Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method
Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method
复制标题
考虑分段内和分段间异质性的区域合并：一种改进的混合遥感图像分割方法
DOI：
10.3390/rs10050781
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发表时间：
2018-05
期刊：
影响因子：
5
通讯作者：
Liu Ying
中科院分区：
文献类型：
--
作者：
Wang Yongji;Qi Qingwen;Meng Qingyan;Yang Jian;Liu Ying
图像分割是基于对象的图像分析的一个重要过程和前提，但将图像分割成有意义的地质对象是一个具有挑战性的问题。最近，一些学者集中在混合方法，采用初始分割和随后的区域合并，因为混合方法同时考虑边界和空间信息。然而，现有的合并准则（MC）只考虑相邻段之间的异质性来计算相邻段的合并代价，从而限制了段与地理对象之间的拟合优度，因为段内的同质性和段间的异质性应该被同等对待。为了克服这一局限性，本文采用了一种混合的遥感图像分割方法，该方法在区域合并过程中考虑了MC的客观异质性和相对同质性（OHRH）。在本文中，OHRH方法在五个不同的研究区域中实施，然后与我们使用客观异质性（OH）方法以及完整λ调度算法（FLSA）的区域合并方法进行比较。非监督评价结果表明，OHRH方法比OH和FLSA方法更准确，视觉评价结果表明，OHRH方法能够区分大小地物。段显示出更大的尺寸变化比其他方法，证明了OHRH方法中考虑段内和段间异质性的优越性。
Image segmentation is an important process and a prerequisite for object-based image analysis, but segmenting an image into meaningful geo-objects is a challenging problem. Recently, some scholars have focused on hybrid methods that employ initial segmentation and subsequent region merging since hybrid methods consider both boundary and spatial information. However, the existing merging criteria (MC) only consider the heterogeneity between adjacent segments to calculate the merging cost of adjacent segments, thus limiting the goodness-of-fit between segments and geo-objects because the homogeneity within segments and the heterogeneity between segments should be treated equally. To overcome this limitation, in this paper a hybrid remote-sensing image segmentation method is employed that considers the objective heterogeneity and relative homogeneity (OHRH) for MC during region merging. In this paper, the OHRH method is implemented in five different study areas and then compared to our region merging method using the objective heterogeneity (OH) method, as well as the full lambda-schedule algorithm (FLSA). The unsupervised evaluation indicated that the OHRH method was more accurate than the OH and FLSA methods, and the visual results showed that the OHRH method could distinguish both small and large geo-objects. The segments showed greater size changes than those of the other methods, demonstrating the superiority of considering within- and between-segment heterogeneity in the OHRH method.
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影响因子：
10.6
作者：
Cheng, Bin;Yang, Jianchao;Huang, Thomas S.
通讯作者：
Huang, Thomas S.
DOI：
10.1016/j.isprsjprs.2014.04.008
发表时间：
2014-08
影响因子：
12.7
作者：
Jian Yang;Peijun Li;Yuhong He
通讯作者：
Jian Yang;Peijun Li;Yuhong He
DOI：
10.1016/j.compeleceng.2015.09.013
发表时间：
2016-08
期刊：
Comput. Electr. Eng.
影响因子：
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作者：
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通讯作者：
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DOI：
10.3233/fi-2000-411207
发表时间：
2000
期刊：
Fundam. Informaticae
影响因子：
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作者：
J. Roerdink;Arnold Meijster
通讯作者：
J. Roerdink;Arnold Meijster
影响因子：
3.6
作者：
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通讯作者：
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