# Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)

arXiv: https://arxiv.org/abs/2003.02822 · HTML: https://mlsanity.com/paper/2003.02822/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 30 of 36)

- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021)
- [Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review](https://mlsanity.com/paper/2205.01380/) (2022)
- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects](https://mlsanity.com/paper/2101.06116/) (2021)
- [Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model](https://mlsanity.com/paper/2105.10196/) (2021)
- [Geographical Knowledge-driven Representation Learning for Remote Sensing Images](https://mlsanity.com/paper/2107.05276/) (2021)
- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models](https://mlsanity.com/paper/2304.05961/) (2023)
- [Deep Hyperspectral Unmixing using Transformer Network](https://mlsanity.com/paper/2203.17076/) (2022)
- [A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models](https://mlsanity.com/paper/2404.14955/) (2024)
- [HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification](https://mlsanity.com/paper/2404.00272/) (2024)
- [Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package](https://mlsanity.com/paper/2308.09375/) (2023)
- [S2Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.18213/) (2024)
- [Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection with an Isolation Forest-Guided Unsupervised Detector](https://mlsanity.com/paper/2209.14971/) (2022)
- [Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2201.01001/) (2022)
- [Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2204.09120/) (2022)
- [SDS-Net: Shallow-Deep Synergism-detection Network for infrared small target detection](https://mlsanity.com/paper/2506.06042/) (2025)
- [Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image](https://mlsanity.com/paper/2104.11904/) (2021)
- [MSHCNet: Multi-Stream Hybridized Convolutional Networks with Mixed Statistics in Euclidean/Non-Euclidean Spaces and Its Application to Hyperspectral Image Classification](https://mlsanity.com/paper/2110.03346/) (2021)
- [The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study](https://mlsanity.com/paper/2104.00788/) (2021)
- [Training Methods of Multi-label Prediction Classifiers for Hyperspectral Remote Sensing Images](https://mlsanity.com/paper/2301.06874/) (2023)
- [A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset](https://mlsanity.com/paper/2407.16384/) (2024)
- [Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2502.19699/) (2025)
- [Hyperspectral Imaging](https://mlsanity.com/paper/2508.08107/) (2025)
- [Absorption-Feature-Guided Distance-Decoupled Estimation and Band Selection for LWIR Hyperspectral Passive Ranging](https://mlsanity.com/paper/2606.31824/) (2026)
- [Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing](https://mlsanity.com/paper/2103.01449/) (2021)
- [Shallow Network Based on Depthwise Over-Parameterized Convolution for Hyperspectral Image Classification](https://mlsanity.com/paper/2112.00250/) (2021)
- [Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification](https://mlsanity.com/paper/2208.02613/) (2022)
- [LUM-ViT: Learnable Under-sampling Mask Vision Transformer for Bandwidth Limited Optical Signal Acquisition](https://mlsanity.com/paper/2403.01412/) (2024)
- [FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo Time-Series Data using Discrete Relaxation](https://mlsanity.com/paper/2403.08403/) (2024)

## Abstract (arXiv)

Hyperspectral images provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands) with continuous spectral information that can accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to the conventional techniques (the so-called curse of dimensionality) for accurate analysis of hyperspectral images. Feature extraction, as a vibrant field of research in the hyperspectral community, evolved through decades of research to address this issue and extract informative features suitable for data representation and classification. The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of feature extraction approaches named shallow and deep techniques. This article outlines the advances in feature extraction approaches for hyperspectral imagery by providing a technical overview of the state-of-the-art techniques, providing useful entry points for researchers at different levels, including students, researchers, and senior researchers, willing to explore novel investigations on this challenging topic. In more detail, this paper provides a bird's eye view over shallow (both supervised and unsupervised) and deep feature extraction approaches specifically dedicated to the topic of hyperspectral feature extraction and its application on hyperspectral image classification. Additionally, this paper compares 15 advanced techniques with an emphasis on their methodological foundations in terms of classification accuracies. Furthermore, the codes and libraries are shared at https://github.com/BehnoodRasti/HyFTech-Hyperspectral-Shallow-Deep-Feature-Extraction-Toolbox.