Earticle

초록

영어

High-resolution remote sensing image scene classification is a challenging visual task, and this study proposes a remote sensing image scene classification method based on Semantic Multi-Granularity Feature Learning Network (SMGFL-Net). The core idea is to learn global and multi-granularity local features from rearranged intermediate feature mappings, thus eliminating meaningless edges. These features are then fused into the final prediction. Through comparative studies, SMGFL-Net consistently outperforms other peer methods in terms of classification accuracy.

목차

Abstract
1. Introduction
2. This Theory
3. Conclusion
Funding
References

저자

• 마흔의 [ Ma, Xinyi | 학생회원ㆍ성균관대학교 건설환경시스템공학과 석사과정 ]
• 윤홍식 [ Yun, Hong-Sic | 정회원ㆍ성균관대학교 건설환경공학과부 정교수 ]
• 이승준 [ Lee, Seung-Jun | 학생회원ㆍ성균관대학교 건설환경시스템공학과 박사과정 ]

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