A boundary optimization model of instance segmentation combined with wavelet transform on Buildings

Data driven deep learning methods have become the mainstream method of building extraction from remote sensing images. In this paper, deep learning algorithm is used to classify and extract buildings from remote sensing images of rural areas around the Great Wall in the suburbs of Beijing captured by unmanned aerial vehicles. Aiming at the shortcomings of the current mainstream instance segmentation algorithm Mask R-CNN in feature fusion and poor prediction of instance mask boundaries, this paper proposes a boundary optimization algorithm for building instance segmentation based on discrete wavelet transform. Firstly, the discrete wavelet transform is introduced into the segmentation task branch of Mask R-CNN algorithm to extract the low-frequency and high-frequency information of the real mask, in which the high-frequency information includes the boundary information. Secondly, the pixel by pixel prediction of the mask turns into the learning of the low-frequency and high-frequency information of the real mask. The learning of the high-frequency information helps the segmentation network to learn the boundary features better. Finally, using the reversibility of discrete wavelet transform, the low-frequency and high-frequency information of the learned mask is inversely transformed to reconstruct the final mask. The improved algorithm is evaluated on the dataset COCO, and applied to the automatic extraction of buildings. The DWT Mask R-CNN algorithm model achieved 70.2% segmentation accuracy and 71.4% detection accuracy, which were improved by 1% and 0.7% respectively compared with the Mask R-CNN and Cascade Mask R-CNN models. The experimental results show that the instance segmentation edge optimization algorithm combined with wavelet transform has achieved better results on the segmentation boundary, improved the poor effect of mask edge detection and achieved higher detection accuracy, and can accurately extract village buildings.