal of Applied Remote Sensing

Rotation and scale invariant shape context registration for remote sensing images with background variations
Author(s): Jie Jiang; Shumei Zhang; Shixiang Cao
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Paper Abstract

Multitemporal remote sensing images generally suffer from background variations, which significantly disrupt traditional region feature and descriptor abstracts, especially between pre and postdisasters, making registration by local features unreliable. Because shapes hold relatively stable information, a rotation and scale invariant shape context based on multiscale edge features is proposed. A multiscale morphological operator is adapted to detect edges of shapes, and an equivalent difference of Gaussian scale space is built to detect local scale invariant feature points along the detected edges. Then, a rotation invariant shape context with improved distance discrimination serves as a feature descriptor. For a distance shape context, a self-adaptive threshold (SAT) distance division coordinate system is proposed, which improves the discriminative property of the feature descriptor in mid-long pixel distances from the central point while maintaining it in shorter ones. To achieve rotation invariance, the magnitude of Fourier transform in one-dimension is applied to calculate angle shape context. Finally, the residual error is evaluated after obtaining thin-plate spline transformation between reference and sensed images. Experimental results demonstrate the robustness, efficiency, and accuracy of this automatic algorithm.

Paper Details

Date Published: 27 February 2015
PDF: 20 pages
J. Appl. Remote Sens. 9(1) 095092 doi: 10.1117/1.JRS.9.095092
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Jie Jiang, BeiHang Univ. (China)
Shumei Zhang, BeiHang Univ. (China)
Shixiang Cao, BeiHang Univ. (China)


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