# Hyperspectral Benchmark: Bridging the Gap between HSI Applications through Comprehensive Dataset and Pretraining

arXiv: https://arxiv.org/abs/2309.11122 · HTML: https://mlsanity.com/paper/2309.11122/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 20 Sep 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021)
- [Deep Learning for Hyperspectral Image Classification: An Overview](https://mlsanity.com/paper/1910.12861/) (2019)
- [HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.06701/) (2019)
- [Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects](https://mlsanity.com/paper/2101.06116/) (2021)
- [SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification](https://mlsanity.com/paper/2104.00341/) (2021)
- [Measuring the Ripeness of Fruit with Hyperspectral Imaging and Deep Learning](https://mlsanity.com/paper/2104.09808/) (2021)
- [Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders](https://mlsanity.com/paper/1511.02916/) (2015)
- [Wavelength-aware 2D Convolutions for Hyperspectral Imaging](https://mlsanity.com/paper/2209.03136/) (2022)
- [Cross-domain CNN for Hyperspectral Image Classification](https://mlsanity.com/paper/1802.00093/) (2018)
- [Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2204.03144/) (2022)
- [Is Pretraining Necessary for Hyperspectral Image Classification?](https://mlsanity.com/paper/1901.08658/) (2019)

## Built on by (most cited first, 3 of 3)

- [SSFT: A Lightweight Spectral-Spatial Fusion Transformer for Generic Hyperspectral Classification](https://mlsanity.com/paper/2604.15828/) (2026)
- [IonMorphNet: Generalizable Learning of Ion Image Morphologies for Peak Picking in Mass Spectrometry Imaging](https://mlsanity.com/paper/2604.19369/) (2026)
- [Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction](https://mlsanity.com/paper/2608.01202/) (2026)

## Abstract (arXiv)

Hyperspectral Imaging (HSI) serves as a non-destructive spatial spectroscopy technique with a multitude of potential applications. However, a recurring challenge lies in the limited size of the target datasets, impeding exhaustive architecture search. Consequently, when venturing into novel applications, reliance on established methodologies becomes commonplace, in the hope that they exhibit favorable generalization characteristics. Regrettably, this optimism is often unfounded due to the fine-tuned nature of models tailored to specific HSI contexts. To address this predicament, this study introduces an innovative benchmark dataset encompassing three markedly distinct HSI applications: food inspection, remote sensing, and recycling. This comprehensive dataset affords a finer assessment of hyperspectral model capabilities. Moreover, this benchmark facilitates an incisive examination of prevailing state-of-the-art techniques, consequently fostering the evolution of superior methodologies. Furthermore, the enhanced diversity inherent in the benchmark dataset underpins the establishment of a pretraining pipeline for HSI. This pretraining regimen serves to enhance the stability of training processes for larger models. Additionally, a procedural framework is delineated, offering insights into the handling of applications afflicted by limited target dataset sizes.