计算机科学
推论
蒸馏
语义学(计算机科学)
分割
人工智能
图形
数据挖掘
图像分割
机器学习
情报检索
理论计算机科学
程序设计语言
化学
有机化学
作者
Wujie Zhou,Y. Li,Jinjie Huang,Weiqing Yan,Meixin Fang,Qiuping Jiang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-12
标识
DOI:10.1109/tgrs.2023.3332336
摘要
In recent years, optical remote sensing image (ORSI) scene analysis has attracted increasing interest. However, existing networks show a trend of bifurcation. Lightweight networks have very high inference speed but poor inference of contextual information in highly complex backgrounds. In contrast, networks with high-performance contextual information reasoning capability require many parameters and are computationally expensive. Since the knowledge distillation method can greatly lighten the model, we propose a graph semantic guided network (GSGNet) that utilizes knowledge refinement for ORSI scenario analysis, which has a high inference speed while maintaining practical contextual inference capability. Rich semantic and detailed information facilitates semantic segmentation of optical remote sensing images. We design adjacent dynamic capture and local-global map inference modules that can effectively extract low-level spatial details and high-level contextual semantics. To improve the attention map relearning performance of the distillation method, we designed semantically guided fusion modules to locate spatial information and refine edge information. We also employed a structural relationship transfer distillation method in which the structural relationship knowledge of the teacher model (GSGNet-T) was used to guide the student model (GSGNet-S). We compared the performances of GSGNet-T and the GSGNet-S with knowledge distillation (GSGNet-S*) with those of several state-of-the-art methods on the Vaihingen and Potsdam datasets. Extensive experiments showed that GSGNet-S* outperformed most advanced methods with only 19.61M parameters and a computation cost of 2.9G FLOPs. The experimental results and code of our network can be accessed at the following URL: https://github.com/LYZ00918/GSGNet-KD.
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