基于融合和IFLICM算法的非监督遥感影像变化检测

doi:10.13474/j.cnki.11-2246.2018.0070

测绘通报 ›› 2018, Vol. 0 ›› Issue (3): 25-31.doi: 10.13474/j.cnki.11-2246.2018.0070

基于融合和IFLICM算法的非监督遥感影像变化检测

严宇¹, 刘耀林^1,2,3

1. 武汉大学资源与环境科学学院, 湖北武汉 430079;
2. 武汉大学地理信息系统教育部国家重点实验室, 湖北武汉 430079;
3. 数字制图与国土信息应用工程国家测绘地理信息局重点实验室, 湖北武汉 430079

收稿日期:2017-09-05 出版日期:2018-03-25 发布日期:2018-04-03
作者简介:严宇(1990-),男,硕士生,主要研究方向为遥感、土地利用演变。E-mail:1054571202@qq.com
基金资助:
国家重点研发计划（2017YFB0503505）

Unsupervised Remote Sensing Image Change Detection Based on Fusion and IFLICM Algorithm

YAN Yu¹, LIU Yaolin^1,2,3

1. School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China;
2. Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China;
3. Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan 430079, China

Received:2017-09-05 Online:2018-03-25 Published:2018-04-03

摘要/Abstract

摘要：

提出了一种基于多尺度小波融合和改进的非监督模糊聚类的多光谱遥感影像变化检测方法。该算法解决了目前很多算法造成虚警率较高，而且未能充分利用像元之间空间关系的问题。首先利用二维离散小波（DWT）多尺度分解的方式来构造差异图，通过对两种小波分解系数融合的方式来抑制噪声点和突出变化区域。考虑到像元之间的空间位置信息，在融合后的基础上采用改进的模糊局部信息聚类（IFLICM）的方法得到变化检测结果。对两个时相的多光谱遥感卫星影像进行变化检测试验，试验表明基于融合的变化检测结果精度更高，并且改进后的聚类算法效果比其他聚类算法效果更好。

关键词: 小波变换, 多尺度融合, 模糊聚类, 变化检测

Abstract:

A new method based on multiscale wavelet fusion and improved unsupervised fuzzy clustering algorithm for multispectral remote sensing image change detection is proposed.This algorithm solves the problems that many state of arts algorithms causing too much noise,and failing to fully exploit the spatial relationship between pixels.Firstly,the difference image is constructed by the method of two-dimensional discrete wavelet transform (DWT) multi-scale decomposition,so the speckle noise is constrained and the changed regions are highlighted by fusing two wavelet decomposition coefficients.Next,Considering the spatial neighborhood information of pixels,the improved fuzzy local information clustering (IFLICM) method is implemented to get the result of the change detection on the basis of fusion image.Experiments on multi-temporal images show that the image fusion strategy integrates the advantages of CVA and ADI images and gains a better performance.The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than other clustering algorithms.

Key words: wavelet transform, multiscale fusion, fuzzy clustering, change detection

中图分类号:

P237

严宇, 刘耀林. 基于融合和IFLICM算法的非监督遥感影像变化检测[J]. 测绘通报, 2018, 0(3): 25-31.

YAN Yu, LIU Yaolin. Unsupervised Remote Sensing Image Change Detection Based on Fusion and IFLICM Algorithm[J]. 测绘通报, 2018, 0(3): 25-31.

