SaNet: Scale-aware neural Network for Parsing Multiple Spatial Resolution Aerial Images

• URL: http://arxiv.org/abs/2103.07935v1
• Date: Sun, 14 Mar 2021 14:19:46 GMT
• Title: SaNet: Scale-aware neural Network for Parsing Multiple Spatial Resolution Aerial Images
• Authors: Libo Wang (School of Remote Sensing and Information Engineering Wuhan University, China)
• Abstract summary: We propose a novel scale-aware neural network (SaNet) for parsing multiple spatial resolution aerial images. For coping with the imbalanced segmentation quality between larger and smaller objects caused by the scale variation, the SaNet deploys a densely connected feature network (DCFPN) module. To alleviate the informative feature loss, a SFR module is incorporated into the network to learn scale-invariant features with spatial relation enhancement.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Assigning the geospatial objects of aerial images with categorical information at the pixel-level is a basic task in urban scene understanding. However, the huge differencc in remote sensing sensors makes the acqured aerial images in multiple spatial resolution (MSR), which brings two issues: the increased scale variation of geospatial objects and informative feature loss as spatial resolution drops. To address the two issues, we propose a novel scale-aware neural network (SaNet) for parsing MSR aerial images. For coping with the imbalanced segmentation quality between larger and smaller objects caused by the scale variation, the SaNet deploys a densely connected feature network (DCFPN) module to capture quality multi-scale context with large receptive fields. To alleviate the informative feature loss, a SFR module is incorporated into the network to learn scale-invariant features with spatial relation enhancement. Extensive experimental results on the ISPRS Vaihingen 2D Dataset and ISPRS Potsdam 2D Dataset demonstrate the outstanding cross-resolution segmentation ability of the proposed SaNet compared to other state-of-the-art networks.

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