Article · IEEE Geoscience and Remote Sensing Letters · January 1, 2024

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

Muhammad Imran Ahmad, Manuel Mazzara

Abstract

Hyperspectral image classification (HSIC) faces challenges in preserving high-frequency features during downsampling 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.

Remote-Sensing Image ClassificationAdvanced Neural Network ApplicationsSpectroscopy and Chemometric Analyseshyperspectral image classificationparameter-efficient neural networksdownsampling

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

Abstract

More by Manuel Mazzara