Alexis J. Comber
Media Technology · 2015–2016
A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Media Technology — 19 citations into it; #3,261 of 3,621 by trust here, #2,280 by citations
- Atmospheric Science — 14 citations into it; #4,121 of 4,804 by trust here, #3,198 by citations
- Ecology — 14 citations into it; #8,798 of 10,717 by trust here, #7,949 by citations
- Computer Vision and Pattern Recognition — 7 citations into it
- Global and Planetary Change — 6 citations into it
Whom Alexis J. Comber trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Lorenzo Bruzzone
- Francesca Bovolo
- Jungho Im
- Thomas Blaschke
- Annemarie Schneider
- J. R. Jensen
- Baudouin Desclée
- Dengsheng Lu
- Jinsong Deng
- Gang Chen
Who trusts Alexis J. Comber overall
Papers from Alexis J. Comber's perspective
Ordered by how strongly the researchers Alexis J. Comber trusts endorse each paper. Same corpus, a different order for every researcher.
- PCA‐based land‐use change detection and analysis using multitemporal and multisensor satellite data
- Automatic analysis of the difference image for unsupervised change detection (IEEE Transactions on Geoscience and Remote Sensing)
- A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain (IEEE Transactions on Geoscience and Remote Sensing)
- A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images (IEEE Transactions on Geoscience and Remote Sensing)
- A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images (Proceedings of the IEEE)
- Change detection techniques
- Object-based classification of remote sensing data for change detection
- Object‐based change detection using correlation image analysis and image segmentation
- Object based image analysis for remote sensing
- Review ArticleDigital change detection methods in ecosystem monitoring: a review