用于遥感图像中弱监督物体检测的多视角上下文自适应网络

IF 3 3区地球科学 Q2 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

International Journal of Remote Sensing Pub Date : 2024-06-21 DOI:10.1080/01431161.2024.2359734

Binfeng Ye, Junjie Zhang, Yutao Rao, Rui Gao, Dan Zeng

引用次数: 0

摘要

弱监督学习在物体检测（即弱监督物体检测，Weakly supervised object detection，WSOD）领域发挥着举足轻重的作用，大大降低了依赖图像级标签的注释成本。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Multi-view contextual adaptation network for weakly supervised object detection in remote sensing images

Weakly supervised learning plays a pivotal role in the field of object detection, i.e. Weakly supervised object detection (WSOD), significantly reducing annotation costs relying on image-level labe...

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

International Journal of Remote Sensing 工程技术-成像科学与照相技术

CiteScore

7.00

自引率

5.90%

发文量

219

审稿时长

4.8 months

期刊介绍： The International Journal of Remote Sensing ( IJRS) is concerned with the theory, science and technology of remote sensing and novel applications of remotely sensed data. The journal’s focus includes remote sensing of the atmosphere, biosphere, cryosphere and the terrestrial earth, as well as human modifications to the earth system. Principal topics include: • Remotely sensed data collection, analysis, interpretation and display. • Surveying from space, air, water and ground platforms. • Imaging and related sensors. • Image processing. • Use of remotely sensed data. • Economic surveys and cost-benefit analyses. • Drones Section: Remote sensing with unmanned aerial systems (UASs, also known as unmanned aerial vehicles (UAVs), or drones).