Affiliation:
1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
2. School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
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.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference44 articles.
1. ESCNet: An end-to-end superpixel-enhanced change detection network for very-high-resolution remote sensing images;Zhang;IEEE Trans. Neural Netw. Learn. Syst.,2021
2. An effective superpixel-based graph convolutional network for small waterbody extraction from remotely sensed imagery;Shi;Int. J. Appl. Earth Obs. Geoinf.,2022
3. Superpixel tensor model for spatial–spectral classification of remote sensing images;Gu;IEEE Trans. Geosci. Remote Sens.,2019
4. Mixture-based superpixel segmentation and classification of SAR images;Arisoy;IEEE Geosci. Remote Sens. Lett.,2016
5. Fast multiscale superpixel segmentation for SAR imagery;Zhang;IEEE Geosci. Remote Sens. Lett.,2020