Remote Sensing, Free Full-Text
Building instances extraction is an essential task for surveying and mapping. Challenges still exist in extracting building instances from high-resolution remote sensing imagery mainly because of complex structures, variety of scales, and interconnected buildings. This study proposes a coarse-to-fine contour optimization network to improve the performance of building instance extraction. Specifically, the network contains two special sub-networks: attention-based feature pyramid sub-network (AFPN) and coarse-to-fine contour sub-network. The former sub-network introduces channel attention into each layer of the original feature pyramid network (FPN) to improve the identification of small buildings, and the latter is designed to accurately extract building contours via two cascaded contour optimization learning. Furthermore, the whole network is jointly optimized by multiple losses, that is, a contour loss, a classification loss, a box regression loss and a general mask loss. Experimental results on three challenging building extraction datasets demonstrated that the proposed method outperformed the state-of-the-art methods’ accuracy and quality of building contours.
Remote Sensing, Free Full-Text, baca renault kangoo
Free Remote Sensing E-books by MDPI
Remote Sensing, Free Full-Text
Remote Sensing, Free Full-Text
From white to green: Snow cover loss and increased vegetation
Remote Sensing, Free Full-Text
Remote Sensing, Free Full-Text
PDF) Remote sensing of algal blooms using a turbidity-free
Remote Sensing, Free Full-Text
Remote Sensing, Free Full-Text
Full Issue in PDF / Numéro complet enform PDF: Canadian Journal of
Remote Sensing, Free Full-Text, renato ruiz pacheco
Remote Sensing, Free Full-Text, scp 7141
Remote Sensing, Free Full-Text