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Dual-Path Sparse Hierarchical Network for Semantic Segmentation of Remote Sensing Images
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DOI：10.1109/LGRS.2021.3070426.png)
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Abstract
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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.
Keywords:
Semantics
Image segmentation
Remote sensing
Spatial resolution
Location awareness
Feature extraction
Aggregates
Deep learning
remote sensing image understanding
semantic segmentation
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