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遥感影像融合的自适应变化检测
引用本文:魏立飞,钟燕飞,张良培,李平湘.遥感影像融合的自适应变化检测[J].遥感学报,2010,14(6):1204-1218.
作者姓名:魏立飞  钟燕飞  张良培  李平湘
作者单位:武汉大学测绘遥感信息工程国家重点实验室,湖北,武汉,430079
基金项目:国家重点基础研究发展计划(编号: 2009CB723905); 国家高技术研究发展计划(863)(编号: 2009AA12Z114), 教育部博士点新教 师基金(编号: 200804861058); 教育部新世纪优秀人才支持计划(编号: NECT-10-0624); 湖北省自然科学基金(编号: 2009CDB173)。
摘    要:提出一种基于影像融合和自适应阈值选择的遥感影像变化检测方法。首先利用经过改进的融合技术对原 始数据的差值影像和比值影像进行处理, 构造融合影像, 在该融合影像的基础上进行自适应迭代运算得到初步变 化阈值范围, 然后通过分析阈值范围两侧影像像元的离散程度, 求解最终的阈值范围, 从而得到更优变化阈值, 提 取变化区域。实验结果表明, 本文方法的检测精度优于传统的变化检测方法, 同时具有一定的稳定性和智能性。

关 键 词:变化检测    影像融合    自适应选择    阈值范围
收稿时间:2009/10/29 0:00:00
修稿时间:2010/5/24 0:00:00

Adaptive change method of remote sensing image fusion
WEI Lifei,ZHONG Yanfei,ZHANG Liangpei and LI Pingxiang.Adaptive change method of remote sensing image fusion[J].Journal of Remote Sensing,2010,14(6):1204-1218.
Authors:WEI Lifei  ZHONG Yanfei  ZHANG Liangpei and LI Pingxiang
Institution:National Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hubei Wuhan 430079, China;National Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hubei Wuhan 430079, China;National Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hubei Wuhan 430079, China;National Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hubei Wuhan 430079, China
Abstract:This paper proposes a change detection algorithm for remote sensing images based on image fusion and adaptive threshold selection. An improved image fusion technology has been employed in processing difference image and ratio image of original data in order to construct the fusion images. Based on these images, a coarse range of change threshold has been got from an adaptive iterative operation. Then, after analyzing the discrete levels of the image pixels distributed on both sides of the threshold range, the final threshold range has been achieved. Thus much more optimal change threshold helps to extract the final change region. the experimental results in the paper suggest that the detection accuracy of this method, which has certain stability and intelligence outperform the traditional change detection methods.
Keywords:change detection  image fusion  adaptive selection  threshold range
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