# Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

arXiv: https://arxiv.org/abs/2101.06116 · HTML: https://mlsanity.com/paper/2101.06116/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 347 references that are in the map.
- First posted on arXiv 15 Jan 2021.

## Builds on (most cited first, 30 of 41)

- [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)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification](https://mlsanity.com/paper/2008.05457/) (2020)
- [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)
- [HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.06701/) (2019)
- [Gradient Centralization: A New Optimization Technique for Deep Neural Networks](https://mlsanity.com/paper/2004.01461/) (2020)
- [An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing](https://mlsanity.com/paper/1810.12000/) (2018)
- [Three dimensional Deep Learning approach for remote sensing image classification](https://mlsanity.com/paper/1806.05824/) (2018)
- [Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)](https://mlsanity.com/paper/2003.02822/) (2020)
- [Cascaded Recurrent Neural Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.10858/) (2019)
- [diffGrad: An Optimization Method for Convolutional Neural Networks](https://mlsanity.com/paper/1909.11015/) (2019)
- [Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods](https://mlsanity.com/paper/1310.5107/) (2013)
- [Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network](https://mlsanity.com/paper/1909.11953/) (2019)
- [ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features](https://mlsanity.com/paper/1901.07925/) (2019)
- [Learnable Manifold Alignment (LeMA) : A Semi-supervised Cross-modality Learning Framework for Land Cover and Land Use Classification](https://mlsanity.com/paper/1901.02838/) (2019)
- [Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network](https://mlsanity.com/paper/1705.00727/) (2017)
- [Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning](https://mlsanity.com/paper/2012.03439/) (2020)
- [CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences](https://mlsanity.com/paper/1812.11501/) (2018)
- [Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification](https://mlsanity.com/paper/1904.02454/) (2019)
- [Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction](https://mlsanity.com/paper/2009.10003/) (2020)
- [Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection](https://mlsanity.com/paper/1905.11074/) (2019)

## Built on by (most cited first, 30 of 49)

- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2501.04944/) (2025)
- [SpectralMamba: Efficient Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.08489/) (2024)
- [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)
- [Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.12003/) (2024)
- [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)
- [Sharpend Cosine Similarity based Neural Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2305.16682/) (2023)
- [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)
- [Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2201.01001/) (2022)
- [Spatial Gated Multi-Layer Perceptron for Land Use and Land Cover Mapping](https://mlsanity.com/paper/2308.05235/) (2023)
- [Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification](https://mlsanity.com/paper/2502.17879/) (2025)
- [3D-Convolution Guided Spectral-Spatial Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.13252/) (2024)
- [GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction](https://mlsanity.com/paper/2507.06806/) (2025)
- [Hyperspectral Benchmark: Bridging the Gap between HSI Applications through Comprehensive Dataset and Pretraining](https://mlsanity.com/paper/2309.11122/) (2023)
- [DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2412.17350/) (2024)
- [Randomized based restricted kernel machine for hyperspectral image classification](https://mlsanity.com/paper/2503.05837/) (2025)
- [Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN](https://mlsanity.com/paper/2101.10532/) (2021)
- [Multimodal Transformer Using Cross-Channel attention for Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2310.13876/) (2023)
- [3D/2D regularized CNN feature hierarchy for Hyperspectral image classification](https://mlsanity.com/paper/2104.12136/) (2021)
- [A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification](https://mlsanity.com/paper/2205.12459/) (2022)
- [Training Methods of Multi-label Prediction Classifiers for Hyperspectral Remote Sensing Images](https://mlsanity.com/paper/2301.06874/) (2023)
- [Unsupervised Band Selection Using Fused HSI and LiDAR Attention Integrating With Autoencoder](https://mlsanity.com/paper/2404.05258/) (2024)
- [Importance of Disjoint Sampling in Conventional and Transformer Models for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.14944/) (2024)
- [Continual Learning for Multimodal Data Fusion of a Soft Gripper](https://mlsanity.com/paper/2409.13792/) (2024)
- [Hybrid State-Space and GRU-based Graph Tokenization Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2502.06427/) (2025)
- [AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution](https://mlsanity.com/paper/2502.06894/) (2025)

## Abstract (arXiv)

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies on the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral features, spatial features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.