END-TO-END BUILDING CHANGE DETECTION MODEL IN AERIAL IMAGERY AND DIGITAL SURFACE MODEL BASED ON NEURAL NETWORKS
Multi-temporal building change detection is one of the most essential major issues of photogrammetry and remote sensing at current stage, which is of great significance for wide applications as offering real estate indicators as well as monitoring urban environment. Although current photogrammetry methodologies could be applicated to 2-D remote sensing imagery for rectification with sensor parameters, multi-temporal aerial or satellite imagery is not adequate to offer spectral and textual features for building change detection. Alongside recent development of Dense Image Matching (DIM) technology, the acquisition of 3-D point cloud and Digital Surface Model (DSM) has been generally realized, which could be combined with imagery, making building change detection more effective with greater spatial structure and texture information. Over the past years, scholars have put forward vast change detection techniques including traditional and model-based solutions. Nevertheless, existing appropriate methodology combined with Neural Networks (NN) for accurate building change detection with multi-temporal imagery and DSM remains to be of great research focus currently due to the inevitable limitations and omissions of existing NN-based methods, which is of great research prospect. This study proposed a novel end-to-end model framework based on deep learning for pixel-level building change detection from high-spatial resolution aerial ortho imagery and corresponding DSM sharing same resolution, which is from the dataset of Tokyo whole area.