Extracting and recognizing buildings from high-resolution remote sensing images faces many problems due to the complexity of the buildings on the surface. The purpose is to improve the recognition and extraction capab...
详细信息
Extracting and recognizing buildings from high-resolution remote sensing images faces many problems due to the complexity of the buildings on the surface. The purpose is to improve the recognition and extraction capabilities of remote sensing satellite images. The Gao Fen-2 (GF-2) high-resolution remote sensing satellite is taken as the research object. The deep convolutional neural network (CNN) serves as the core of image feature extraction, and PCA (principal component analysis) is adopted to reduce the dimensionality of the data. A correction neural network model, that is, boundary regulated network (BR-Net) is proposed. The features of remote sensing images are extracted through convolution, pooling, and classification. Different data collection models are utilized for comparative analysis to verify the performance of the proposed model. Results demonstrate that when using CNN to recognize remote sensing images, the recognition accuracy is much higher than that of traditional imagerecognition models, which can reach 95.3%. Compared with the newly researched models, the performance is improved by 15%, and the recognition speed is increased by 20%. When extracting buildings with higher accuracy, the proposed model can also ensure clear boundaries, thereby obtaining a complete building image. Therefore, using deep learning technology to identify and extract buildings from high-resolution satellite remote sensing images is of great significance for advancing the deep learning applications in imagerecognition.
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