| 高空间分辨率遥感影像最优分割结果自动确定方法EI北大核心CSCD |
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| 引用本文： | 程结海,黄中意,王建如,何湜.高空间分辨率遥感影像最优分割结果自动确定方法EI北大核心CSCD[J].测绘学报,2022,51(5):658-667. |
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| 作者姓名： | 程结海 黄中意 王建如 何湜 |
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| 作者单位： | 河南理工大学测绘与国土信息工程学院, 河南 焦作 454000 |
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| 基金项目： | 国家自然科学基金(42171299)；;河南自然科学基金(162300410122)；;河南省科技攻关项目(212102311149;222102320341)；;河南省高等学校重点科研项目(22B420004)； |
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| 摘 要： | 针对现有方法普遍存在不能充分顾及遥感影像多波段光谱信息,以及忽视遥感影像中地理要素的多尺度特性等问题,提出一种自动确定高空间分辨率遥感影像最优分割结果的非监督评价方法。该方法基于信息熵生成光谱信息离散度,利用光谱信息离散度构建能表达分割对象内部光谱均质性指标和分割对象与其相邻分割对象间光谱异质性指标。基于构建的光谱均质性和光谱异质性指标,采用“粗估计+精确定”的策略,逐步得到一个多级优化后的影像最优分割结果。本文在3个不同下垫面影像区域进行试验。结果表明,该方法能有效地实现自动确定高空间分辨率遥感影像最优分割结果,与现有方法相比,本文方法确定出的影像最优分割结果质量更高,与参考分割结果更加贴近。
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| 关 键 词： | 高分遥感影像 面向地理对象影像分析 影像最优分割 信息熵 光谱信息离散度 |
| 收稿时间： | 2021-07-30 |
| 修稿时间： | 2022-03-14 |
The automatic determination method of the optimal segmentation result of high-spatial resolution remote sensing image |
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| CHENG Jiehai,HUANG Zhongyi,WANG Jianru,HE Shi.The automatic determination method of the optimal segmentation result of high-spatial resolution remote sensing image[J].Acta Geodaetica et Cartographica Sinica,2022,51(5):658-667. |
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| Authors: | CHENG Jiehai HUANG Zhongyi WANG Jianru HE Shi |
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| Institution: | School of Surveying & Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China |
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| Abstract: | The existing methods cannot fully take into account the multi-band spectral information of remote sensing images, and ignore the multi-scale characteristics of geographical elements in remote sensing images. This study proposed an unsupervised evaluation method for automatically determining the optimal segmentation result of high-spatial resolution remote sensing image. This method generates the spectral information divergence based on information entropy, and uses the spectral information divergence to construct the indexes that can express the intra-segment homogeneity and inter-segment heterogeneity. Based on the constructed homogeneity and heterogeneity indexes, the strategy of "rough estimation + fine determination" is adopted to gradually obtain an optimal image segmentation result after multi-level optimization. The proposed method was carried out in three different underlying surface image areas. Experimental results demonstrate that the method can effectively automatically determine the optimal segmentation results of high-spatial resolution remote sensing images. Compared with existing methods, the optimal image segmentation results determined by the method have higher quality and are closer to the reference segmentation results. |
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