联合像素级和对象级分析的遥感影像变化检测

冯文卿; 眭海刚; 涂继辉; 孙开敏

doi:10.11947/j.AGCS.2017.20160606

测绘学报 >

2017 , Vol. 46 >Issue 9: 1147 - 1155

DOI: https://doi.org/10.11947/j.AGCS.2017.20160606

摄影测量学与遥感

联合像素级和对象级分析的遥感影像变化检测

冯文卿 ,
眭海刚 ,
涂继辉 ,
孙开敏

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武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079

冯文卿(1991-),男,博士生,研究方向为高分辨率遥感影像分类及变化检测。E-mail,wq_feng@whu.edu.cn

收稿日期: 2016-11-28

修回日期: 2017-05-24

网络出版日期: 2017-10-12

基金资助

国家重点研发计划（2016YFB0502600）；测绘遥感信息工程国家重点实验室开放基金（16E01）；国家自然科学基金（41471354）

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Remote Sensing Image Change Detection Based on the Combination of Pixel-level and Object-level Analysis

FENG Wenqing ,
SUI Haigang ,
TU Jihui ,
SUN Kaimin

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State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Received date: 2016-11-28

Revised date: 2017-05-24

Online published: 2017-10-12

Supported by

The National Key Research and Development Program of China (No. 2016YFB0502600);The Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (No. 16E01);The National Natural Science Foundation of China(No. 41471354)

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摘要

为改善高空间分辨率遥感影像的变化检测精度，提出一种联合像素级和对象级分析的变化检测新框架。首先将多时相影像进行叠合，对叠加影像进行主成分分析，并利用基于熵率的方法对第一主成分影像进行分割，通过改变超像素数目来获取多层次不同尺寸大小的超像素区域。同时，对多时相影像进行光谱差异和纹理差异分析，采用自适应PCNN神经网络方法进行图像融合，利用水平集（CV）方法对融合后的影像进行分割获取像素级变化检测结果。最后，结合多尺度区域标记矩阵对检测结果进行变化强度等级量化和决策级融合，作为变化检测的后处理部分，以获取最终的对象级变化检测结果。采用SPOT-5多光谱影像进行试验。结果表明这种新框架可以有效集成基于像素和基于对象两种图像分析方法的优势，能够进一步提高变化检测过程的稳定性和适用性。

关键词： 像素级; 对象级; 变化检测; 超像素; PCNN神经网络; 决策级融合

本文引用格式

冯文卿 , 眭海刚 , 涂继辉 , 孙开敏 . 联合像素级和对象级分析的遥感影像变化检测[J]. 测绘学报, 2017 , 46(9) : 1147 -1155 . DOI: 10.11947/j.AGCS.2017.20160606

Abstract

In order to improve the change detection accuracy of the high resolution remote sensing image, a novel framework based on the combination of pixel-level and object-level analysis is proposed. Firstly, the two temporal images are superimposed, and the principal component analysis is performed. Then, it is utilized that the entropy rate segmentation algorithm to segment the first principal component image by changing the number of super-pixels to obtain the multi-layer super-pixel regions with different sizes. At the same time, by analyzing the difference of spectral feature and texture feature on two temporal images, it is used that adaptive PCNN neural network algorithm to make a fusion of the two difference images. Afterwards, the level set (CV) method is used to get the pixel-level change detection results. At last, the change intensity level quantization and decision level fusion are used on the initial change detection results with the region labeling matrix, serving as the post-processing part to obtain the changed objects. Experimental results on the sets of SPOT-5 multi-spectral images show that the new framework can effectively integrate the advantages of pixel-based and object-based image analysis methods, which can further improve the stability and applicability of the change detection process.

Key words： pixel-level; object-level; change detection; super-pixel; PCNN neural network; decision level fusion

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