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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (2) : 19-25     DOI: 10.6046/gtzyyg.2020.02.03
High resolution remote sensing image object change detection based on box-plot method
Chunsen ZHANG1, Rongrong WU1, Guojun LI1, Weihong CUI2, Chenyi FENG1
1. College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
2. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
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The traditional statistics-based change detection method requires the prerequisite that the dataset should obey the Gaussian distribution, such as the iterative chi-square test based change detection method. However, the dataset does not strictly obey the Gaussian distribution, and hence the result is not ideal. A novel change detection method is proposed in this paper, which does not need any assumptions and can take change detection by its own structure. First, an incremental segmentation method is adapted to get objects. After that, spectral and contextual features are combined to calculate its cosine value. Finally, changed objects are found by the box-plot. High-resolution remote sensing images of GF-1 are used as the experimental data. The results are much better than the results of the traditional statistical object-based change detection.

Keywords change detection      high resolution remote sensing image      incremental segmentation      box-plot     
:  TP753  
Issue Date: 18 June 2020
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Chunsen ZHANG
Rongrong WU
Guojun LI
Weihong CUI
Chenyi FENG
Cite this article:   
Chunsen ZHANG,Rongrong WU,Guojun LI, et al. High resolution remote sensing image object change detection based on box-plot method[J]. Remote Sensing for Land & Resources, 2020, 32(2): 19-25.
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Fig.1  Flowchart ofproposedmethod
Fig.2  The flowchart of incremental segmentation
序号 特征组合 OIF
6 PCA前3个主成分分量 3.827 5
Tab.1  OIF of different features combination
Fig.3  Configuration of box-plot
Fig.4  Data and change detection result
Fig.5  Subsets of the study area encompassing some objects detected as having changed with different method
评价指标 0.90 0.95 0.97 0.99
正确率 50.37 70.67 73.67 79.43
遗漏率 6.58 10.30 15.21 19.67
总体精度 58.37 77.10 80.21 85.35
Tab.2  Accuracy in different confidence(%)
评价指标 马氏距离与
正确率 79.43 80.25 83.14
遗漏率 19.67 16.82 17.37
总体精度 85.35 86.34 88.21
Tab.3  Accuracy assessmentof change detection(%)
Fig.6  Mahalanobis distance data graph
Fig.7  Box-whisker plot and change index based on different similarity measure method
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