# Dropout Concrete Autoencoder for Band Selection on HSI Scenes

arXiv: https://arxiv.org/abs/2401.16522 · HTML: https://mlsanity.com/paper/2401.16522/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [A* Sampling](https://mlsanity.com/paper/1411.0030/) (2014)
- [Concrete Autoencoders for Differentiable Feature Selection and Reconstruction](https://mlsanity.com/paper/1901.09346/) (2019)
- [Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods](https://mlsanity.com/paper/1310.5107/) (2013)
- [Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Image](https://mlsanity.com/paper/2004.10476/) (2020)
- [BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral Image](https://mlsanity.com/paper/1904.08269/) (2019)
- [Dropout Feature Ranking for Deep Learning Models](https://mlsanity.com/paper/1712.08645/) (2017)
- [Optimal Clustering Framework for Hyperspectral Band Selection](https://mlsanity.com/paper/1904.13036/) (2019)
- [Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks](https://mlsanity.com/paper/2106.00645/) (2021)
- [SRL-SOA: Self-Representation Learning with Sparse 1D-Operational Autoencoder for Hyperspectral Image Band Selection](https://mlsanity.com/paper/2202.09918/) (2022)

## Built on by (most cited first, 1 of 1)

- [Adaptive Band Selection for Hyperspectral Classification with Spatially Disjoint Evaluation](https://mlsanity.com/paper/2606.06684/) (2026)

## Abstract (arXiv)

Deep learning-based informative band selection methods on hyperspectral images (HSI) recently have gained intense attention to eliminate spectral correlation and redundancies. However, the existing deep learning-based methods either need additional post-processing strategies to select the descriptive bands or optimize the model indirectly, due to the parameterization inability of discrete variables for the selection procedure. To overcome these limitations, this work proposes a novel end-to-end network for informative band selection. The proposed network is inspired by the advances in concrete autoencoder (CAE) and dropout feature ranking strategy. Different from the traditional deep learning-based methods, the proposed network is trained directly given the required band subset eliminating the need for further post-processing. Experimental results on four HSI scenes show that the proposed dropout CAE achieves substantial and effective performance levels outperforming the competing methods.