Automatic detection of suburban building areas (SBAs) is one of the research hotspots for remote sensing images (RSIs), which are widely used in the dynamic monitoring of land use, illegal building monitoring, anti-terrorism, etc. However, because the buildings are distributed targets and their appearances are quite different, the current mainstream detection methods have difficulty obtaining good detection results. To improve the detection performance, this paper presents an SBA detection method based on a deformation adaptability model. It can be divided into two main stages. (1) During key structure (KS) extraction, we first obtain building potential area by Mask R-CNN, and then we extract KS from building potential area based on roof parallel structures (RPSs) to achieve the rapid extraction of KSs. (2) In candidate region identification, to make full use of the relationship among the key structures of buildings, we present a deformation adaptability model, which adapts well to distributed targets with different appearances, and it also has strong resistance to intra-class deformations and a strong ability to eliminate false alarms. We test the proposed method on both general and complex datasets, and the experimental results show that our method has a higher detection accuracy and efficiency than typical methods.

中文翻译：

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基于变形适应性模型的光学遥感影像郊区建筑检测

郊区建筑区（SBA）的自动检测是遥感图像（RSI）的研究热点之一，其广泛应用于土地利用动态监测、违章建筑监测、反恐等领域。由于是分布式目标，外观差异较大，目前主流的检测方法难以获得良好的检测效果。为了提高检测性能，本文提出了一种基于变形适应性模型的SBA检测方法。它可以分为两个主要阶段。（1）在关键结构（KS）提取过程中，我们首先通过Mask R-CNN获取建筑潜在区域，然后基于屋顶平行结构（RPS）从建筑潜在区域中提取KS，以实现KS的快速提取。(2) 在候选区域识别中，为了充分利用建筑物关键结构之间的关系，我们提出了一种变形适应性模型，该模型对不同外观的分布式目标具有很好的适应能力，并且对类内变形具有很强的抵抗能力，并且具有很强的消除误报的能力. 我们在通用数据集和复杂数据集上测试了所提出的方法，实验结果表明，我们的方法比典型方法具有更高的检测精度和效率。