# Deep Learning Techniques for Hyperspectral Image Analysis in Agriculture: A Review

arXiv: https://arxiv.org/abs/2304.13880 · HTML: https://mlsanity.com/paper/2304.13880/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 229 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Less is more: sampling chemical space with active learning](https://mlsanity.com/paper/1801.09319/) (2018)
- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network](https://mlsanity.com/paper/1806.00183/) (2018)
- [Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning](https://mlsanity.com/paper/1703.06452/) (2017)
- [A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2111.08685/) (2021)
- [Hyperspectral Anomaly Change Detection Based on Auto-encoder](https://mlsanity.com/paper/2010.14119/) (2020)
- [Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders](https://mlsanity.com/paper/1511.02916/) (2015)
- [Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/1703.07910/) (2017)
- [Identifying Wrongly Predicted Samples: A Method for Active Learning](https://mlsanity.com/paper/2010.06890/) (2020)
- [FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images](https://mlsanity.com/paper/1908.00671/) (2019)
- [Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse Representations](https://mlsanity.com/paper/2103.07437/) (2021)
- [D-TrAttUnet: Dual-Decoder Transformer-Based Attention Unet Architecture for Binary and Multi-classes Covid-19 Infection Segmentation](https://mlsanity.com/paper/2303.15576/) (2023)

## Built on by (most cited first, 12 of 12)

- [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)
- [Hyperspectral Adapter for Semantic Segmentation with Vision Foundation Models](https://mlsanity.com/paper/2509.20107/) (2025)
- [Transformer-Driven Active Transfer Learning for Cross-Hyperspectral Image Classification](https://mlsanity.com/paper/2411.18115/) (2024)
- [HyperCap: Hyperspectral Land Cover Captioning Dataset for Vision Language Models](https://mlsanity.com/paper/2505.12217/) (2025)
- [Advancing Wheat Crop Analysis: A Survey of Deep Learning Approaches Using Hyperspectral Imaging](https://mlsanity.com/paper/2505.00805/) (2025)
- [AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution](https://mlsanity.com/paper/2502.06894/) (2025)
- [Boosting Hyperspectral Image Classification with Gate-Shift-Fuse Mechanisms in a Novel CNN-Transformer Approach](https://mlsanity.com/paper/2406.14120/) (2024)
- [Hyperspectral Unmixing of Agricultural Images taken from UAV Using Adapted U-Net Architecture](https://mlsanity.com/paper/2409.19701/) (2024)
- [Multispectral Texture Synthesis using RGB Convolutional Neural Networks](https://mlsanity.com/paper/2410.16019/) (2024)
- [A Data-Driven Review of Remote Sensing-Based Data Fusion in Precision Agriculture from Foundational to Transformer-Based Techniques](https://mlsanity.com/paper/2410.18353/) (2024)
- [SpectralCA: Bi-Directional Cross-Attention for Next-Generation UAV Hyperspectral Vision](https://mlsanity.com/paper/2510.09912/) (2025)

## Abstract (arXiv)

In the recent years, hyperspectral imaging (HSI) has gained considerably popularity among computer vision researchers for its potential in solving remote sensing problems, especially in agriculture field. However, HSI classification is a complex task due to the high redundancy of spectral bands, limited training samples, and non-linear relationship between spatial position and spectral bands. Fortunately, deep learning techniques have shown promising results in HSI analysis. This literature review explores recent applications of deep learning approaches such as Autoencoders, Convolutional Neural Networks (1D, 2D, and 3D), Recurrent Neural Networks, Deep Belief Networks, and Generative Adversarial Networks in agriculture. The performance of these approaches has been evaluated and discussed on well-known land cover datasets including Indian Pines, Salinas Valley, and Pavia University.