BiShuffleNeXt: A lightweight bi-path network for remote sensing scene classification. (15th March 2023) - Chen, Zhichao; Yang, Jie; Feng, Zhicheng; Chen, Lifang; Li, Linhong
Abstract: To address the problem of excessive computation in the remote sensing scene classification (RSSC) algorithms, this paper proposes an efficient RSSC model, which is named BiShuffleNeXt. Firstly, we use the sandglass bottleneck to design the ShuffleNeXt as a context path for deep semantic information extraction. Secondly, the spatial path consists of three sandglass bottlenecks, each with a stride of 2, to retain more spatial information and obtain a high-resolution feature map. Finally, the feature combination module is designed to perform the fusion of bi-path features. Experimental results show that the proposed model achieves a classification accuracy of 95.93% on the NWPU-45 dataset, which is a 3.6% improvement over using the context path alone. The proposed method is tested on three datasets and outperforms ShuffleNet v2, MobileNetv2, Mobile-Former and ResNet-50 respectively. The floating point operations (FLOPs) of BiShuffleNeXt are only 298.25M, which greatly reduces the calculation amount while ensuring classification accuracy. Graphical abstract: Highlights: A deep learning model named BiShuffleNeXt is proposed for efficient remote sensing scene classification. Based on lightweight neural networks to ensure real-time inference of the model. The proposed model separates the extraction of spatial and context information and deep semantic information of remote sensing images into two paths.