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.

中文翻译：

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用于检测单视倾斜遥感图像中带或不带开口的建筑立面的自适应阈值

遥感 (RS) 倾斜图像提供了建筑物立面的宝贵信息。使用光谱、空间和仅纹理特征的立面检测不能准确地分离单视图倾斜图像中的立面和非立面区域。因此，提出了立面指数和单峰阈值方法来表征和检测立面区域。这个名为概率空间立面指数 (PSFI) 的新指数首先突出了立面区域。然后，通过基于直方图的阈值创建立面图。在单峰直方图中，阈值程序是根据拟合到 PSFI 直方图的四次多项式模型的二阶导数的根。所提出方法的所有步骤都在谷歌地球引擎云计算平台上实现，可以自动处理非常高分辨率的倾斜图像（在角度和方向方面），没有任何限制。此外，可以有效地处理各种高层和低层建筑，而无需对立面结构进行任何假设。评估表明，该方法在不同的测试区域具有很高的性能，在区分立面和非立面区域以及分离立面和屋顶区域的平均总体准确率分别为 97% 和 86%。可以有效地处理各种高层和低层建筑，而无需对立面结构进行任何假设。评估表明，该方法在不同的测试区域具有很高的性能，在区分立面和非立面区域以及分离立面和屋顶区域的平均总体准确率分别为 97% 和 86%。可以有效地处理各种高层和低层建筑，而无需对立面结构进行任何假设。评估表明，该方法在不同的测试区域具有很高的性能，在区分立面和非立面区域以及分离立面和屋顶区域的平均总体准确率分别为 97% 和 86%。