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

Remote Sensing, Free Full-Text, está bogaz 0.41

Remote Sensing, Free Full-Text

3D Remote Sensing Applications in Forest Ecology: Composition

Images

Remote Sensing, Free Full-Text, code legend piece update 0.5

ISPRS Journal of Photogrammetry and Remote Sensing 2012: Vol 69

PDF) Remote Sensing Image Classification Using Transfer Learning

Full Issue in PDF / Numéro complet enform PDF: Canadian Journal of

Remote Sensing

Remote Sensing, Free Full-Text

PDF) Remote Sensing Image Classification Using Transfer Learning

PDF) Remote sensing of agriculture – South/Southeast Asia research

Remote Sensing UAV Market Size to Grow at Over 5% CAGR Amid the

Remote Sensing, Free Full-Text, code legend piece update 0.5