Remote sensing image scene classification using deep combinative feature learning

Lei Min; Kun Gao; Hong Wang; Junwei Wang; Peilin Yu; Ting Li; Zhuoyi Chen

doi:10.1117/12.2579961

5 November 2020 Remote sensing image scene classification using deep combinative feature learning

Lei Min, Kun Gao, Hong Wang, Junwei Wang, Peilin Yu, Ting Li, Zhuoyi Chen

Proceedings Volume 11567, AOPC 2020: Optical Sensing and Imaging Technology; 115672N (2020) https://doi.org/10.1117/12.2579961
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

Scene classification shows pivotal role in remote sensing image researches. Since challenges of large similarity between classes, high diversity in each class and huge variations in background, spatial resolution, translation, etc., remote sensing image scene classification still urgently need development. In this paper, we propose a novel method named deep combinative feature learning (DCFL) to extract low-level texture and high-level semantic information from different network layers. First, feature encoder VGGNet-16 is fine-tuned for subsequent multi-scale feature extraction. And two shallow convolutional (Conv) layers are selected for convolutional feature summing maps (CFSM), from which we extract uniform LBP with rotation invariance to excavate detailed texture. Deep semantic features from fully-connected (FC) layer concatenated with shallow detailed features constitute deep combinative features, which are thrown into support vector machine (SVM) classifier for final classification. Extensive experiments are carried out and results prove the comparable advantages and effectiveness of the proposed DCFL contrasting with different state-of-art methods.

Citation Download Citation

Lei Min, Kun Gao, Hong Wang, Junwei Wang, Peilin Yu, Ting Li, and Zhuoyi Chen "Remote sensing image scene classification using deep combinative feature learning", Proc. SPIE 11567, AOPC 2020: Optical Sensing and Imaging Technology, 115672N (5 November 2020); https://doi.org/10.1117/12.2579961

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