Associate professor
Supervisor of Master's Candidates
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Affiliation of Author(s):计算机科学与工程学院
Journal:Applied Sciences-Basel
Funded by:自选课题
Key Words:superpixel segmentation; gating mechanism; multi-scale superpixel feature
Abstract:A superpixel is a group of pixels with similar low-level and mid-level properties, which can be seen as a basic unit in the pre-processing of remote sensing images. Therefore, superpixel segmentation can reduce the computation cost largely. However, all the deep-learning-based methods still suffer from the under-segmentation and low compactness problem of remote sensing images. To fix the problem, we propose EAGNet, an enhanced atrous extractor and self-dynamic gate network. The enhanced atrous extractor is used to extract the multi-scale superpixel feature with contextual information. The multi-scale superpixel feature with contextual information can solve the low compactness effectively. The self-dynamic gate network introduces the gating and dynamic mechanisms to inject detailed information, which solves the under-segmentation effectively. Massive experiments have shown that our EAGNet can achieve the state-of-the-art performance between k-means and deep-learning-based methods. Our methods achieved 97.61 in ASA and 18.85 in CO on the BSDS500. Furthermore, we also conduct the experiment on the remote sensing dataset to show the generalization of our EAGNet in remote sensing fields.
Co-author:仲昭昊,胡统业,赵宏伟
First Author:Kevin Liu
Indexed by:Journal paper
Page Number:2
Translation or Not:no
Date of Publication:2023-12-08
