期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 11, 期 11, 页码 4048-4062出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2018.2874225
关键词
Hyperspectral image (HSI); salient samples; supervised classification; transfer learning
类别
资金
- Qatar National Research Fund (a member of the Qatar Foundation) through the National Priorities Research Program [7-684-1-127]
A hyperspectral image (HSI) includes a vast quantity of samples, a large number of bands, and randomly occurring redundancy. Classifying such complex data is challenging, and its classification performance can be affected significantly by the amount of labeled training samples, as well as the quality, position, and others factors of these samples. Collecting such labeled training samples is labor and time consuming, motivating the idea of taking advantage of labeled samples from other pre-existing related images. Therefore, transfer learning, which can mitigate the semantic gap between existing and new HSIs, has drawn increasing research attention. However, existing transfer learning methods for HSIs (which mainly concentrate on how to overcome the divergence among images) may fail to carefully consider the contents to be transferred and thus limit their performances. In this paper, we present two novel ideas: 1) we, for the first time, introduce an active learning process to initialize the salient samples on the HSI data, which would be transferred later; and 2) we propose constructing and connecting higher level features for the source and target HSI data to further overcome the cross-domain disparity. Different from existing methods, the proposed framework requires no a priori knowledge on the target domain, and it works for both homogeneous and heterogeneous HSI data. Experimental results on three real-world HSIs support the effectiveness of the proposed method for HSI classification.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据