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 .

中文翻译：

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FuSENet：融合的挤压和激励网络，用于光谱空间高光谱图像分类

近年来，基于深度学习的方法由于与基于特征的手工提取方法相比性能卓越而变得非常重要。卷积神经网络（CNN）是一种用于处理图像/视频数据的深度学习架构。残差网络，挤压和激励网络（SENet）是CNN中用于图像分类的最新进展。但是，SENet的性能取决于全局池完成的压缩操作，这有时可能会导致性能下降。在这项研究中，作者提出了在不同类型的挤压操作（例如全局池和最大池）上的双线性融合机制。使用挤压操作的融合输出执行励磁操作。他们使用剩余单元对建议的融合SENet进行建模，并将其命名为富信 。在这里，分类实验是在基准高光谱图像数据集上执行的。实验结果证实了所提方法的优越性。富信相对于最新方法的方法。完整系统的源代码在以下位置公开可用https://github.com/swalpa/FuSENet 。