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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (2) : 93-99     DOI: 10.6046/gtzyyg.2018.02.13
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Change detection based on adaptive fusion of multiple features
Guanghui WANG1(), Jianlei LI2, Huabin WANG1, Huachao YANG2
1. Satelite Surveying and Mapping Application,NASG,Beijing 100830,China
2. China University of Mining and Technology(Xuzhou), Xuzhou 221116, China
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Abstract  

In view of the fact that the traditional change detection algorithm mainly depends on the spectral information and fails to effectively use image feature detection advantage, the authors put forward a multi-feature fusion of remote sensing image change detection algorithm. First, color histogram and edge histogram of gradient image object with multi-scale segmentation is statistically analyzed based on the calculation of each object. Then, the object color distance and edge linear characteristics distance between different periods are calculated by using the earth mover’s distance method; the adaptive weighted method is used to combine color distance and edge linear characteristics distance so as to construct object heterogeneity. Finally, the images change detection results are analyzed by using histogram curvature. The experimental results show that the method can fully fuse the color and edge line features and improve the accuracy of detection.
Keywords change detection      color histogram      linear gradient histogram      histogram distance      histogram curvature     
:  TP79  
Issue Date: 30 May 2018
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Guanghui WANG
Jianlei LI
Huabin WANG
Huachao YANG
Cite this article:   
Guanghui WANG,Jianlei LI,Huabin WANG, et al. Change detection based on adaptive fusion of multiple features[J]. Remote Sensing for Land & Resources, 2018, 30(2): 93-99.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.02.13     OR     https://www.gtzyyg.com/EN/Y2018/V30/I2/93
Fig.1  Workflow of change detection
Fig.2  Area of color dominate
Fig.3  Area of edge line dominate
Fig.4  Suzhou City images
Fig.5  Segmentation and standard detection results
Fig.6  Different weight results of change detection
参数 ωhsv=1 ωline=0 ωhsv=0.7 ωline=0.3 ωhsv=0.5 ωline=0.5
变化 未变化 变化 未变化 变化 未变化
实际变化/m2 684 947.40 781 227.60 926 630.50 575 792.40 814 214.35 739 430.11
实际未变化/m2 319 829.90 9 790 902.40 391 942.40 9 682 541.80 393 480.80 9 629 782.05
合计/m2 1 004 777.30 10 572 130.00 1 318 572.90 10 258 334.20 1 207 695.15 10 369 212.16
虚检率 0.32 0.30 0.33
漏检率 0.53 0.38 0.46
正确率 0.90 0.92 0.90
参数 ωhsv=0.3 ωline=0.7 ωhsv=0 ωline=1 本文算法
变化 未变化 变化 未变化 变化 未变化
实际变化/m2 1 090 209.13 348 681.20 1 288 899.00 177 276.00 1 174 865.98 327 581.02
实际未变化/m2 583 248.27 9 554 768.70 867 573.00 9 243 159.20 374 527.02 9 699 933.08
合计/m2 1 673 457.40 9 903 449.90 2 156 472.00 9 420 435.20 1 549 393.00 10 027 514.10
虚检率 0.35 0.40 0.24
漏检率 0.24 0.12 0.22
正确率 0.92 0.91 0.94
Tab.1  Accuracy comparison by methods with different weights
参数 文献[9]方法 文献[16]方法 本文算法
虚检率 0.32 0.32 0.24
漏检率 0.21 0.25 0.22
正确率 0.93 0.92 0.94
Tab.2  Accuracy comparison of different methods of change detection
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