A class distribution learning method for few-shot remote sensing scene classification

IF 1.4 4区地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

Remote Sensing Letters Pub Date : 2024-05-06 DOI:10.1080/2150704x.2024.2347523

Ming Zhao, Yang Liu

引用次数: 0

Abstract

In recent years, few-shot remote sensing scene classification has become an important foundational research topic, aiming to achieve better classification performance with limited labeled data. Due...

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用于少镜头遥感场景分类的类分布学习方法

近年来，少镜头遥感场景分类已成为一个重要的基础研究课题，旨在利用有限的标注数据实现更好的分类性能。由于...

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来源期刊

Remote Sensing Letters REMOTE SENSING-IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

CiteScore

4.10

自引率

4.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Remote Sensing Letters is a peer-reviewed international journal committed to the rapid publication of articles advancing the science and technology of remote sensing as well as its applications. The journal originates from a successful section, of the same name, contained in the International Journal of Remote Sensing from 1983 –2009. Articles may address any aspect of remote sensing of relevance to the journal’s readership, including – but not limited to – developments in sensor technology, advances in image processing and Earth-orientated applications, whether terrestrial, oceanic or atmospheric. Articles should make a positive impact on the subject by either contributing new and original information or through provision of theoretical, methodological or commentary material that acts to strengthen the subject.