计算机应用与软件2018，Vol.35Issue(2)：1-6,43,7.DOI:10.3969/j.issn.1000-386x.2018.02.001
基于异质多视图主动学习的高光谱地物分类
HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON HETEROGENEOUS MULTI-VIEW ACTIVE LEARNING
摘要
Abstract
Hyperspectral remote sensing images have multi-source, heterogeneous characteristics, but face the problems of less samples and labeling difficulty.This paper intent to extract multiple types of attribute features,including spatial shape and texture,etc.,to construct multi-view and study the heterogeneous multi-view based active learning for hyperspectral image classification.Two main issues were included: 1)a new query strategy based on the minimum posteriori probability difference(MPPD)for multi-view active learning was proposed.Each view was used to predict the conditional probability of each sample according to the multinomial logistical regression classifier; the posterior probability of each sample under the multi-view was calculated according to the full probability formula;the samples with the minimum difference in posterior probability were selected as the most informative ones.2)A heterogeneous view generation strategy was proposed based on the multi-scale spatial shape and texture features.The experimental results showed that the proposed algorithm could speed up the convergence of learning functions and improved the predictive performance of learners with a small number of labeled samples with large information content.关键词
高光谱地物分类/多视图主动学习/多属性形态剖面/GaborKey words
Hyperspectral image classification/Multi-view active learning/Multiple attribute profiles/Gabor分类
信息技术与安全科学引用本文复制引用
姚琼,徐翔,邹昆..基于异质多视图主动学习的高光谱地物分类[J].计算机应用与软件,2018,35(2):1-6,43,7.基金项目
国家自然科学基金项目(61502088) （61502088）
广东省科技计划项目(2013B090500035). （2013B090500035）
