Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification

TL;DR

This paper introduces an active multi-kernel domain adaptation framework for hyperspectral image classification that effectively leverages labeled source data and selectively queries informative target samples to improve accuracy.

Contribution

It proposes a novel adaptive multi-kernel domain adaptation method combined with active learning, specifically using margin sampling, for hyperspectral image classification.

Findings

The method outperforms existing approaches on two HSI datasets.

Active learning reduces the number of labeled samples needed.

The adaptive multi-kernel approach effectively minimizes domain bias.

Abstract

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.