Rotation and scale invariant shape context registration for remote sensing images with background variations

Jie Jiang; Shumei Zhang; Shixiang Cao

doi:10.1117/1.JRS.9.095092

27 February 2015 Rotation and scale invariant shape context registration for remote sensing images with background variations

Jie Jiang, Shumei Zhang, Shixiang Cao

Author Affiliations +

Funded by: Natural Science Foundation of China, National Natural Science Fund, National Natural Science Fund of China, Specialized Research Fund for the Doctoral Program of Higher Education of China, Doctoral Program of Higher Education, Specialized Research Fund for the Doctoral Program of Higher Education

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 095092 (February 2015). https://doi.org/10.1117/1.JRS.9.095092

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.

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Jie Jiang, Shumei Zhang, and Shixiang Cao "Rotation and scale invariant shape context registration for remote sensing images with background variations," Journal of Applied Remote Sensing 9(1), 095092 (27 February 2015). https://doi.org/10.1117/1.JRS.9.095092

Published: 27 February 2015

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