# Supervised Embedded Methods for Hyperspectral Band Selection

arXiv: https://arxiv.org/abs/2401.11420 · HTML: https://mlsanity.com/paper/2401.11420/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 21 Jan 2024.

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

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
- [Concrete Autoencoders for Differentiable Feature Selection and Reconstruction](https://mlsanity.com/paper/1901.09346/) (2019)
- [Three dimensional Deep Learning approach for remote sensing image classification](https://mlsanity.com/paper/1806.05824/) (2018)
- [Deep Learning for Classification of Hyperspectral Data: A Comparative Review](https://mlsanity.com/paper/1904.10674/) (2019)
- [HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image](https://mlsanity.com/paper/1802.10478/) (2018)
- [BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral Image](https://mlsanity.com/paper/1904.08269/) (2019)
- [Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification](https://mlsanity.com/paper/2103.08741/) (2021)
- [Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image](https://mlsanity.com/paper/2012.10122/) (2020)

## Built on by (most cited first, 2 of 2)

- [Vision-Language Guided Hyperspectral Object Tracking via Semantics Fusion and Contextual Template Updating](https://mlsanity.com/paper/2606.09167/) (2026)
- [Band-Selection Stability and Semantic Segmentation Performance: A Study on Hyperspectral City](https://mlsanity.com/paper/2609.31074/) (2026)

## Abstract (arXiv)

Hyperspectral Imaging (HSI) captures rich spectral information across contiguous wavelength bands, supporting applications in precision agriculture, environmental monitoring, and autonomous driving. However, its high dimensionality poses computational challenges, particularly in real-time or resource-constrained settings. While prior band selection methods attempt to reduce complexity, they often rely on separate preprocessing steps and lack alignment with downstream tasks. We propose two novel supervised, embedded methods for task-specific HSI band selection that integrate directly into deep learning models. By embedding band selection within the training pipeline, our methods eliminate the need for separate preprocessing and ensure alignment with the target task. Extensive experiments on three remote sensing benchmarks and an autonomous driving dataset show that our methods achieve state-of-the-art performance while selecting only a minimal number of bands. These results highlight the potential of efficient, task-specific HSI pipelines for practical deployment.