# Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2103.08741 · HTML: https://mlsanity.com/paper/2103.08741/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 75 references that are in the map.
- First posted on arXiv 15 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [Deep Learning for Hyperspectral Image Classification: An Overview](https://mlsanity.com/paper/1910.12861/) (2019)
- [A Survey of Deep Reinforcement Learning in Video Games](https://mlsanity.com/paper/1912.10944/) (2019)
- [Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery](https://mlsanity.com/paper/1803.02642/) (2018)
- [Deep Learning for Classification of Hyperspectral Data: A Comparative Review](https://mlsanity.com/paper/1904.10674/) (2019)
- [Active Object Localization with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06015/) (2015)
- [Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images](https://mlsanity.com/paper/1812.00645/) (2018)
- [GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection](https://mlsanity.com/paper/1905.01662/) (2019)
- [Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods](https://mlsanity.com/paper/1310.5107/) (2013)
- [Optimal Clustering Framework for Hyperspectral Band Selection](https://mlsanity.com/paper/1904.13036/) (2019)

## 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)
- [LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification](https://mlsanity.com/paper/2404.03883/) (2024)
- [Out-of-distribution detection in satellite image classification](https://mlsanity.com/paper/2104.05442/) (2021)
- [Supervised Embedded Methods for Hyperspectral Band Selection](https://mlsanity.com/paper/2401.11420/) (2024)
- [One-shot neural band selection for spectral recovery](https://mlsanity.com/paper/2305.09236/) (2023)
- [DCP-Net: A Distributed Collaborative Perception Network for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2309.02230/) (2023)
- [Unsupervised Band Selection Using Fused HSI and LiDAR Attention Integrating With Autoencoder](https://mlsanity.com/paper/2404.05258/) (2024)
- [Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class Discovery](https://mlsanity.com/paper/2308.13801/) (2023)
- [BSS-Bench: Towards Reproducible and Effective Band Selection Search](https://mlsanity.com/paper/2312.14570/) (2023)
- [Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral Band Selection](https://mlsanity.com/paper/2406.07949/) (2024)
- [BMCR: Adaptive Backbone Module Composition via Reinforcement Learning for Remote Sensing Object Detection](https://mlsanity.com/paper/2606.05586/) (2026)
- [Prior-aware and Context-guided Group Sampling for Active Probabilistic Subsampling](https://mlsanity.com/paper/2607.07083/) (2026)

## Abstract (arXiv)

Band selection refers to the process of choosing the most relevant bands in a hyperspectral image. By selecting a limited number of optimal bands, we aim at speeding up model training, improving accuracy, or both. It reduces redundancy among spectral bands while trying to preserve the original information of the image. By now many efforts have been made to develop unsupervised band selection approaches, of which the majority are heuristic algorithms devised by trial and error. In this paper, we are interested in training an intelligent agent that, given a hyperspectral image, is capable of automatically learning policy to select an optimal band subset without any hand-engineered reasoning. To this end, we frame the problem of unsupervised band selection as a Markov decision process, propose an effective method to parameterize it, and finally solve the problem by deep reinforcement learning. Once the agent is trained, it learns a band-selection policy that guides the agent to sequentially select bands by fully exploiting the hyperspectral image and previously picked bands. Furthermore, we propose two different reward schemes for the environment simulation of deep reinforcement learning and compare them in experiments. This, to the best of our knowledge, is the first study that explores a deep reinforcement learning model for hyperspectral image analysis, thus opening a new door for future research and showcasing the great potential of deep reinforcement learning in remote sensing applications. Extensive experiments are carried out on four hyperspectral data sets, and experimental results demonstrate the effectiveness of the proposed method.