[2603.04720] A Benchmark Study of Neural Network Compression Methods for Hyperspectral Image Classification

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.04720 (cs) [Submitted on 5 Mar 2026] Title:A Benchmark Study of Neural Network Compression Methods for Hyperspectral Image Classification Authors:Sai Shi View a PDF of the paper titled A Benchmark Study of Neural Network Compression Methods for Hyperspectral Image Classification, by Sai Shi View PDF HTML (experimental) Abstract:Deep neural networks have achieved strong performance in image classification tasks due to their ability to learn complex patterns from high-dimensional data. However, their large computational and memory requirements often limit deployment on resource-constrained platforms such as remote sensing devices and edge systems. Network compression techniques have therefore been proposed to reduce model size and computational cost while maintaining predictive performance. In this study, we conduct a systematic evaluation of neural network compression methods for a remote sensing application, namely hyperspectral land cover classification. Specifically, we examine three widely used compression strategies for convolutional neural networks: pruning, quantization, and knowledge distillation. Experiments are conducted on two benchmark hyperspectral datasets, considering classification accuracy, memory consumption, and inference efficiency. Our results demonstrate that compressed models can significantly reduce model size and computational cost while maintaining competitive classification perf...