Inmaculada Dópido

518 total citations
16 papers, 430 citations indexed

About

Inmaculada Dópido is a scholar working on Media Technology, Atmospheric Science and Analytical Chemistry. According to data from OpenAlex, Inmaculada Dópido has authored 16 papers receiving a total of 430 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Media Technology, 12 papers in Atmospheric Science and 6 papers in Analytical Chemistry. Recurrent topics in Inmaculada Dópido's work include Remote-Sensing Image Classification (16 papers), Remote Sensing and Land Use (12 papers) and Spectroscopy and Chemometric Analyses (6 papers). Inmaculada Dópido is often cited by papers focused on Remote-Sensing Image Classification (16 papers), Remote Sensing and Land Use (12 papers) and Spectroscopy and Chemometric Analyses (6 papers). Inmaculada Dópido collaborates with scholars based in Spain, Italy and China. Inmaculada Dópido's co-authors include Antonio Plaza, Paolo Gamba, Jun Li, Alberto Villa, José M. Bioucas‐Dias, Prashanth Marpu, Jón Atli Benediktsson, Maciel Zortea, Xia Zhang and Yanli Sun and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters and IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

In The Last Decade

Inmaculada Dópido

15 papers receiving 407 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Inmaculada Dópido Spain 9 379 249 124 77 67 16 430
Alp Ertürk Türkiye 11 381 1.0× 214 0.9× 200 1.6× 73 0.9× 41 0.6× 61 555
Shrutika S. Sawant India 13 330 0.9× 248 1.0× 108 0.9× 58 0.8× 71 1.1× 22 449
Erting Pan China 9 356 0.9× 196 0.8× 158 1.3× 49 0.6× 41 0.6× 20 463
Chengle Zhou China 15 423 1.1× 249 1.0× 180 1.5× 45 0.6× 104 1.6× 33 557
Shuguo Jiang China 9 447 1.2× 195 0.8× 168 1.4× 43 0.6× 110 1.6× 11 570
Xiangpo Wei China 7 360 0.9× 267 1.1× 135 1.1× 56 0.7× 54 0.8× 12 467
Joshua Broadwater United States 9 302 0.8× 146 0.6× 68 0.5× 56 0.7× 62 0.9× 23 369
Xuanwen Tao Spain 13 308 0.8× 144 0.6× 153 1.2× 37 0.5× 73 1.1× 31 474
Fardin Mirzapour Iran 10 282 0.7× 243 1.0× 108 0.9× 56 0.7× 33 0.5× 18 421
Moussa Sofiane Karoui Algeria 11 357 0.9× 192 0.8× 98 0.8× 79 1.0× 106 1.6× 89 466

Countries citing papers authored by Inmaculada Dópido

Since Specialization
Citations

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

Fields of papers citing papers by Inmaculada Dópido

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Inmaculada Dópido

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

All Works

16 of 16 papers shown
1.
Sun, Yanli, et al.. (2016). A new semi-supervised classification strategy combining active learning and spectral unmixing of hyperspectral data. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10007. 1000708–1000708. 4 indexed citations
2.
Dópido, Inmaculada, Himar Fabelo, Gustavo M. Callicó, et al.. (2015). Decision tree classification system for brain cancer detection using spectrographic samples. Acceda (Universidad de Las Palmas de Gran Canaria). 228. 1–6. 1 indexed citations
3.
Dópido, Inmaculada, Jun Li, Paolo Gamba, & Antonio Plaza. (2014). A New Hybrid Strategy Combining Semisupervised Classification and Unmixing of Hyperspectral Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7(8). 3619–3629. 29 indexed citations
4.
Li, Jun, Inmaculada Dópido, Paolo Gamba, & Antonio Plaza. (2014). Complementarity of Discriminative Classifiers and Spectral Unmixing Techniques for the Interpretation of Hyperspectral Images. IEEE Transactions on Geoscience and Remote Sensing. 53(5). 2899–2912. 26 indexed citations
5.
Dópido, Inmaculada, Paolo Gamba, & Antonio Plaza. (2013). Spectral unmixing-based post-processing for hyperspectral image classification. 1 indexed citations
6.
Dópido, Inmaculada, Jun Li, Antonio Plaza, & Paolo Gamba. (2013). Semi-supervised classification of urban hyperspectral data using spectral unmixing concepts. 2. 186–189.
7.
Dópido, Inmaculada, Jun Li, Prashanth Marpu, et al.. (2013). Semisupervised Self-Learning for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 51(7). 4032–4044. 164 indexed citations
8.
Dópido, Inmaculada, Alberto Villa, Antonio Plaza, & Paolo Gamba. (2012). A Quantitative and Comparative Assessment of Unmixing-Based Feature Extraction Techniques for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 5(2). 421–435. 108 indexed citations
9.
Dópido, Inmaculada, Jun Li, Antonio Plaza, & José M. Bioucas‐Dias. (2012). Semi-supervised active learning for urban hyperspectral image classification. 1586–1589. 8 indexed citations
10.
Dópido, Inmaculada, Jun Li, Antonio Plaza, & José M. Bioucas‐Dias. (2012). A new semi-supervised approach for hyperspectral image classification with different active learning strategies. 10 indexed citations
11.
Dópido, Inmaculada, Jun Li, Antonio Plaza, & Paolo Gamba. (2012). Semi-supervised classification of hyperspectral data using spectral unmixing concepts. 2. 353–358. 3 indexed citations
12.
Dópido, Inmaculada, Alberto Villa, Antonio Plaza, & Paolo Gamba. (2011). A comparative assessment of several processing chains for hyperspectral image classification: What features to use?. 19. 1–4. 8 indexed citations
13.
Dópido, Inmaculada, Alberto Villa, & Antonio Plaza. (2011). Unsupervised clustering and spectral unmixing for feature extraction prior to supervised classification of hyperspectral images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8157. 81570M–81570M. 6 indexed citations
14.
Dópido, Inmaculada & Antonio Plaza. (2011). Unmixing prior to supervised classification of urban hyperspectral images. 1. 97–100. 3 indexed citations
15.
Dópido, Inmaculada, Maciel Zortea, Alberto Villa, Antonio Plaza, & Paolo Gamba. (2011). Unmixing Prior to Supervised Classification of Remotely Sensed Hyperspectral Images. IEEE Geoscience and Remote Sensing Letters. 8(4). 760–764. 43 indexed citations
16.
Dópido, Inmaculada, et al.. (2010). Comparison of support vector machine-based processing chains for hyperspectral image classification. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 16 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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