Miriam Cha

751 citations
19 papers · 483 · h-index 8

Impact in

Papers in

Miriam Cha

19 papers receiving 459 citations

Peers

Miriam Cha
Comparison fields: 5 of 84
  • Experimental and Cognitive Psychology 114
  • Signal Processing 98
  • Computer Vision and Pattern Recognition 161
  • Applied Psychology 34
  • Health Informatics 9
Replace Yale Chang with:
Yale Chang United States
Jens-Uwe Garbas Germany
Wanqing Xie China
Ali Komaty France
Raffaella Lanzarotti Italy
José M. Leiva-Murillo Spain
Zhihua Liu China
Beate Meffert Germany
Otilia Kocsis Greece
V. Ramu Reddy India
Miriam Cha relative to Yale Chang United States Yale Chang's profile →
Citations per field
00.5×7.3×
Yale Chang · 1×
Citations per year

Countries citing papers authored by Miriam Cha

Since Specialization
Citations

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

Fields of papers citing papers by Miriam Cha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Miriam Cha. 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 Miriam Cha. The network helps show where Miriam Cha may publish in the future.

Co-authors

The 25 scholars most cited alongside Miriam Cha, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Miriam Cha Line = papers co-authored together Miriam Cha links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 2016160
2 201094
3 201841
4 201940
5 202131
6 201528
7 202121
8 201721
9 20167
10 20127
11 20216
12 20165
13 20115
14 20145
15 20214
16 20123
17 20152
18 20222
19 20141

About Miriam Cha

Miriam Cha is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering, Signal Processing, Media Technology and Environmental Engineering, having authored 19 papers that have together received 483 indexed citations. Recurring topics across this work include Synthetic Aperture Radar (SAR) Applications and Techniques (6 papers), Advanced SAR Imaging Techniques (5 papers), Remote-Sensing Image Classification (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Sparse and Compressive Sensing Techniques (2 papers), Multimodal Machine Learning Applications (2 papers), Soil Moisture and Remote Sensing (2 papers) and Image and Signal Denoising Methods (2 papers). The work is most often cited by research in Experimental and Cognitive Psychology (114 citations), Signal Processing (98 citations), Computer Vision and Pattern Recognition (161 citations), Applied Psychology (34 citations) and Health Informatics (9 citations). Miriam Cha has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Youngjune Gwon, H. T. Kung, Pooya Khorrami, Charlie K. Dagli, James R. Williamson, Elizabeth Godoy, Thomas F. Quatieri, Marios Savvides, Patrick J. Wolfe and Arjun Majumdar. Their work appears in journals such as IEEE Transactions on Geoscience and Remote Sensing, The Journal of Trauma: Injury, Infection, and Critical Care, Lecture notes in computer science, Proceedings of the International AAAI Conference on Web and Social Media and Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School).

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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