David W. Messinger

1.9k total citations
161 papers, 1.4k citations indexed

About

David W. Messinger is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, David W. Messinger has authored 161 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 106 papers in Media Technology, 45 papers in Computer Vision and Pattern Recognition and 25 papers in Artificial Intelligence. Recurrent topics in David W. Messinger's work include Remote-Sensing Image Classification (103 papers), Advanced Image Fusion Techniques (37 papers) and Geochemistry and Geologic Mapping (22 papers). David W. Messinger is often cited by papers focused on Remote-Sensing Image Classification (103 papers), Advanced Image Fusion Techniques (37 papers) and Geochemistry and Geologic Mapping (22 papers). David W. Messinger collaborates with scholars based in United States, United Kingdom and Canada. David W. Messinger's co-authors include William Basener, Amanda Ziemann, Bin Chen, Emmett J. Ientilucci, John K. Delaney, John R. Schott, W. G. Roberge, Carl Salvaggio, John P. Kerekes and Nathan D. Cahill and has published in prestigious journals such as The Astrophysical Journal, IEEE Transactions on Geoscience and Remote Sensing and IEEE Transactions on Image Processing.

In The Last Decade

David W. Messinger

148 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David W. Messinger United States 20 871 302 290 233 189 161 1.4k
Yang-Lang Chang Taiwan 21 430 0.5× 429 1.4× 280 1.0× 221 0.9× 107 0.6× 96 1.5k
Jerry E. Solomon United States 9 877 1.0× 209 0.7× 321 1.1× 456 2.0× 336 1.8× 18 1.8k
John P. Kerekes United States 21 1.1k 1.3× 328 1.1× 480 1.7× 275 1.2× 383 2.0× 159 1.9k
Michael T. Eismann United States 17 985 1.1× 255 0.8× 428 1.5× 171 0.7× 246 1.3× 67 1.4k
Daniel Heinz United States 9 1.5k 1.7× 190 0.6× 905 3.1× 246 1.1× 317 1.7× 32 1.9k
Jihao Yin China 18 690 0.8× 456 1.5× 267 0.9× 233 1.0× 160 0.8× 89 1.6k
Daniele Cerra Germany 16 480 0.6× 267 0.9× 163 0.6× 189 0.8× 197 1.0× 85 974
Yoann Altmann United Kingdom 23 782 0.9× 303 1.0× 376 1.3× 184 0.8× 174 0.9× 108 2.1k
Rob Heylen Belgium 15 1.0k 1.2× 239 0.8× 476 1.6× 184 0.8× 235 1.2× 57 1.3k
Hsuan Ren United States 17 1.0k 1.2× 257 0.9× 581 2.0× 192 0.8× 184 1.0× 67 1.4k

Countries citing papers authored by David W. Messinger

Since Specialization
Citations

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

Fields of papers citing papers by David W. Messinger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David W. Messinger. 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 David W. Messinger. The network helps show where David W. Messinger may publish in the future.

Co-authorship network of co-authors of David W. Messinger

This figure shows the co-authorship network connecting the top 25 collaborators of David W. Messinger. A scholar is included among the top collaborators of David W. Messinger 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 David W. Messinger. David W. Messinger is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Cao, Bei & David W. Messinger. (2025). Spatial-spectral graph convolutional network for automatic pigment mapping of historical artifacts. npj Heritage Science. 13(1). 2 indexed citations
2.
Messinger, David W., et al.. (2024). Colorimetric characterization of multispectral imaging systems for visualization of historical artifacts. Journal of Cultural Heritage. 68. 136–148. 2 indexed citations
3.
4.
Messinger, David W., et al.. (2023). Virtual cleaning of works of art using a deep generative network: spectral reflectance estimation. Heritage Science. 11(1). 4 indexed citations
5.
Messinger, David W., et al.. (2023). Image quality and object detection performance of convolutional neural networks. 19–19. 1 indexed citations
6.
Hörbrand, Franziska, et al.. (2022). PHARAO-Studie: Arzneimittelversorgung entzündlich rheumatischer Erkrankungen. Zeitschrift für Rheumatologie. 82(9). 787–797. 2 indexed citations
7.
Messinger, David W., et al.. (2021). Virtual cleaning of works of art using deep convolutional neural networks. Heritage Science. 9(1). 8 indexed citations
8.
Patterson, Catherine Schmidt, et al.. (2020). An alternative approach to mapping pigments in paintings with hyperspectral reflectance image cubes using artificial intelligence. Heritage Science. 8(1). 48 indexed citations
9.
Ferwerda, James A., et al.. (2020). Digital Modeling of Cultural Heritage Objects. RIT Scholar Works (Rochester Institute of Technology). 2(1). 22. 1 indexed citations
10.
Messinger, David W., et al.. (2020). Spatial resolution as a trade-space for low-light imaging of sensitive cultural heritage documents. Journal of Cultural Heritage. 45. 81–90.
11.
Messinger, David W., et al.. (2018). Integrating spatial and spectral information for enhancing spatial features in the Gough map of Great Britain. Journal of Cultural Heritage. 34. 159–165. 2 indexed citations
12.
Messinger, David W., et al.. (2018). Aesthetic Inference for Smart Mobile Devices. 1764–1773. 2 indexed citations
13.
Messinger, David W., et al.. (2018). Processes for conducting HSI pan-sharpening with 3D digital flattening. 154. 71–71. 1 indexed citations
14.
Messinger, David W., et al.. (2014). Estimating sampling completeness of lidar datasets using voxel-based geometry. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9080. 90800P–90800P. 3 indexed citations
15.
Kerekes, John P., Oliver Weatherbee, David W. Messinger, et al.. (2012). SpecTIR hyperspectral airborne Rochester experiment data collection campaign. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8390. 839028–839028. 58 indexed citations
16.
Messinger, David W., et al.. (2011). Graph theoretic metrics for spectral imagery with application to change detection. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8048. 804809–804809. 8 indexed citations
17.
Basener, William, et al.. (2011). High spatial resolution hyperspectral spatially adaptive endmember selection and spectral unmixing. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8048. 80481O–80481O. 7 indexed citations
18.
Basener, William & David W. Messinger. (2009). Enhanced detection and visualization of anomalies in spectral imagery. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7334. 73341Q–73341Q. 25 indexed citations
19.
Messinger, David W., et al.. (2008). Geometric estimation of the inherent dimensionality of a single material cluster in multi- and hyperspectral imagery. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6966. 69661G–69661G. 11 indexed citations
20.
Szykman, J., et al.. (2006). A hybrid thermal video and FTIR spectrometer system for rapidly locating and characterizing gas leaks. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6299. 62990O–62990O. 1 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.

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