Remote sensing (RS) oblique imagery provides valuable information of buildings’ facades. Facade detection using spectral-, spatial-, and texture-only features does not accurately separate facade and nonfacade regions in a single-view oblique image. Therefore, a facade index and a unimodal thresholding method were proposed to characterize and detect facade regions. This new index, named the probability-spatial facade index (PSFI), first highlighted facade areas. Then, the facade map was created through a histogram-based thresholding. In unimodal histograms, the thresholding procedure was according to the roots of the second derivative of a fourth-degree polynomial model that is fitted to the PSFI’s histogram. All the steps of the proposed method were implemented in the Google Earth Engine cloud computing platform and could automatically handle very high resolution oblique imagery (in terms of both angle and direction) without any limitations. Furthermore, various high- and low-rise buildings could be effectively processed without any assumptions about the structure of facades. The evaluation showed the high performance of the proposed method in different test areas, in which the average overall accuracies in distinguishing facade and nonfacade regions and in separating facade and rooftop regions were 97% and 86%, respectively.
Shadow detection plays an important role in remote sensing applications. Shadow should be detected with damage assessment algorithms, and it should be removed from the ground surface with semantic labeling applications. The procedure of a typical shadow detection method includes defining a shadow index and thresholding it, either automatic or manually. An automatic shadow detection method is proposed to facilitate the process of automatic applications. Specifications of shadow and nonshadow areas are analyzed to construct a new spectral–spatial shadow index. Spectral elements of the index can handle the dark shadow extraction. The spatial element of the index provides a high separability between light shadow and dark nonshadow areas such as water bodies. Index definition follows a segmentation algorithm to provide a segment-based analysis. Thresholding is a nonseparable part of shadow detection methods as it divides the region into shadow and nonshadow areas. To avoid high false positive or negative results, the proposed thresholding method is dependent on the enhanced bimodality test. Bimodal distribution can easily be thresholded by typical techniques such as the Otsu thresholding, whereas a special clustering-based thresholding is proposed in the unimodal distribution. The evaluations show a great improvement in shadow detection of very high-resolution RGB images.
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