Active multi-kernel domain adaptation for hyperspectral image classification. (May 2018)

• Record Type: Journal Article
• Authors: Deng, Cheng; Liu, Xianglong; Li, Chao; Tao, Dacheng
• Is Part Of: Pattern recognition. Volume 77(2018:May)
• Page Start: 306
• Page End: 315
• Publication Date: 2018-05
• Subjects: Active learning -- Multi-kernel -- Domain adaptation -- Hyperspectral image classification -- Remote sensing; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.10.007
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose a multi-kernel domain adaption framework for HIS classification. The multiple kernels can help capture the connection among domains. The method can adaptively migrate the information across domains. We design a nested actively updating process in the framework. Experiments validate the superior HIS classification performances. 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.