参考文献

[1] RADKE R J,ANDRA S,ALKOFAHI O,et al.Image Change Detection Algorithms:A Systematic Survey[J].IEEE Transactions on Image Processing,2005,14(3):294-307.
[2] LI D.Remotely Sensed Images and GIS Data Fusion for Automatic Change Detection[J].International Journal of Image and Data Fusion,2010,1(1):99-108.
[3] GHOSH A,MISHRA N S,GHOSH S.Fuzzy Clustering Algorithms for Unsupervised Change Detection in Remote Sensing Images[J].Information Sciences,2011,181(4):699-715.
[4] KHANDELWAL P,SINGH K K,SINGH B K,et al.Unsupervised Change Detection of Multispectral Images Using Wavelet Fusion and Kohonen Clustering Network[J].International Journal of Engineering & Technology,2013,5(2):1401-1406.
[5] 狄亚南,江刚武,张一,等.联合变化强度和相关系数最小错分概率变化检测阈值优化[J].测绘科学技术学报,2016,33(2):180-184.
[6] CELIK T.Bayesian Change Detection Based on Spatial Sampling and Gaussian Mixture Model[J].Pattern Recognition Letters,2011,32(12):1635-1642.
[7] 郝睿,徐俊峰,王庆宝,等.基于BP神经网络的多特征融合变化检测方法[J].海洋测绘,2016,36(1):79-82.
[8] 徐俊峰,张保明,郭海涛,等.一种多特征融合的面向对象多源遥感影像变化检测方法[J].测绘科学技术学报,2015,32(5):505-509.
[9] SHAO P,SHI W,HE P,et al.Novel Approach to Unsupervised Change Detection Based on a Robust Semi-supervised FCM Clustering Algorithm[J].Remote Sensing,2016,8(3):264.
[10] KRINIDIS S,CHATZIS V.A Robust Fuzzy Local Information C-means Clustering Algorithm.[J].IEEE Transactions on Image Processing,2010,19(5):1328-1337.
[11] BRUZZONE L,PRIETO D F.Automatic Analysis of the Difference Image for Unsupervised Change Detection[J].IEEE Transactions on Geoscience & Remote Sensing,2000,38(3):1171-1182.
[12] CELIK T.Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-Means Clustering[J].IEEE Geoscience & Remote Sensing Letters,2009,6(4):772-776.
[13] SZILAGYI L,BENYO Z,SZILAGYI S M,et al.MR Brain Image Segmentation Using an Enhanced Fuzzy C-means Algorithm[C]//Proceedings of the 25th Annual International Conference of the IEEE.[S.l.]:IEEE,2003:724-726.
[14] KRINIDIS S,CHATZIS V.A Robust Fuzzy Local Information C-Means Clustering Algorithm[J].IEEE Transactions on Image Processing,2010,19(5):1328-1337.

[1]	阮明浩, 卢永华, 刘玉贤, 汤圣君, 王伟玺, 张宇琦, 李游. 激光与倾斜点云精确配准与无缝融合[J]. 测绘通报, 2022, 0(4): 6-10.
[2]	潘建平, 李鑫, 孙博文, 胡勇, 李明明. 基于注意力密集连接金字塔网络的新增建设用地变化检测[J]. 测绘通报, 2022, 0(3): 41-46,59.
[3]	杨景玉, 贾多科, 王阳萍. 基于超像素分割与CRF的高分辨率遥感影像变化检测[J]. 测绘通报, 2022, 0(2): 25-30,49.
[4]	乐颖, 夏元平. 基于比值法的多源多时相数据城区变化检测[J]. 测绘通报, 2022, 0(2): 56-61.
[5]	车子杰, 高飞, 吴兆福, 李振轩. 基于改进U-Net网络的多源遥感影像洪涝灾害信息提取与变化分析[J]. 测绘通报, 2022, 0(1): 26-32.
[6]	周秀华, 李乃强. 基于多种相似度特征的道路实体融合方法[J]. 测绘通报, 2021, 0(8): 102-105,157.
[7]	苏艺凡, 党建武, 王阳萍, 杨景玉. 改进的区间二型模糊聚类遥感影像变化检测[J]. 测绘通报, 2021, 0(7): 44-51,58.
[8]	刘海飞, 杨敏华, 王晶, 徐刚, 陈杰. 利用结构面对象的高分遥感影像建筑物变化检测[J]. 测绘通报, 2021, 0(6): 16-20.
[9]	张伟良, 刘琦, 吴长彬, 杨光迪. 基于孪生神经网络的土地利用现状年度变化检测[J]. 测绘通报, 2021, 0(3): 91-95,104.
[10]	赖正文, 夏小云. 像素级和对象级高分辨率遥感图像变化检测分析[J]. 测绘通报, 2021, 0(2): 59-63.
[11]	韩建平. 青海省地震灾后地物变化的快速识别[J]. 测绘通报, 2021, 0(11): 120-123.
[12]	韩建平. 基于遥感影像的青海省环境变化监测[J]. 测绘通报, 2021, 0(10): 123-126,145.
[13]	聂倩, 赵艳福. 结合IR-MAD与均值漂移算法的密集城区遥感影像变化检测[J]. 测绘通报, 2020, 0(6): 57-62.
[14]	刘霞, 郭亚男. 基于随机森林的遥感影像变化检测[J]. 测绘通报, 2020, 0(5): 16-20.
[15]	李亚丽, 张松林, 韩杰. 基于小波系数分割的局部自适应阈值图像去噪[J]. 测绘通报, 2020, 0(5): 43-46,122.

基于融合和IFLICM算法的非监督遥感影像变化检测

Unsupervised Remote Sensing Image Change Detection Based on Fusion and IFLICM Algorithm

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价