Optimizing Remote Sensing Image Scene Classification Through Brain-Inspired Feature Bias Estimation and Semantic Representation Analysis

doi:10.1109/ACCESS.2023.3264502

	Optimizing Remote Sensing Image Scene Classification Through Brain-Inspired Feature Bias Estimation and Semantic Representation Analysis
	Zhong Dong 1,2; Baojun Lin3,4,5,6 ; Fang Xie 2,3,4,5,6
	2023
发表期刊	IEEE ACCESS (IF:3.4[JCR-2023],3.7[5-Year])
ISSN	2169-3536
卷号	11 页码:34764-34771
发表状态	已发表
DOI	10.1109/ACCESS.2023.3264502
摘要	In the realm of remote sensing image classification and detection, deep learning has emerged as a highly effective approach, owing to the remarkable advancements in object perception models and the availability of abundant annotated data. Nevertheless, for specific remote sensing image scene classification tasks, obtaining diverse and large amounts of data remains a daunting challenge, leading to limitations in the applicability of trained models. Consequently, researchers are increasingly focusing on optimal data utilization and interpretability of learning. Drawing inspiration from brain neural perception research, researchers have proposed novel approaches for deeper interpretation and optimization of deep learning models from diverse perspectives. In this paper, we present a brain-inspired network optimization model for remote sensing image scene classification, which considers both shape and texture features and reconstructs feature scaling of data through feature bias estimation. The model achieves greater robustness through complementary training. We evaluate our optimized model on general datasets by integrating it into an existing benchmark method and compare its performance with the original approach. Our results demonstrate that the proposed model is highly effective, with dynamically reconstructed data leading to a significant enhancement of model learning.
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收录类别	SCI ; EI
来源库	IEEE
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/299833
专题	信息科学与技术学院信息科学与技术学院_特聘教授组_林宝军组
作者单位	1.Department of Automation, Tsinghua University, Beijing, China 2.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China 3.University of Chinese Academy of Sciences, Beijing, China 4.Innovation Academy for Microsatellites, Chinese Academy of Sciences, Shanghai, China 5.Shanghai Engineering Center for Microsatellites, Shanghai, China 6.School of Information Science and Technology, ShanghaiTech University, Shanghai, China
推荐引用方式 GB/T 7714	Zhong Dong,Baojun Lin,Fang Xie. Optimizing Remote Sensing Image Scene Classification Through Brain-Inspired Feature Bias Estimation and Semantic Representation Analysis[J]. IEEE ACCESS,2023,11:34764-34771.
APA	Zhong Dong,Baojun Lin,&Fang Xie.(2023).Optimizing Remote Sensing Image Scene Classification Through Brain-Inspired Feature Bias Estimation and Semantic Representation Analysis.IEEE ACCESS,11,34764-34771.
MLA	Zhong Dong,et al."Optimizing Remote Sensing Image Scene Classification Through Brain-Inspired Feature Bias Estimation and Semantic Representation Analysis".IEEE ACCESS 11(2023):34764-34771.