高空间分辨率遥感影像最优分割结果自动确定方法

doi:10.11947/j.AGCS.2022.20210423

测绘学报 ›› 2022, Vol. 51 ›› Issue (5): 658-667.doi: 10.11947/j.AGCS.2022.20210423

高空间分辨率遥感影像最优分割结果自动确定方法

程结海, 黄中意, 王建如, 何湜

河南理工大学测绘与国土信息工程学院, 河南焦作 454000

收稿日期:2021-07-30 修回日期:2022-03-14 出版日期:2022-05-20 发布日期:2022-05-28
作者简介:程结海(1980-),男,博士,副教授,主要研究方向为高分遥感影像分析与处理。E-mail:chengjiehai@hpu.edu.cn
基金资助:
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）；河南省高校基本科研业务费专项资金资助（NSFRF210401）

The automatic determination method of the optimal segmentation result of high-spatial resolution remote sensing image

CHENG Jiehai, HUANG Zhongyi, WANG Jianru, HE Shi

School of Surveying & Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China

Received:2021-07-30 Revised:2022-03-14 Online:2022-05-20 Published:2022-05-28
Supported by:
The National Natural Science Foundation of China (No. 42171299);The Natural Science Foundation of Henan (No. 162300410122);The Science and Technology Research Project of Henan (Nos. 212102311149;222102320341);The Key Scientific Research Project of Colleges and Universities of Henan (No. 22B420004);The Fundamental Research Fund for the Universities of Henan (No. NSFRF210401)

摘要/Abstract

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。

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

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.

Key words: high spatial resolution remote sensing image, geographic object-based image analysis, optimal image segmentation, information entropy, spectral information divergence

中图分类号:

P237

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[J]. 测绘学报, 2022, 51(5): 658-667.

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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高空间分辨率遥感影像最优分割结果自动确定方法

The automatic determination method of the optimal segmentation result of high-spatial resolution remote sensing image

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