Charis Lanaras

1.1k total citations · 1 hit paper
9 papers, 710 citations indexed

About

Charis Lanaras is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Astronomy and Astrophysics. According to data from OpenAlex, Charis Lanaras has authored 9 papers receiving a total of 710 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 5 papers in Media Technology and 3 papers in Astronomy and Astrophysics. Recurrent topics in Charis Lanaras's work include Advanced Image Fusion Techniques (5 papers), Remote-Sensing Image Classification (4 papers) and Image and Signal Denoising Methods (3 papers). Charis Lanaras is often cited by papers focused on Advanced Image Fusion Techniques (5 papers), Remote-Sensing Image Classification (4 papers) and Image and Signal Denoising Methods (3 papers). Charis Lanaras collaborates with scholars based in Switzerland, Germany and Portugal. Charis Lanaras's co-authors include Konrad Schindler, E. Baltsavias, José M. Bioucas‐Dias, Silvano Galliani, Valentin Bickel, U. Mall, Simon Loew, Andrea Manconi, Emmanuel P. Baltsavias and Andrea Manconi and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Geoscience and Remote Sensing and Remote Sensing.

In The Last Decade

Charis Lanaras

9 papers receiving 689 citations

Hit Papers

Hyperspectral Super-Resolution by Coupled Spectral Unmixing 2015 2026 2018 2022 2015 100 200 300

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Charis Lanaras Switzerland 6 561 452 91 62 41 9 710
Guoming Gao China 16 274 0.5× 182 0.4× 53 0.6× 114 1.8× 27 0.7× 53 616
Yufeng Cheng China 12 149 0.3× 149 0.3× 45 0.5× 66 1.1× 50 1.2× 27 460
Giuseppe Masi Italy 8 917 1.6× 739 1.6× 82 0.9× 53 0.9× 50 1.2× 12 1.1k
Ruyi Feng China 10 338 0.6× 141 0.3× 104 1.1× 165 2.7× 45 1.1× 16 429
Hang Fu China 11 416 0.7× 150 0.3× 75 0.8× 230 3.7× 70 1.7× 28 553
Zhipeng Dong China 13 193 0.3× 183 0.4× 60 0.7× 38 0.6× 101 2.5× 35 450
Giovanni Marchisio United States 12 377 0.7× 284 0.6× 122 1.3× 118 1.9× 54 1.3× 33 619
Fanqiang Lin China 4 199 0.4× 117 0.3× 75 0.8× 120 1.9× 28 0.7× 11 411
Hao Cui China 11 229 0.4× 177 0.4× 61 0.7× 92 1.5× 80 2.0× 26 451

Countries citing papers authored by Charis Lanaras

Since Specialization
Citations

This map shows the geographic impact of Charis Lanaras's research. 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 Charis Lanaras with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Charis Lanaras more than expected).

Fields of papers citing papers by Charis Lanaras

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charis Lanaras. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Charis Lanaras. The network helps show where Charis Lanaras may publish in the future.

Co-authorship network of co-authors of Charis Lanaras

This figure shows the co-authorship network connecting the top 25 collaborators of Charis Lanaras. A scholar is included among the top collaborators of Charis Lanaras based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Charis Lanaras. Charis Lanaras is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
Bickel, Valentin, Charis Lanaras, Andrea Manconi, Simon Loew, & U. Mall. (2019). Lunar Rockfall Detection and Mapping usinng Deep Neural Networks. MPG.PuRe (Max Planck Society). 1595. 1 indexed citations
2.
Bickel, Valentin, Charis Lanaras, Andrea Manconi, Simon Loew, & U. Mall. (2019). A Deep Neural Network for Automated Detection and Mapping of lunar Rockfalls. Repository for Publications and Research Data (ETH Zurich). 21. 3328. 1 indexed citations
3.
Bickel, Valentin, Charis Lanaras, Andrea Manconi, Simon Loew, & U. Mall. (2018). Automated detection of lunar rockfalls using a Faster Region-based Convolutional Neural Network. AGU Fall Meeting Abstracts. 2018. 1 indexed citations
4.
Lanaras, Charis, José M. Bioucas‐Dias, Silvano Galliani, E. Baltsavias, & Konrad Schindler. (2018). Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network. ISPRS Journal of Photogrammetry and Remote Sensing. 146. 305–319. 244 indexed citations
5.
Bickel, Valentin, Charis Lanaras, Andrea Manconi, Simon Loew, & U. Mall. (2018). Automated Detection of Lunar Rockfalls Using a Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing. 57(6). 3501–3511. 33 indexed citations
6.
Lanaras, Charis, E. Baltsavias, & Konrad Schindler. (2017). Hyperspectral Super-Resolution with Spectral Unmixing Constraints. Remote Sensing. 9(11). 1196–1196. 34 indexed citations
7.
Lanaras, Charis, José M. Bioucas‐Dias, E. Baltsavias, & Konrad Schindler. (2017). Super-Resolution of Multispectral Multiresolution Images from a Single Sensor. 1505–1513. 59 indexed citations
8.
Lanaras, Charis, E. Baltsavias, & Konrad Schindler. (2015). Hyperspectral Super-Resolution by Coupled Spectral Unmixing. 3586–3594. 323 indexed citations breakdown →
9.
Lanaras, Charis, Emmanuel P. Baltsavias, & Konrad Schindler. (2015). ADVANCES IN HYPERSPECTRAL AND MULTISPECTRAL IMAGE FUSION AND SPECTRAL UNMIXING. SHILAP Revista de lepidopterología. XL-3/W3. 451–458. 14 indexed citations

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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026