FuSENet: Fused squeeze-and-excitation network for spectral-spatial hyperspectral image classification
IET Image Processing
Deep learning-based approaches have become very prominent in recent years due to its outstanding performance as compared to the hand-extracted feature-based methods. Convolutional neural network (CNN) is a type of deep learning architecture to deal with the image/video data. Residual network and squeeze and excitation network (SENet) are among recent developments in CNN for image classification. However, the performance of SENet depends on the squeeze operation done by global pooling, which sometimes may lead to poor performance. In this study, the authors propose a bilinear fusion mechanism over different types of squeeze operation such as global pooling and max pooling. The excitation operation is performed using the fused output of squeeze operation. They used to model the proposed fused SENet with the residual unit and name it as FuSENet. Here the classification experiments are performed over benchmark hyperspectral image datasets. The experimental results confirm the superiority of the proposed FuSENet method with respect to the state-of-the-art methods. The source code of the complete system is made publicly available at https://github.com/swalpa/FuSENet.
Roy, Swalpa Kumar; Dubey, Shiv Ram; Chatterjee, Subhrasankar; and Chaudhuri, Bidyut Baran, "FuSENet: Fused squeeze-and-excitation network for spectral-spatial hyperspectral image classification" (2020). Journal Articles. 241.
Open Access, Bronze