Unsupervised domain adaptation semantic segmentation of high-resolution
remote sensing imagery with invariant domain-level context memory
- URL: http://arxiv.org/abs/2208.07722v1
- Date: Tue, 16 Aug 2022 12:35:57 GMT
- Title: Unsupervised domain adaptation semantic segmentation of high-resolution
remote sensing imagery with invariant domain-level context memory
- Authors: Jingru Zhu, Ya Guo, Geng Sun, Libo Yang, Min Deng, Jie Chen
- Abstract summary: This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery.
MemoryAdaptNet constructs an output space adversarial learning scheme to bridge the domain distribution discrepancy between source domain and target domain.
Experiments under three cross-domain tasks indicate that our proposed MemoryAdaptNet is remarkably superior to the state-of-the-art methods.
- Score: 10.210120085157161
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Semantic segmentation is a key technique involved in automatic interpretation
of high-resolution remote sensing (HRS) imagery and has drawn much attention in
the remote sensing community. Deep convolutional neural networks (DCNNs) have
been successfully applied to the HRS imagery semantic segmentation task due to
their hierarchical representation ability. However, the heavy dependency on a
large number of training data with dense annotation and the sensitiveness to
the variation of data distribution severely restrict the potential application
of DCNNs for the semantic segmentation of HRS imagery. This study proposes a
novel unsupervised domain adaptation semantic segmentation network
(MemoryAdaptNet) for the semantic segmentation of HRS imagery. MemoryAdaptNet
constructs an output space adversarial learning scheme to bridge the domain
distribution discrepancy between source domain and target domain and to narrow
the influence of domain shift. Specifically, we embed an invariant feature
memory module to store invariant domain-level context information because the
features obtained from adversarial learning only tend to represent the variant
feature of current limited inputs. This module is integrated by a category
attention-driven invariant domain-level context aggregation module to current
pseudo invariant feature for further augmenting the pixel representations. An
entropy-based pseudo label filtering strategy is used to update the memory
module with high-confident pseudo invariant feature of current target images.
Extensive experiments under three cross-domain tasks indicate that our proposed
MemoryAdaptNet is remarkably superior to the state-of-the-art methods.
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