Paper Info
Title
Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images
Abstract
In this article, we focus on the challenging multicategory instance segmentation problem in remote sensing images (RSIs), which aims at predicting the categories of all instances and localizing them with pixel-level masks. Although many landmark frameworks have demonstrated promising performance in instance segmentation, the complexity in the background and scale variability instances still remain challenging, for instance, segmentation of RSIs. To address the above problems, we propose an end-to-end multicategory instance segmentation model, namely, the semantic attention (SEA) and scale complementary network, which mainly consists of a SEA module and a scale complementary mask branch (SCMB). The SEA module contains a simple fully convolutional semantic segmentation branch with extra supervision to strengthen the activation of interest instances on the feature map and reduce the background noise’s interference. To handle the undersegmentation of geospatial instances with large varying scales, we design the SCMB that extends the original single mask branch to trident mask branches and introduces complementary mask supervision at different scales to sufficiently leverage the multiscale information. We conduct comprehensive experiments to evaluate the effectiveness of our proposed method on the iSAID dataset and the NWPU Instance Segmentation dataset and achieve promising performance.
Year
DOI
Venue
2022
10.1109/TCYB.2021.3096185
IEEE Transactions on Cybernetics
Keywords
DocType
Volume
Attention,Image Processing, Computer-Assisted,Remote Sensing Technology,Semantics
Journal
52
Issue
ISSN
Citations 
10
2168-2267
0
PageRank 
References 
Authors
0.34
17
7
Name
Order
Citations
PageRank
Tianyang Zhang1638.35
Xiangrong Zhang249348.70
Peng Zhu312.03
Xu Tang42210.14
Chen Li58054.64
L. C. Jiao660726.06
Huiyu Zhou71303111.91