| 使用多尺度模糊融合的高分影像变化检测 |
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| 引用本文： | 张慧芳,张鹏林,晁剑.使用多尺度模糊融合的高分影像变化检测[J].武汉大学学报(信息科学版),2022,47(2):296-303. |
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| 作者姓名： | 张慧芳 张鹏林 晁剑 |
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| 作者单位： | 1.武汉大学测绘遥感信息工程学院，湖北 武汉，430079 |
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| 基金项目： | 自然资源部城市国土资源监测与仿真重点实验室开放基金KF-2019-04-046 |
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| 摘 要： | 为了提高高分辨率遥感影像变化检测的可靠性,提出了一种基于模糊综合评判的遥感影像变化检测方法.首先对两个时相的影像进行波段叠加,对多波段新影像进行多尺度分割;然后针对单一尺度上的对象,综合考虑两时相遥感影像对象的光谱特征和纹理特征,建立模糊综合评判模型,对各个对象内的像素是否发生变化进行隶属度计算;最后采用熵权法对影像各...
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| 关 键 词： | 多尺度 变化检测 模糊综合评判 熵权法 |
| 收稿时间： | 2021-10-12 |
Change Detection by Multi-scale Fuzzy Fusion on High Resolution Images |
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| Institution: | 1.School of Remote Sensing and Information Engnieering, Wuhan University, Wuhan 430079, China2.Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resource, Shenzhen 518000, China3.Geomatics Technology and Application Key Laboratory of Qinghai Province, Xining 810001, China |
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| Abstract: | Objectives With the development of remote sensing technology, the spatial resolution of remote sensing image keeps improving, which brings both opportunities and challenges for the traditional remote sensing image classification and change detection.In order to improve the reliability of change detection of high resolution remote sensing image, this paper proposes a method of change detection of remote sensing image based on fuzzy comprehensive evaluation. Methods Firstly, images of two-phases were overlapped to a new image which can be segmented at multi-scale. Secondly, a fuzzy comprehensive evaluation model was established for objects in certain scale to calculate membership of pixels in each object, which took the spectral and texture characteristics of two-phase remote sensing image objects into comprehensive consideration. Finally, the entropy method was used to fuse the fuzzy evaluation membership degree of each pixel in different scales based on information entropy. Results Taking two groups of high resolution images with different phases as examples, we realized the effective fusion of multi-scale change detection based on fuzzy logic, which made full use of multi-level pixel features and consequently improved the overall effect of single scale object-oriented change detection. Conclusions The proposed method provides a new idea for the exploration of multi-scale change detection. |
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