A high-resolution remote sensing image building extraction method based on deep learning

Traditional building extraction from very high resolution remote sensing optical imagery is limited by low precision and incomplete boundary. In this paper, a high-resolution remote sensing image building extraction method based on deep learning is proposed. Firstly, Principal Component Analysis is used to pre-train network structure in an unsupervised way and obtain the characteristics of remote sensing image. Secondly, an adaptive pooling model is proposed to reduce the feature information loss in the pooling process. The texture features are extracted by non-subsampled contour wave transformation and introduced to the network to improve the building extraction. Finally, the obtained image features are inputted into the softmax classifier for classification and building extraction results. A typical experiment areas selected. The comparison with typical building extraction method, the experimental results shows that the proposed method can extract the buildings with higher accuracy, especially the clearer and more complete boundary.