Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images
Impact in
- Media Technology 352
Classified as
In The Last Decade
doi.org/10.1109/tgrs.2018.2886643 →Countries where authors are citing Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images
This map shows the geographic impact of Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images more than expected).
Fields of papers citing Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images
This network shows the impact of Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images.
About Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images
This paper, published in 2019, received 429 indexed citations . Written by Sudipan Saha, Francesca Bovolo and Lorenzo Bruzzone covering the research area of Artificial Intelligence, Atmospheric Science and Media Technology. It is primarily cited by scholars working on Media Technology (352 citations), Atmospheric Science (239 citations), Ecology (157 citations), Computer Vision and Pattern Recognition (63 citations) and Artificial Intelligence (61 citations). Published in IEEE Transactions on Geoscience and Remote Sensing.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1109/tgrs.2018.2886643.