SCSNet: Sharpened Cosine Similarity-Based Neural Network for Hyperspectral Image Classification

Hyperspectral image classification (HSIC) faces challenges in preserving high-frequency features during down-sampling and hierarchical filtering in the CNN architecture. To overcome this, we propose sharpened cosine similarity (SCS) as an alternative to convolutions within a neural network for HSIC. SCSNet emphasizes parameter efficiency by bypassing nonlinear activation layers, normalization steps, and dropout post the SCS layer. Additionally, MaxAbsPool is implemented instead of MaxPool for superior performance. Experimental results on public HSI datasets demonstrate SCS's comparable accuracy, achieving 99% for both Indian Pines and Salinas datasets.