# BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral Image

arXiv: https://arxiv.org/abs/1904.08269 · HTML: https://mlsanity.com/paper/1904.08269/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 14 of 14)

- [LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification](https://mlsanity.com/paper/2404.03883/) (2024)
- [Large-Scale Hyperspectral Image Clustering Using Contrastive Learning](https://mlsanity.com/paper/2111.07945/) (2021)
- [Learning Channel Importance for High Content Imaging with Interpretable Deep Input Channel Mixing](https://mlsanity.com/paper/2308.16637/) (2023)
- [Triplet-Watershed for Hyperspectral Image Classification](https://mlsanity.com/paper/2103.09384/) (2021)
- [OSTA: One-shot Task-adaptive Channel Selection for Semantic Segmentation of Multichannel Images](https://mlsanity.com/paper/2305.04766/) (2023)
- [Supervised Embedded Methods for Hyperspectral Band Selection](https://mlsanity.com/paper/2401.11420/) (2024)
- [SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights](https://mlsanity.com/paper/2507.04412/) (2025)
- [Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and Clustering](https://mlsanity.com/paper/2111.07942/) (2021)
- [Dropout Concrete Autoencoder for Band Selection on HSI Scenes](https://mlsanity.com/paper/2401.16522/) (2024)
- [Subspace Clustering of Subspaces: Unifying Canonical Correlation Analysis and Subspace Clustering](https://mlsanity.com/paper/2509.18653/) (2025)
- [Representative Spectral Correlation Network for Multi-source Remote Sensing Image Classification](https://mlsanity.com/paper/2604.27323/) (2026)
- [Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral Band Selection](https://mlsanity.com/paper/2406.07949/) (2024)
- [Optimal Hyperspectral Undersampling Strategy for Satellite Imaging](https://mlsanity.com/paper/2504.19279/) (2025)
- [Adaptive Band Selection for Hyperspectral Classification with Spatially Disjoint Evaluation](https://mlsanity.com/paper/2606.06684/) (2026)

## Abstract (arXiv)

Hyperspectral image (HSI) consists of hundreds of continuous narrow bands with high spectral correlation, which would lead to the so-called Hughes phenomenon and the high computational cost in processing. Band selection has been proven effective in avoiding such problems by removing the redundant bands. However, many of existing band selection methods separately estimate the significance for every single band and cannot fully consider the nonlinear and global interaction between spectral bands. In this paper, by assuming that a complete HSI can be reconstructed from its few informative bands, we propose a general band selection framework, Band Selection Network (termed as BS-Net). The framework consists of a band attention module (BAM), which aims to explicitly model the nonlinear inter-dependencies between spectral bands, and a reconstruction network (RecNet), which is used to restore the original HSI cube from the learned informative bands, resulting in a flexible architecture. The resulting framework is end-to-end trainable, making it easier to train from scratch and to combine with existing networks. We implement two BS-Nets respectively using fully connected networks (BS-Net-FC) and convolutional neural networks (BS-Net-Conv), and compare the results with many existing band selection approaches for three real hyperspectral images, demonstrating that the proposed BS-Nets can accurately select informative band subset with less redundancy and achieve significantly better classification performance with an acceptable time cost.