Score-ranking smooth average precision loss for remote sensing image retrieval

IF 1 4区地球科学 Q4 GEOGRAPHY, PHYSICAL

Journal of Spatial Science Pub Date : 2024-01-18 DOI:10.1080/14498596.2024.2302167

Yun Ge, Qiyang Liu, Gang Wu, Sijia Wang, Famao Ye

引用次数: 0

Abstract

In remote sensing image retrieval task, deep metric learning loss is an effective method for learning semantic similarity relationships between images. However, most deep metric learning losses tre...

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来源期刊

Journal of Spatial Science 地学-地质学

CiteScore

5.00

自引率

5.30%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of Spatial Science publishes papers broadly across the spatial sciences including such areas as cartography, geodesy, geographic information science, hydrography, digital image analysis and photogrammetry, remote sensing, surveying and related areas. Two types of papers are published by he journal: Research Papers and Professional Papers. Research Papers (including reviews) are peer-reviewed and must meet a minimum standard of making a contribution to the knowledge base of an area of the spatial sciences. This can be achieved through the empirical or theoretical contribution to knowledge that produces significant new outcomes. It is anticipated that Professional Papers will be written by industry practitioners. Professional Papers describe innovative aspects of professional practise and applications that advance the development of the spatial industry.