Paper Info
Title
Dual-Path Sparse Hierarchical Network for Semantic Segmentation of Remote Sensing Images
Abstract
Semantic segmentation of remote sensing images aims to label every pixel with the correct semantic category. The core challenge of the current deep convolutional network (ConvNet)-based methods lies in the difficulty of effectively aggregating high-level categorical semantics and low-level local details along the hierarchy of backbone. Most current approaches consider only fusing adjacent feature layers gradually with short-range feature connections, which lack the diversity of feature interactions, such as long-range cross-scale connections. To this end, we propose a novel dual-path sparse hierarchical network that is characterized by rich cross-scale feature interactions. Multiscale features are first sparsely grouped with a predefined interval, which is then aggregated via both long-range and short-range cross-scale connections in a hierarchical manner. Moreover, in order to further enrich the diversity of feature interactions, we also introduce another fusion path in parallel but with different sparsity for feature grouping, forming a dual-path network. In this way, our model is able to effectively aggregate multilevel features by incorporating both long-range and short-range feature interactions in both parallel and hierarchical manner. Meanwhile, the semantic and resolution gap between multilevel features can also be bridged.
Year
DOI
Venue
2022
10.1109/LGRS.2021.3070426
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Keywords
DocType
Volume
Semantics, Image segmentation, Remote sensing, Spatial resolution, Location awareness, Feature extraction, Aggregates, Deep learning, remote sensing image understanding, semantic segmentation
Journal
19
ISSN
Citations 
PageRank 
1545-598X
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Yupei Wang1131.97
Hao Shi25622.60
Shan Dong313.05
Yin Zhuang4177.77
L. Chen532.74