Hyperspectral Image Classification (HSIC) is a difficult task due to high inter and intra-class similarity and variability, nested regions, and overlapping. 2D Convolutional Neural Networks (CNN) emerged as a viable network whereas, 3D CNNs are a better alternative due to accurate classification. However, 3D CNNs are highly computationally complex due to their volume and spectral dimensions. Moreover, down-sampling and hierarchical filtering (high frequency) i.e., texture features need to be smoothed during the forward pass which is crucial for accurate HSIC. Furthermore, CNN requires tons of tuning parameters which increases the training time. Therefore, to overcome the aforesaid issues, Sharpened Cosine Similarity (SCS) concept as an alternative to convolutions in a Neural Network for HSIC is introduced. SCS is exceptionally parameter efficient due to skipping the non-linear activation layers, normalization, and dropout after the SCS layer. Use of MaxAbsPool instead of MaxPool which selects the element with the highest magnitude of activity, even if it's negative. Experimental results on publicly available HSI datasets proved the performance of SCS as compared to the convolutions in Neural Networks.

翻译：高光谱图像分类（HSIC）是一项具有挑战性的任务，原因在于其类间与类内相似性和变异性高、区域嵌套以及重叠现象。二维卷积神经网络（CNN）被证明是一种可行的网络结构，而三维CNN由于分类精度更高，成为更优的选择。然而，三维CNN因其体积和光谱维度而具有极高的计算复杂度。此外，下采样和分层滤波（高频滤波）即纹理特征在前向传播过程中需要平滑处理，这对准确的HSIC至关重要。同时，CNN需要大量调优参数，这增加了训练时间。因此，为解决上述问题，本文引入了锐化余弦相似度（SCS）概念，作为神经网络中卷积的替代方案用于HSIC。SCS由于跳过了SCS层后的非线性激活层、归一化和丢弃操作，因而具有极高的参数效率。使用MaxAbsPool替代MaxPool，前者能选择具有最高幅度的元素，即使该元素为负值。在公开可用的高光谱数据集上的实验结果证明了与神经网络中的卷积相比，SCS的性能优越性。