Urban Land Cover Classification and Change Detection Using Fully Atrous Convolutional Neural Network

Urban land use/land cover classification and change detection based on remote sensing imagery are of great significance in land use surveying and updating. Based on Wuhan high-resolution aerial and satellite remote sensing images and corresponding GIS vector data, we propose a novel convolutional neural network to apply in the urban land cover classification and change detection. Firstly, a fully atrous convolutional neural network (FACNN) is proposed, which could take into account the different scale and LOD (level of detail) of polygons in the GIS vector data. Then, both pixel-based change detection and object-based change detection are analyzed according to the classification maps from FACNN and a previous GIS map. Finally, the effectiveness and advantage of our method are verified by the classification and change detection experiments in very high resolution remote sensing images of Wuhan city covering more than 8 000 km². The proposed FACNN proved outperforming mainstream CNN based methods as FCN-16, U-Net, and Dense-Net, and the precision of the object-based change detection achieved 74.1% and the recall was 96.4%, indicating application prospects for unban GIS map updating.