摘要:
目前主流的高分遥感影像(high resolution remote sensing image,HRI)分割算法在区域合并顺序的确定中很少考虑区域自身的分割质量信息.针对该问题,提出了一种启发法优化的策略,以提高农田地区HRI的分割精度.首先,提出了区域内和区域间的"均一致模型",前者是利用区域内光谱变化信息来建模的,后者则综合考虑了区域间多光谱与植被信息提取的边界强度;其次,将区域内和区域间的均一致模型合并,构建启发法的执行标准;最后,利用该标准使区域合并的搜索过程能够考虑待合并区域自身的分割质量,从而有效抑制过分割与亚分割错误.利用2景不同特点的农田地区HRI进行分割实验,并将所提出的启发法与2种新提出的分割算法进行对比分析.对分割结果的定量评价结果表明,启发法优化策略可以显著提高农田地区HRI的分割精度.
Abstract:
Many mainstream segmentation algorithms for high resolution remote sensing image ( HRI ) rarely consider the segmentation quality in their region merging process. In order to solve this problem, this paper proposed a strategy to optimize heuristics with the purpose of enhancing segmentation accuracy of HRI captured over agricultural areas. Intra- and inter- region homogeneity models were firstly proposed, with the former constructed upon within-region spectral variance, and the latter considering edge strength extracted from multi-spectral and vegetation information. The criterion of the proposed heuristics was then constructed by combining the intra- and inter- region homogeneity. The new criterion enables the merging process to take into account the segmentation quality, thus constraining over- and under- segmentation errors effectively. Two scenes of HRI acquired over agricultural areas were utilized for validation experiment, and the performance of the proposed method was compared with other two newly proposed methods. By analyzing the quantitative evaluation of the segmentation results, it is found that the proposed method can remarkably improve the segmentation accuracy of HRI in agricultural landscape.
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