Segmentation optimization algorithm based on the fusion information of the boundaries of a high-resolution remote sensing image

Chu Sensen; Hong Liang; Chen Jie; Deng Min; Yang Kun; Liu Chun

doi:10.11834/jig.20160814

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Segmentation optimization algorithm based on the fusion information of the boundaries of a high-resolution remote sensing image
Vol. 21, Issue 8, Pages: 1096-1105(2016)
Published Online：28 July 2016，

Published：2016
DOI： 10.11834/jig.20160814
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DOI：

Chu Sensen, Hong Liang, Chen Jie, Deng Min, Yang Kun, Liu Chun. Segmentation optimization algorithm based on the fusion information of the boundaries of a high-resolution remote sensing image[J]. Journal of Image and Graphics, 2016, 21(8): 1096-1105. DOI： 10.11834/jig.20160814.

摘要

针对目前区域分割算法获取的区域边界与真实地物边界不一致问题，利用高分辨率遥感影像地物内具有均质性和地物间边缘信息突出的特点，提出一种融合边界信息的高分辨率遥感影像分割优化算法。首先采用Canny算法对遥感影像进行边缘提取并进行边缘连接处理，产生闭合边界；然后将边界与初始分割结果进行融合处理，获得新的分割结果；最后在闭合边界约束下，基于灰度相似性准则对新的分割结果进行区域合并，获得优化后的最终分割结果。采用本文提出的分割优化算法对Mean Shift算法和eCognition软件获得的分割结果进行优化处理，优化后的分割结果与初始分割结果相比正确分割率（RR）平均提高了4%，验证了本文算法的有效性。该优化算法适用性广，可优化基于区域、基于边界和基于聚类等多种分割方法，同时该算法既能保持高分辨率遥感影像分割的区域完整性，又能保持地物边缘细节特征，提高了分割精度。

Abstract

To overcome nonconformity between the boundaries of a region using a segmentation algorithm and real object boundary

a segmentation optimization algorithm based on the fusion information of boundaries is presented in this study using the homogeneity characteristic of the objects and the salient boundary information in a high-resolution remote-sensing image. First

the canny edge filter was applied to extract edge information for remote-sensing images

and then the edge discontinuity problem was addressed to obtain the closed boundary. Second

the boundary and the initial segmentation results were fused

and new segmentation results were obtained. Finally

the new segmentation results under the closed boundary constraint were merged to obtain the optimization segmentation results based on the gray similarity criterion. This study presents a segmentation algorithm used to optimize the segmentation results obtained via MeanShift and eCognition. Furthermore

the rightly-segmented ratio (RR) is increased by 4% compared with the initial results

thereby verifying the effectiveness of the optimized algorithm. The optimized algorithm has extensive applicability and can optimize methods based on regions

boundaries

and clustering. This algorithm cannot only maintain the regional integrity of the object in high-resolution remote sensing

but can also keep the details of the object edge

thereby improving segmentation accuracy.