Building detection is a crucial task in the field of remote sensing, which can facilitate urban construction planning, disaster survey, and emergency landing. However, for large-size remote sensing images, the great majority of existing works have ignored the image tilt problem. This problem can result in partitioning buildings into separately oblique parts when the large-size images are partitioned. This is not beneficial to preserve semantic completeness of the building objects. Motivated by the above fact, we first propose a framework for detecting objects in a large-size image, particularly for building detection. The framework mainly consists of two phases. In the first phase, we particularly propose a tilt correction (TC) algorithm, which contains three steps: texture mapping, tilt angle assessment, and image rotation. In the second phase, building detection is performed with object detectors, especially deep-neural-network-based methods. Last but not least, the detection results will be inversely mapped to the original large-size image. Furthermore, a challenging dataset named Aerial Image Building Detection is contributed for the public research. To evaluate the TC method, we also define an evaluation metric to compute the cost of building partition. The experimental results demonstrate the effects of the proposed method for building detection.

中文翻译：

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遥感图像建筑物检测的倾斜校正


建筑物检测是遥感领域的一项重要任务，可以为城市建设规划、灾害调查、紧急迫降等提供便利。然而，对于大尺寸遥感图像，现有的绝大多数工作都忽略了图像倾斜问题。当分割大尺寸图像时，此问题可能会导致将建筑物分割成单独的倾斜部分。这不利于保持建筑对象的语义完整性。受上述事实的启发，我们首先提出了一个用于检测大尺寸图像中的对象的框架，特别是用于建筑物检测。该框架主要包括两个阶段。在第一阶段，我们特别提出了倾斜校正（TC）算法，该算法包含三个步骤：纹理映射、倾斜角度评估和图像旋转。在第二阶段，使用对象检测器进行建筑物检测，特别是基于深度神经网络的方法。最后但并非最不重要的一点是，检测结果将反映射到原始大尺寸图像。此外，还为公共研究贡献了一个名为“航空图像建筑检测”的具有挑战性的数据集。为了评估 TC 方法，我们还定义了一个评估指标来计算构建分区的成本。实验结果证明了所提出的建筑物检测方法的效果。