A COMPARATIVE STUDY for BUILDING SEGMENTATION in REMOTE SENSING IMAGES USING DEEP NETWORKS: CSCRS ISTANBUL BUILDING DATASET and RESULTS

Abstract

Building semantic segmentation is an exceedingly important issue in the field of remote sensing. A new building dataset as created consisting of very high-resolution optical satellite images provided by the Center for Satellite Communications and Remote Sensing (CSCRS). The imagery is obtained by Pleiades satellite and have a resolution of 0.5 meters. Segmentation results have been obtained using post-FCN architectures. Architectures examined in this work fall under one of few categories. The first category is Encoder-Decoder Network: An encoder that reduces the spatial resolution of the data and a decoder that recreates the lower resolution result of the encoder and upsamples it. The second category is Feature Pyramid Network, in this type of network scene information is aggregated across pyramid structures which produce more comprehensive results. The third category is Dilated Network, due to its atrous structure, which can calculate any layer at any desired resolution, with the presence of holes in the filter. The final category is Attention-Based Network, in these networks, certain aspects of the data are emphasized while other aspects are ignored. After this work, it can be seen that according to several metrics Dilated and Attention-Based Networks perform better than their counterparts. As a result of the training of 100 epochs with the data set in architectures belonging to Dilated and Attention-Based Networks, IoU values above 0.90 were obtained.