Abstract
Remote sensing images with clouds, shadows or stripes are usually considered as defective data which limit their application for change detection. This paper proposes a method to fuse a series of defective images as evidences for change detection. In the proposed method, post-classification comparison process is firstly performed on multi-source defective images. Then, the classification results of all the images, together with their corresponding confusion matrixes are used to calculate the Basic Belief Assignment (BBA) of each pixel. Further, based on the principle of Dempster-Shafer evidence theory, a BBA redistribution process is introduced to deal with the defective parts of multi-source data. At last, evidential fusion and decision making rules are applied on the pixel level, and the final map of change detection can be derived. The proposed method can finish change detection with data fusion and image completion in one integrated process, which makes use of the complementary and redundant information from the input images. The method is applied to a case study of landslide barrier lake formed in Aug. 3rd, 2014, with a series of multispectral images from different sensors of GF-1 satellite. Result shows that the proposed method can not only complete the defective parts of the input images, but also provide better change detection accuracy than post-classification comparison method with single pair of pre- and post-change images. Subsequent analysis indicates that high conflict degree between evidences is the main source of errors in the result. Finally, some possible reasons that result in evidence conflict on the pixel level are analysed.
Highlights
Change detection with remote sensing techniques are usually depend on images with precise registration, radiometric and atmospheric calibration, similar phenological states, similar spatial and spectral resolution if possible (Coppin P., 2004)
Change detection based on remote sensing images relies heavily on the quality of input data
Multi-source remote sensing data provide the second choice to remedy the defects of images for change detection, especially in emergency situations
Summary
Change detection with remote sensing techniques are usually depend on images with precise registration, radiometric and atmospheric calibration, similar phenological states, similar spatial and spectral resolution if possible (Coppin P., 2004). The idealized data source is not always available due to the limitation of satellite revisiting period, the weather condition and the sensor defects. Applications such as disaster monitoring cannot wait for the acquisition of high-quality remote sensing images in a short time. The schemes of fusion multi-source data expand the application of change detection, and make it possible to use remote sensing images from different sensors (Nichol J., 2005, Xu M., 2009, Longbotham N., 2012, Liu Z., 2014); The schemes of combining multi-temporal data demonstrate that detailed spatial variations of objects can be measured with time series images; The schemes of fusing different change indexes
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