A semantic-driven image scene fine-grained enhancement recognition

Dongyang Qu; Yaling Li; Xiaoyan Luo; Xiaofeng Shi

doi:10.1117/12.2617708

15 February 2022 A semantic-driven image scene fine-grained enhancement recognition

Dongyang Qu, Yaling Li, Xiaoyan Luo, Xiaofeng Shi

Proceedings Volume 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021); 1216660 (2022) https://doi.org/10.1117/12.2617708
Event: Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 2021, Hong Kong, Hong Kong

Abstract

Scene classification for Remote sensing image has attracted great attention because of its difficulties and wide application. There exits several limitations for traditional CNN-based methods, such as insufficient feature extraction ability and complex target of remote sensing image features. In addition, the experimental data is based on the overhead view, which is characterized by fuzzy semantics, small differences between classes and significant differences within classes. To address those issues, we realize several classic network improvement methods such as transfer learning and introduce the attention mechanism Squeeze-and-Excitation (SE) module. We carry out the fine-grained analysis of the space-based view scene image, specifically using the progressive multi-granularity puzzle training for scene recognition. We also propose a semantic-driven scene fine-grained enhancement based on the classic classification network and the progressive multi-granularity puzzle training. To verify the effectiveness of the proposed semantic-driven scene fine-grained enhancement model, we conduct comparative experiments based on several widely used CNN models and a public remote sensing image scene classification data set, and achieve the state-of-the-art result on the data set.

Citation Download Citation

Dongyang Qu, Yaling Li, Xiaoyan Luo, and Xiaofeng Shi "A semantic-driven image scene fine-grained enhancement recognition", Proc. SPIE 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 1216660 (15 February 2022); https://doi.org/10.1117/12.2617708

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KEYWORDS

RGB color model

Image enhancement

Image classification

Classification systems

Data modeling

Scene classification

Remote sensing

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