Building Detection From Remote Sensing Imagery Based On Boundary Regulated Network And Watershed Segmentation

High-density urban cities contain numerous similar buildings positioned in close proximity.Building detection from high spatial resolution remote sensing imagery in such scenes remains a challenge in the fields of computer vision and remote sensing urban application.The integration of traditional segmentation algorithms and a novel neural network is an effective way for such challenging settings.Inspired by the recent success of deep-learning-based edge detection,a new building detection method aiming at accurate boundary is proposed.According to characteristics of building and its border,this paper improves the network structure and integrates the network with bottom-up watershed segmentation to improve boundary precision as well as classification accuracy.First,two auxiliary labels-the building boundary and the parting line are derived from the original dataset through data preprocessing.Subsequently,a newly proposed building detection frame ICT-Net is improved by modifying structure and loss function in accordance with two auxiliary labels to obtain the probability of three classes.Finally,one postprocess integrating watershed segmentation with gradient boosted regression trees is employed to achieve high accuracy of building detection.Specifically,the probability feature map is generated by merging the probability of three classes.Watershed segmentation with building marker thresholds is applied to obtain building instances from probability feature map.Then the building probability of each building instance predicted by gradient boosted regression trees is used to select building instances,resulting in building detection results.In addition,parameters selection is implemented.The performance of the proposed method is validated on INRIA dataset which provides aerial orthorectified color imagery with a spatial resolution of0.3m with corresponding ground truth label for two semantic classes: building and not building.The experimental results suggest that data preprocessing and the application of boundary loss can gain an improvement of 1% in terms of Intersection over Union(IOU)of building detection.Postprocess can take full advantage of probability information from the network,effectively optimizing the building boundary.Postprocess brings an improvement of 10.5% in terms of building instance recall compared with that of results from the neural network.Our paper has a building instance recall that is 22.9% higher than that of the original ICT-Net.A novel building detection method based on boundary regulated network and watershed segmentation is proposed in this study.The experimental results show the advantages of the enhanced-boundary-oriented data preprocessing and the modified neural network and demonstrate the proposed method can further improve prediction accuracy on the basis of network.Nevertheless,the great performance of the proposed method largely depends on parameters selection,and further improvements should be made in the future.