联合概率密度空间的遥感自适应变化检测方法

doi:10.11947/j.AGCS.2016.20140484

测绘学报 ›› 2016, Vol. 45 ›› Issue (1): 73-79.doi: 10.11947/j.AGCS.2016.20140484

联合概率密度空间的遥感自适应变化检测方法

吴炜¹, 沈占锋², 吴田军³, 王卫红¹

1. 浙江工业大学计算机学院, 浙江杭州 310023;
2. 中国科学院遥感与数字地球研究所, 北京 100101;
3. 长安大学理学院, 陕西西安 710064

收稿日期:2014-09-17 修回日期:2015-09-13 出版日期:2016-01-20 发布日期:2016-01-28
通讯作者: 王卫红, wwh@zjut.edu.cn E-mail:wwh@zjut.edu.cn
作者简介:吴炜(1985—),男,博士,讲师,研究方向为遥感信息处理与分析。
基金资助:
国家自然科学基金(41301473); 国家科技重大专项(03-Y30B06-9001-13/15-01);浙江省自然科学基金(LZ14F020001)

Joint Probability Space Based Self-adaptive Remote Sensing Change Detection Method

WU Wei¹, SHEN Zhanfeng², WU Tianjun³, WANG Weihong¹

1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China;
2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;
3. College of Science, Chang'an University, Xi'an 710064, China

Received:2014-09-17 Revised:2015-09-13 Online:2016-01-20 Published:2016-01-28
Supported by:
The National Natural Science Foundation of China (No.41301473);National Science and Technology Major Project (No.03-Y30B06-9001-13/15-01).The National Natural Science Foundation of Zhejiang Province of China(No. LZ14F020001)

摘要/Abstract

摘要： 多种因素引起的辐射特征变化,将造成阈值法变化检测的误检。对此,本文提出了一种联合概率密度空间的多阈值自适应变化检测方法。首先,将影像从像素空间转化到联合概率密度空间,将变化地物定义为联合概率密度空间的离群点,并采用迭代方法将其提取,然后映射回原始影像后确定变化区域。选取两种典型应用进行试验,结果表明,本文方法在正确率、误检率和漏检率方面优于传统方法,具有较好的稳健性。

关键词: 非监督变化检测, 联合概率密度, 自适应多阈值, 迭代法

Abstract: A variety of factors has led to radiometric variations of the land cover, which severely limits the threshold based change detection method performance. To overcome this problem, we propose a joint probability density space based self adaptive multi-threshold change detection approach. Firstly, the two images of the same geographic area acquired at different time are transformed into the joint probability space. In which, the land cover change pixels are defined as outliers and identified by an iterative method. Then, the extracted outliers are mapped back to the original image space and determine the change area. To illustrate the performance of the proposed method, an experimental analysis on two classical applications is reported and discussed, results show that the proposed method over performed the state of art method in true rate, false alarm rate and omit alarm rate, with high stability.

Key words: unsupervised change detection, joint probability density, self-adaptive multi threshold, iterative method

中图分类号:

P237

吴炜, 沈占锋, 吴田军, 王卫红. 联合概率密度空间的遥感自适应变化检测方法[J]. 测绘学报, 2016, 45(1): 73-79.

WU Wei, SHEN Zhanfeng, WU Tianjun, WANG Weihong. Joint Probability Space Based Self-adaptive Remote Sensing Change Detection Method[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(1): 73-79.

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联合概率密度空间的遥感自适应变化检测方法

Joint Probability Space Based Self-adaptive Remote Sensing Change Detection Method

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