Journal:Remote Sensing

Abstract:High-resolution remote sensing images (HRRSIs) cover a broad range of geographic regions and contain a wide variety of artificial objects and natural elements at various scales that comprise different image contexts. In semantic segmentation tasks based on deep convolutional neural networks (DCNNs), different resolution features are not equally effective for extracting ground objects with different scales. In this article, we propose a novel context-driven feature-focusing network (CFFNet) aimed at focusing on the multi-scale ground object in fused features of different resolutions. The CFFNe

Indexed by:Journal paper

Document Type:J

Translation or Not:no

Included Journals:SCI

Links to published journals:https://www.mdpi.com/2072-4292/15/5/1348/htm