25 October 2016 The regularized iteratively reweighted object-based MAD method for change detection in bi-temporal, multispectral data
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Proceedings Volume 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology; 101560P (2016) https://doi.org/10.1117/12.2245323
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
Abstract
As the resolution for multispectral images continuously improving, the phenomena that "synonyms spectrum", "foreign body with spectrum" become more apparently, therefore, in the results for pixel-based iteratively reweighted multivariate alteration detection (IR -MAD) may appear many issues such as broken patch, much pseudo-change, more noise, the low overall detection rate et al. In order to improving the above problems. In this paper, the pixel-based IR-MAD algorithm can be transferred to the object domain. the object-based IR-MAD(OB_IRMAD) method apply different and meaningful combinations of features rather than the original pixels. these features are classified into meaningful combinations, so that the results for object-based change detection can get higher reliability and accuracy. To stabilize solutions to the IR-MAD problem, some of regularization may be needed. A case with ZY-3 multispectral image at one point in time in province of Xinjiang border port demonstrate the effectiveness and feasibility of the OB_IRMAD. Compared as, using the same date and region we do the pixel-level IR-MAD change detection and artificial visual change detection. Finally, we calculate the various evaluation indexes for accuracy utilizing confuse matrix and compare the accuracy of the two detection results. The results show: in the ways of overall accuracy, correct rate, error rate, the OB_IRMAD is better than pixel-level IRMAD, change polygon more rules and indicates a less noisy.
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Qiangqiang Xu, Zhengjun Liu, Fangfang Li, Mingze Yang, Haicheng Ren, "The regularized iteratively reweighted object-based MAD method for change detection in bi-temporal, multispectral data", Proc. SPIE 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology, 101560P (25 October 2016); doi: 10.1117/12.2245323; https://doi.org/10.1117/12.2245323
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