Scale-aware neural network for semantic segmentation of multi-resolution remote sensing images

Wang, Libo; Zhang, Ce ORCID: https://orcid.org/0000-0001-5100-3584; Li, Rui; Duan, Chenxi; Meng, Xiaoliang; Atkinson, Peter M.. 2021 Scale-aware neural network for semantic segmentation of multi-resolution remote sensing images. Remote Sensing, 13 (24), 5015. 19, pp. 10.3390/rs13245015

Before downloading, please read NORA policies.

Preview

Text N531914JA.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (3MB) | Preview

Abstract/Summary

Assigning geospatial objects with specific categories at the pixel level is a fundamental task in remote sensing image analysis. Along with the rapid development of sensor technologies, remotely sensed images can be captured at multiple spatial resolutions (MSR) with information content manifested at different scales. Extracting information from these MSR images represents huge opportunities for enhanced feature representation and characterisation. However, MSR images suffer from two critical issues: (1) increased scale variation of geo-objects and (2) loss of detailed information at coarse spatial resolutions. To bridge these gaps, in this paper, we propose a novel scale-aware neural network (SaNet) for the semantic segmentation of MSR remotely sensed imagery. SaNet deploys a densely connected feature network (DCFFM) module to capture high-quality multi-scale context, such that the scale variation is handled properly and the quality of segmentation is increased for both large and small objects. A spatial feature recalibration (SFRM) module was further incorporated into the network to learn intact semantic content with enhanced spatial relationships, where the negative effects of information loss are removed. The combination of DCFFM and SFRM allows SaNet to learn scale-aware feature representation, which outperforms the existing multi-scale feature representation. Extensive experiments on three semantic segmentation datasets demonstrated the effectiveness of the proposed SaNet in cross-resolution segmentation.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.3390/rs13245015
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-)
ISSN:	2072-4292
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	deep convolutional neural network, multiple spatial resolutions, remote sensing, scale-aware feature representation, semantic segmentation
NORA Subject Terms:	Electronics, Engineering and Technology Computer Science
Date made live:	08 Feb 2022 17:16 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/531914

Actions (login required)

View Item

Document Downloads

More statistics for this item...