Seamline Determination Based on PKGC Segmentation for Remote Sensing Image Mosaicking
AbstractThis paper presents a novel method of seamline determination for remote sensing image mosaicking. A two-level optimization strategy is applied to determine the seamline. Object-level optimization is executed firstly. Background regions (BRs) and obvious regions (ORs) are extracted based on the results of parametric kernel graph cuts (PKGC) segmentation. The global cost map which consists of color difference, a multi-scale morphological gradient (MSMG) constraint, and texture difference is weighted by BRs. Finally, the seamline is determined in the weighted cost from the start point to the end point. Dijkstra’s shortest path algorithm is adopted for pixel-level optimization to determine the positions of seamline. Meanwhile, a new seamline optimization strategy is proposed for image mosaicking with multi-image overlapping regions. The experimental results show the better performance than the conventional method based on mean-shift segmentation. Seamlines based on the proposed method bypass the obvious objects and take less time in execution. This new method is efficient and superior for seamline determination in remote sensing image mosaicking. View Full-Text
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Dong, Q.; Liu, J. Seamline Determination Based on PKGC Segmentation for Remote Sensing Image Mosaicking. Sensors 2017, 17, 1721.
Dong Q, Liu J. Seamline Determination Based on PKGC Segmentation for Remote Sensing Image Mosaicking. Sensors. 2017; 17(8):1721.Chicago/Turabian Style
Dong, Qiang; Liu, Jinghong. 2017. "Seamline Determination Based on PKGC Segmentation for Remote Sensing Image Mosaicking." Sensors 17, no. 8: 1721.