# Cross-domain CNN for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/1802.00093 · HTML: https://mlsanity.com/paper/1802.00093/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2018.

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

- [Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2204.03144/) (2022)
- [Self-supervised Contrastive Learning for Cross-domain Hyperspectral Image Representation](https://mlsanity.com/paper/2202.03968/) (2022)
- [Hyperspectral Benchmark: Bridging the Gap between HSI Applications through Comprehensive Dataset and Pretraining](https://mlsanity.com/paper/2309.11122/) (2023)
- [Enhancing Knowledge Transfer in Hyperspectral Image Classification via Cross-scene Knowledge Integration](https://mlsanity.com/paper/2512.08989/) (2025)
- [Agreement Disagreement Guided Knowledge Transfer for Cross-Scene Hyperspectral Imaging](https://mlsanity.com/paper/2512.08990/) (2025)

## Abstract (arXiv)

In this paper, we address the dataset scarcity issue with the hyperspectral image classification. As only a few thousands of pixels are available for training, it is difficult to effectively learn high-capacity Convolutional Neural Networks (CNNs). To cope with this problem, we propose a novel cross-domain CNN containing the shared parameters which can co-learn across multiple hyperspectral datasets. The network also contains the non-shared portions designed to handle the dataset specific spectral characteristics and the associated classification tasks. Our approach is the first attempt to learn a CNN for multiple hyperspectral datasets, in an end-to-end fashion. Moreover, we have experimentally shown that the proposed network trained on three of the widely used datasets outperform all the baseline networks which are trained on single dataset.