Hi-ResNet: A High-Resolution Remote Sensing Network for Semantic
Segmentation
- URL: http://arxiv.org/abs/2305.12691v2
- Date: Tue, 23 May 2023 04:32:46 GMT
- Title: Hi-ResNet: A High-Resolution Remote Sensing Network for Semantic
Segmentation
- Authors: Yuxia Chen, Pengcheng Fang, Jianhui Yu, Xiaoling Zhong, Xiaoming
Zhang, Tianrui Li
- Abstract summary: High-resolution remote sensing (HRS) semantic segmentation extracts key objects from high-resolution coverage areas.
objects of the same category within HRS images show significant differences in scale and shape across diverse geographical environments.
We propose a High-resolution remote sensing network (Hi-ResNet) with efficient network structure designs.
- Score: 7.216053041550996
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: High-resolution remote sensing (HRS) semantic segmentation extracts key
objects from high-resolution coverage areas. However, objects of the same
category within HRS images generally show significant differences in scale and
shape across diverse geographical environments, making it difficult to fit the
data distribution. Additionally, a complex background environment causes
similar appearances of objects of different categories, which precipitates a
substantial number of objects into misclassification as background. These
issues make existing learning algorithms sub-optimal. In this work, we solve
the above-mentioned problems by proposing a High-resolution remote sensing
network (Hi-ResNet) with efficient network structure designs, which consists of
a funnel module, a multi-branch module with stacks of information aggregation
(IA) blocks, and a feature refinement module, sequentially, and Class-agnostic
Edge Aware (CEA) loss. Specifically, we propose a funnel module to downsample,
which reduces the computational cost, and extract high-resolution semantic
information from the initial input image. Secondly, we downsample the processed
feature images into multi-resolution branches incrementally to capture image
features at different scales and apply IA blocks, which capture key latent
information by leveraging attention mechanisms, for effective feature
aggregation, distinguishing image features of the same class with variant
scales and shapes. Finally, our feature refinement module integrate the CEA
loss function, which disambiguates inter-class objects with similar shapes and
increases the data distribution distance for correct predictions. With
effective pre-training strategies, we demonstrated the superiority of Hi-ResNet
over state-of-the-art methods on three HRS segmentation benchmarks.
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