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2018-12-21

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2019-05-06

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高分辨率遥感影像的多尺度分割优化算法研究
摘要:

遥感影像分割是面向对象影像分析方法的关键步骤,分割质量直接影响面向对象影像分析的分类精度,目前所有分割方法都很难让分割结果同时达到全局和局部最优。本文针对上述问题,提出一种新的高分辨率遥感影像多尺度分割优化算法。该算法主要包括:(1)采用局部方差准则获得多尺度分割的全局最优分割尺度;(2)在全局最优分割尺度的分割结果基础上,采用对象空间统计指数(局部莫兰指数和对象方差)检测过分割和欠分割对象;(3)再分别对过分割和欠分割对象进行优化处理,过分割对象根据空间异质性准则进行合并,欠分割对象通过融合对象内局部边界信息进行再分割。(4)最终将欠分割和过分割对象的优化分割结果与全局最优分割尺度的分割结果进行融合,获到最终优化后的分割结果。本文采用二个QuickBird高分辨率遥感影像(中国东莞和美国佛罗里达州)来验证了该算法的有效性,并对优化前和优化后的分割结果进行了定性和定量分析。实验结果表明:(1)从视觉效果来看,优化后的分割结果具有更准确的分割边界,大尺度的地物保持较好的区域性,小尺度的地物保持了更多细节;(2)从定量评价指标(RR、RI和ARI)分析:在实验一中,该算法比全局最优分割尺度的RR¥RI¥ARI分别提高了2.1%,2.4%,30.2%;在实验二中,该算法比全局最优分割尺度的RR¥RI¥ARI分别提高了4.5%,2.7%,29.3%。该算法和全局最优分割尺度的分割精度都高于小尺度和大尺度分割结果。

The study of multiscale segmentation optimized algorithm based on high spatial remote sensing imagery
Abstract:

The remote sensing segmentation is key step in the OBIA(Object-based Image Analysis),and segmentation quality directly effect to classification accuracy in the OBIA. Currently, it is difficult for all segmentation algorithms to achieve global and local optimum of segmentation. In this paper, a new multi-scale segmentation optimization algorithm for high spatial resolution remote sensing image was proposed. The algorithm mainly includes following steps: (1) to obtain global optimal segmentation scale for multiscale segmentation using local variance criterion;(2) According to the global optimal segmentation results, the over-segmentation and under-segmentation objects were detected using object spatial statistic index (Local Moran’s I and object variance);(3)optimally processing the over-segmentation and under-segmentation objects respectively, which the over-segmentation objects were merged and the under-segmentation objects were Re-segmented by merging object boundary inner object.(4) the result merging was implemented based on the global optimal segmentation and the optimized over-segmentation and under-segmentation results. In this paper, the high spatial resolution remote sensing images (Dongguan, China and Florida, USA) were used to verify the effectiveness of the proposed algorithm, and the qualitative and quantitative analysis was performed between pro-optimization and post-optimization results. The experimental results are shown following as: (1) From the perspective of visual effects, the optimized segmentation results obtain more accurately segmentation boundary than the pro-optimization, and the large-scale objects maintain better regional feature, and the small-scale objects maintain more detail information.(2) From the quantitative evaluation indicators(RR, RI and ARI) analysis, the presented algorithm increased the RR,RI and ARI by 2.1%,2.4 and 30.2% in comparison with the global optimal segmentation scale in the first experiment, and by 4.5%, 2.7%, and 29.3% in the second experiment. The proposed algorithm and the global optimal segmentation scale obtained more accuracy than the small-scale and large-scale segmentation results.

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