Abstract
Abstract. The detection and reconstruction of building have attracted more attention in the community of remote sensing and computer vision. Light detection and ranging (LiDAR) has been proved to be a good way to extract building roofs, while we have to face the problem of data shortage for most of the time. In this paper, we tried to extract the building roofs from very high resolution (VHR) images of Chinese satellite Gaofen-2 by employing convolutional neural network (CNN). It has been proved that the CNN is of a higher capability of recognizing detailed features which may not be classified out by object-based classification approach. Several major steps are concerned in this study, such as generation of training dataset, model training, image segmentation and building roofs recognition. First, urban objects such as trees, roads, squares and buildings were classified based on random forest algorithm by an object-oriented classification approach, the building regions were separated from other classes at the aid of visually interpretation and correction; Next, different types of building roofs mainly categorized by color and size information were trained using the trained CNN. Finally, the industrial and residential building roofs have been recognized individually and the results have been validated individually. The assessment results prove effectiveness of the proposed method with approximately 91% and 88% of quality rates in detection industrial and residential building roofs, respectively. Which means that the CNN approach is prospecting in detecting buildings with a very higher accuracy.
Highlights
High-resolution remote sensing images can provide massive surface feature information with rich texture and spectral characteristics, so they have been widely used in map mapping
(3) Urban buildings are often shaded by dense trees, and lowrise buildings are often shaded by high-rise buildings, which makes it extremely difficult to extract complete and complete buildings
The deep learning method represented by convolutional neural network (CNN) realizes the recognition and classification of objects
Summary
High-resolution remote sensing images can provide massive surface feature information with rich texture and spectral characteristics, so they have been widely used in map mapping. Gong Danchao et al proposed a method of building detection based on boundary line, which realized automatic building extraction These traditional algorithms only pay attention to the geometric features of buildings [5]. Among many different types of ground objects such as buildings, water bodies, vegetation, etc., the shapes of the ground objects such as water bodies and vegetation are mostly irregular shapes, accounting for a small proportion, and the importance of these ground object types in urban planning and construction of smart cities is far less than that of buildings and. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W10, 2020 International Conference on Geomatics in the Big Data Era (ICGBD), 15–17 November 2019, Guilin, Guangxi, China roads, so extracting buildings and roads for change monitoring of urban construction is the core idea of this paper
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