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We propose a network for semantic mapping called the Dense DilatedConvolutions Merging Network (DDCM-Net) to provide a deep learning approachthat can recognize multi-scale and complex shaped objects with similar colorand textures, such as buildings, surfaces/roads, and trees in very highresolution remote sensing images. The proposed DDCM-Net consists of densedilated convolutions merged with varying dilation rates. This can effectivelyenlarge the kernels' receptive fields, and, more importantly, obtain fusedlocal and global context information to promote surrounding discriminativecapability. We demonstrate the effectiveness of the proposed DDCM-Net on thepublicly available ISPRS Potsdam dataset and achieve a performance of 92.3%F1-score and 86.0% mean intersection over union accuracy by only using the RGBbands, without any post-processing. We also show results on the ISPRS Vaihingendataset, where the DDCM-Net trained with IRRG bands, also obtained bettermapping accuracy (89.8% F1-score) than previous state-of-the-art approaches.

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images