Optimizing Remote Sensing Image Scene Classification Through Brain-Inspired Feature Bias Estimation and Semantic Representation Analysis
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 classif...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10092750/ |
| _version_ | 1797848285990354944 |
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| author | Zhong Dong Baojun Lin Fang Xie |
| author_facet | Zhong Dong Baojun Lin Fang Xie |
| author_sort | Zhong Dong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T18:25:40Z |
| format | Article |
| id | doaj.art-9f99ef242f8948808b2133ae024bc67a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T18:25:40Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9f99ef242f8948808b2133ae024bc67a2023-04-11T23:00:16ZengIEEEIEEE Access2169-35362023-01-0111347643477110.1109/ACCESS.2023.326450210092750Optimizing Remote Sensing Image Scene Classification Through Brain-Inspired Feature Bias Estimation and Semantic Representation AnalysisZhong Dong0https://orcid.org/0000-0003-0523-8651Baojun Lin1Fang Xie2Department of Automation, Tsinghua University, Beijing, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaIn 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.https://ieeexplore.ieee.org/document/10092750/Remote sensing imagescene classificationbrain-inspired learningfeature biasdata enhancement |
| spellingShingle | Zhong Dong Baojun Lin Fang Xie Optimizing Remote Sensing Image Scene Classification Through Brain-Inspired Feature Bias Estimation and Semantic Representation Analysis IEEE Access Remote sensing image scene classification brain-inspired learning feature bias data enhancement |
| title | Optimizing Remote Sensing Image Scene Classification Through Brain-Inspired Feature Bias Estimation and Semantic Representation Analysis |
| title_full | Optimizing Remote Sensing Image Scene Classification Through Brain-Inspired Feature Bias Estimation and Semantic Representation Analysis |
| title_fullStr | Optimizing Remote Sensing Image Scene Classification Through Brain-Inspired Feature Bias Estimation and Semantic Representation Analysis |
| title_full_unstemmed | Optimizing Remote Sensing Image Scene Classification Through Brain-Inspired Feature Bias Estimation and Semantic Representation Analysis |
| title_short | Optimizing Remote Sensing Image Scene Classification Through Brain-Inspired Feature Bias Estimation and Semantic Representation Analysis |
| title_sort | optimizing remote sensing image scene classification through brain inspired feature bias estimation and semantic representation analysis |
| topic | Remote sensing image scene classification brain-inspired learning feature bias data enhancement |
| url | https://ieeexplore.ieee.org/document/10092750/ |
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