基于图像重建损失的无监督变化检测，包含任何段落

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-08-18 DOI:10.1080/2150704x.2024.2388851

HyeonCheol Noh, JinGi Ju, YuHyun Kim, MinWoo Kim, Dong-Geol Choi

引用次数: 0

摘要

在遥感领域，基于深度学习的变化检测显示出良好的性能。然而，收集多时空配对图像来训练变化检测模型的成本很高。为了解决这个问题，我们采用了...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Unsupervised change detection based on image reconstruction loss with segment anything

In remote sensing, change detection based on deep learning shows promising performance. However, collecting multi-temporal paired images for training a change detection model is costly. To solve th...

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.