Abstract
Accurate roof information of buildings can be obtained from UAV high-resolution images. The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and mono-pitched roofs) of rural buildings is crucial for rural planning and construction. At present, most UAV high-resolution optical images only have red, green and blue (RGB) band information, which aggravates the problems of inter-class similarity and intra-class variability of image features. Furthermore, the different roof types of rural buildings are complex, spatially scattered, and easily covered by vegetation, which in turn leads to the low accuracy of roof type identification by existing methods. In response to the above problems, this paper proposes a method for identifying roof types of complex rural buildings based on visible high-resolution remote sensing images from UAVs. First, the fusion of deep learning networks with different visual features is investigated to analyze the effect of the different feature combinations of the visible difference vegetation index (VDVI) and Sobel edge detection features and UAV visible images on model recognition of rural building roof types. Secondly, an improved Mask R-CNN model is proposed to learn more complex features of different types of images of building roofs by using the ResNet152 feature extraction network with migration learning. After we obtained roof type recognition results in two test areas, we evaluated the accuracy of the results using the confusion matrix and obtained the following conclusions: (1) the model with RGB images incorporating Sobel edge detection features has the highest accuracy and enables the model to recognize more and more accurately the roof types of different morphological rural buildings, and the model recognition accuracy (Kappa coefficient (KC)) compared to that of RGB images is on average improved by 0.115; (2) compared with the original Mask R-CNN, U-Net, DeeplabV3 and PSPNet deep learning models, the improved Mask R-CNN model has the highest accuracy in recognizing the roof types of rural buildings, with F1-score, KC and OA averaging 0.777, 0.821 and 0.905, respectively. The method can obtain clear and accurate profiles and types of rural building roofs, and can be extended for green roof suitability evaluation, rooftop solar potential assessment, and other building roof surveys, management and planning.
Highlights
To evaluate the performance of the improved Mask RCNN model, we trained the model on the feature combination images with the highest accuracy of roof type recognition and compared it with the original Mask R-Convolutional Neural Network (CNN), UNet [71], DeeplabV3 [72] and PSPNet [73] models, and we verified the impact of different models on the roof type recognition results of single rural buildings and the overall roof type
Rural areas in China account for nearly half of the Chinese population, and the survey of rural building roof types is of great significance for the planning and construction of beautiful villages in China
Aiming at the current problems that most of the UAV highresolution remote sensing images only have a visible band, that the existing methods have difficulties extracting features of complex roof types, and that features with similar spectral features such as low reflection, obscured vegetation, and concrete roads are confused with building roof types, this paper proposes a method to identify rural building roof types in UAV visible images based on different combinations of visual features, and an improved Mask R-CNN deep learning model is used to improve the recognition accuracy of complex building roof types
Summary
Due to the presence of a large number of shadows, features with similar spectral characteristics to buildings (such as roads, etc.) and intra-class hybrid image elements, the traditional remote sensing image classification methods based on pixel features cannot effectively, correctly and completely recognize different roof types [12]. Considering that the size of image elements in high spatial resolution remote sensing images reflecting the ground target is closer to the natural scene target taken on the ground, which is more in line with the human eye’s perception compared with low and medium resolution images [13], there are many scholars using machine learning methods to extract different roof types, such as objectoriented classification, support vector machine classification (SVM) and random forest classification (RF) [14,15]. Some studies have combined LiDAR point cloud data and satellite image data using SVM and RF models to identify multiple roof types (e.g., flat, gabled, hipped, pyramidal and skillion roof types) to improve the accuracy of roof category identification by machine learning models [18], the high cost of acquiring
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