High resolution remote sensing image object change detection based on box-plot method

doi:10.6046/gtzyyg.2020.02.03

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Abstract

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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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.02.03 OR https://www.gtzyyg.com/EN/Y2020/V32/I2/19

Fig.1 Flowchart ofproposedmethod

Fig.2 The flowchart of incremental segmentation

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

Tab.2 Accuracy in different confidence(%)

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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