# Sharpend Cosine Similarity based Neural Network for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2305.16682 · HTML: https://mlsanity.com/paper/2305.16682/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 4 of 4)

- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [Deep Learning for Hyperspectral Image Classification: An Overview](https://mlsanity.com/paper/1910.12861/) (2019)
- [Graph Convolutional Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2008.02457/) (2020)
- [Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects](https://mlsanity.com/paper/2101.06116/) (2021)

## Built on by (most cited first, 7 of 7)

- [Pyramid Hierarchical Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.14945/) (2024)
- [WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01231/) (2024)
- [Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01224/) (2024)
- [Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.01095/) (2024)
- [Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01372/) (2024)
- [Hybrid State-Space and GRU-based Graph Tokenization Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2502.06427/) (2025)
- [Deceptive Risk Minimization: Out-of-Distribution Generalization by Deceiving Distribution Shift Detectors](https://mlsanity.com/paper/2509.12081/) (2025)

## Abstract (arXiv)

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.