# Research on Building Target Detection Based on High-Resolution Optical Remote Sensing Imagery
## 1 Introduction  
Accurate building target detection has important scientific and practical significance in urban 3D modeling and urban dynamic change management. The problem of building target detection has been a research hotspot in the field of remote sensing; however, timely and accurate detection of building targets on high-resolution remote sensing images is a challenge. With the development of remote sensing technology, the spatial resolution of optical remote sensing images has been gradually improved, and nowadays it has entered the sub-meter level, and the related research on remote sensing images has become an important part of the field, providing unprecedented opportunities for the development of remote sensing applications [1, 2, 3]. Building target detection based on remote sensing images has been developed rapidly with the development and popularization of remote sensing technology [4, 5, 6]. At present, the research on building detection based on remote sensing images at home and abroad can be mainly divided into three types of methods based on geometric structure, supervised classification and texture features. Compared with the non-building backgrounds, the building targets have relatively regular geometric structures, and their geometric features are the most obvious and typical characteristics of buildings, which are inherent properties not easily affected by changes in external temperature, humidity, and light. Therefore, geometric structure-based detection methods are one of the most popular methods in the research of building detection. [PERSON] et al. [7] used Scale Invariant Feature Transform (SIFT) (in Table 1) and adaptive windowed Hoff transform to extract the corner points of building targets and the line segmentsforming the boundary for detection. [PERSON] et al. [8] proposed a building detection method for very high spatial resolution (VHR) images based on the classical local binarization algorithm. The geometric structure-based approach is popular for research. It takes advantage of the fact that the basic structure of the building target is composed of a rectangle of different shapes, so it adopts the detection of right angles, line segments or corner points of the rectangular structure to extract features such as point, line, surface features and gradient features of the building, and then realize the detection of the building. With the rapid development and application of deep learning, there are more and more examples of deep learning algorithm applications in building detection, including YOLO series, CNN, ImageNet, DeconvNet and many other algorithms. [PERSON] et al. [9] used a very large training dataset for supervised classification, using powerful CNNs and a huge pre-trained ImageNet to extract buildings, with good quantitative validation results, but high difficulty and huge time consumption to obtain the training dataset. [PERSON] et al. [10] proposed a method for automatic detection of suburban buildings based on super-scale saliency sliding window detection of candidate regions combined with CNNs. [PERSON] et al. [11] proposed a hierarchical building detection framework based on deep-learning models. The Gaussian pyramid technique is used to construct a generative model of multi-layer training samples, and a building area suggestion network is proposed to quickly extract candidate building areas. Convolutional neural networks (CNNs) are used to build a multi-layer building detection model for detecting building targets in remote sensing images. The advantages of the deep-learning-based approach are high accuracy and ease of use, while the disadvantages are high difficulty in obtaining training datasets and high time consumption. Texture features contain image color, contrast, gray-scale histogram, gray-scale co-occurrence matrix, all of which are very important information for the target detection process of remote sensing images. [PERSON] et al. [12] proposed an adaptive local texture feature detection method based on continuous background removal. [PERSON] et al.