# BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/1612.00144 · HTML: https://mlsanity.com/paper/1612.00144/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2016.

## Built on by (most cited first, 5 of 5)

- [DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2304.09915/) (2023)
- [Prototype-Based Methods in Explainable AI and Emerging Opportunities in the Geosciences](https://mlsanity.com/paper/2410.19856/) (2024)
- [Physics-Informed Spectral Modeling for Hyperspectral Imaging](https://mlsanity.com/paper/2508.21618/) (2025)
- [Decision Tree Learning with Spatial Modal Logics](https://mlsanity.com/paper/2109.08325/) (2021)
- [Feature selection simultaneously preserving both class and cluster structures](https://mlsanity.com/paper/2307.03902/) (2023)

## Abstract (arXiv)

Deep learning based landcover classification algorithms have recently been proposed in literature. In hyperspectral images (HSI) they face the challenges of large dimensionality, spatial variability of spectral signatures and scarcity of labeled data. In this article we propose an end-to-end deep learning architecture that extracts band specific spectral-spatial features and performs landcover classification. The architecture has fewer independent connection weights and thus requires lesser number of training data. The method is found to outperform the highest reported accuracies on popular hyperspectral image data sets.