Segmentation of large scale remote sensing image based on Mean Shift
Shicai ZHU1, Xiaotong ZHAI2, Zongwei WANG1()
1. Jiangsu Province Surveying and Mapping Engineering Institute, Nanjing 210013, China 2. Jiangsu Province Archives of Surveying and Mapping Production, Nanjing 210013, China
Mean Shift algorithm has been widely used in image segmentation because of its fast convergence speed and good segmentation accuracy. However, when large scale remote sensing images are processed, Mean Shift algorithm has some problems, such as slow speed and low efficiency. In this paper, a parallel seamless segmentation algorithm based on Mean Shift is proposed. The algorithm is based on block parallel Mean Shift segmentation. The elimination criterion of block lines is determined by uniform coding of label images and establishing corresponding relations between label values of overlapping regions. Then, the row and column directions of the label image are stitched together. Finally, the segmented label image is vectorized to generate the final segmentation result. Compared with the original Mean Shift algorithm, the algorithm put forward in this paper can not only ensure the reliability of segmentation results but also greatly improve the efficiency of image segmentation, and can also solve the problem of large scale remote sensing image segmentation.
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