基于锐化余弦相似度的神经网络用于高光谱图像分类
Sharpend Cosine Similarity based Neural Network for Hyperspectral Image Classification
AI总结：
针对高光谱图像分类中三维CNN计算复杂、参数多的问题，提出用锐化余弦相似度替代卷积，并配合MaxAbsPool，在公开数据集上验证了其高效分类性能。
摘要：
高光谱图像分类（HSIC）是一项困难的任务，因为类间和类内相似性与变异性高、存在嵌套区域和重叠。二维卷积神经网络（CNN）成为一种可行的网络，而三维CNN由于分类准确，是更好的替代方案。然而，三维CNN由于其体积和光谱维度而具有很高的计算复杂度。此外，下采样和层次滤波（高频）即纹理特征需要在前向传播过程中进行平滑处理，这对准确的HSIC至关重要。而且，CNN需要大量调优参数，这增加了训练时间。因此，为了克服上述问题，引入了锐化余弦相似度（SCS）概念，作为神经网络中用于HSIC的卷积的替代方案。SCS由于在SCS层之后跳过了非线性激活层、归一化和dropout，因此参数效率极高。使用MaxAbsPool代替MaxPool，它选择活动幅度最高的元素，即使该元素为负。在公开可用的HSI数据集上的实验结果证明了SCS相较于神经网络中卷积的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
