# Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/1904.05200 · HTML: https://mlsanity.com/paper/1904.05200/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 12 of 12)

- [Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects](https://mlsanity.com/paper/2101.06116/) (2021)
- [A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models](https://mlsanity.com/paper/2404.14955/) (2024)
- [Discrepancy-Based Active Learning for Domain Adaptation](https://mlsanity.com/paper/2103.03757/) (2021)
- [MLAN: Multi-Level Adversarial Network for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2103.12991/) (2021)
- [When Deep Learners Change Their Mind: Learning Dynamics for Active Learning](https://mlsanity.com/paper/2107.14707/) (2021)
- [Burn After Reading: Online Adaptation for Cross-domain Streaming Data](https://mlsanity.com/paper/2112.04345/) (2021)
- [Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image](https://mlsanity.com/paper/2104.11904/) (2021)
- [EiHi Net: Out-of-Distribution Generalization Paradigm](https://mlsanity.com/paper/2209.14946/) (2022)
- [AdaTreeFormer: Few Shot Domain Adaptation for Tree Counting from a Single High-Resolution Image](https://mlsanity.com/paper/2402.02956/) (2024)
- [Domain Adaptation for Big Data in Agricultural Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2506.05972/) (2025)
- [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)

Recent years have witnessed the quick progress of the hyperspectral images (HSI) classification. Most of existing studies either heavily rely on the expensive label information using the supervised learning or can hardly exploit the discriminative information borrowed from related domains. To address this issues, in this paper we show a novel framework addressing HSI classification based on the domain adaptation (DA) with active learning (AL). The main idea of our method is to retrain the multi-kernel classifier by utilizing the available labeled samples from source domain, and adding minimum number of the most informative samples with active queries in the target domain. The proposed method adaptively combines multiple kernels, forming a DA classifier that minimizes the bias between the source and target domains. Further equipped with the nested actively updating process, it sequentially expands the training set and gradually converges to a satisfying level of classification performance. We study this active adaptation framework with the Margin Sampling (MS) strategy in the HSI classification task. Our experimental results on two popular HSI datasets demonstrate its effectiveness.