D.F. Prieto

3.3k total citations · 1 hit paper
29 papers, 2.3k citations indexed

About

D.F. Prieto is a scholar working on Media Technology, Atmospheric Science and Artificial Intelligence. According to data from OpenAlex, D.F. Prieto has authored 29 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Media Technology, 11 papers in Atmospheric Science and 8 papers in Artificial Intelligence. Recurrent topics in D.F. Prieto's work include Remote-Sensing Image Classification (15 papers), Remote Sensing in Agriculture (7 papers) and Spectroscopy and Chemometric Analyses (7 papers). D.F. Prieto is often cited by papers focused on Remote-Sensing Image Classification (15 papers), Remote Sensing in Agriculture (7 papers) and Spectroscopy and Chemometric Analyses (7 papers). D.F. Prieto collaborates with scholars based in Italy, France and United States. D.F. Prieto's co-authors include Lorenzo Bruzzone, Sebastiano B. Serpico, Claude Duguay, Laura C. Brown, Luca Brocca, Christian Massari, Luca Ciabatta, Yann H. Kerr, Viviana Maggioni and Gab Abramowitz and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Image Processing and International Journal of Remote Sensing.

In The Last Decade

D.F. Prieto

27 papers receiving 2.1k citations

Hit Papers

Automatic analysis of the difference image for unsupervis... 2000 2026 2008 2017 2000 250 500 750 1000
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Automatic analysis of the difference image for unsupervised change detection
    2000 1.0k citations Lorenzo Bruzzone, D.F. Prieto IEEE Transactions on Geoscience and Remote Sensing profile →

Peers

D.F. Prieto
Zhiyong Lv China
Giovanna Trianni Italy
N. Keshava United States
Michele Volpi Switzerland
Xinyu Wang China
Renlong Hang China
Fabio Pacifici United States
Marian-Daniel Iordache Belgium
Javier Calpe‐Maravilla Spain
Zhiyong Lv China
D.F. Prieto
Citations per year, relative to D.F. Prieto D.F. Prieto (= 1×) peers Zhiyong Lv

Countries citing papers authored by D.F. Prieto

Since Specialization
Citations

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

Fields of papers citing papers by D.F. Prieto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by D.F. Prieto. 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 D.F. Prieto. The network helps show where D.F. Prieto may publish in the future.

Co-authorship network of co-authors of D.F. Prieto

This figure shows the co-authorship network connecting the top 25 collaborators of D.F. Prieto. A scholar is included among the top collaborators of D.F. Prieto 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 D.F. Prieto. D.F. Prieto 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.
Nägler, Thomas, et al.. (2024). Monitoring Ice Sheet Melt and Refreeze Using Active Microwave Measurements. 49–52. 1 indexed citations
2.
Massari, Christian, Luca Brocca, Thierry Pellarin, et al.. (2020). A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products. Hydrology and earth system sciences. 24(5). 2687–2710. 55 indexed citations
3.
Pellet, Victor, Filipe Aires, Simon Munier, et al.. (2019). Integrating multiple satellite observations into a coherent dataset to monitor the full water cycle – application to the Mediterranean region. Hydrology and earth system sciences. 23(1). 465–491. 31 indexed citations
4.
5.
Brocca, Luca, Angelica Tarpanelli, Paolo Filippucci, et al.. (2018). How much water is used for irrigation? A new approach exploiting satellite soil moisture observations. Lirias (KU Leuven). 4558. 2 indexed citations
6.
Duguay, Claude, et al.. (2016). Evidence of recent changes in the ice regime of lakes in the Canadian High Arctic from spaceborne satellite observations. ˜The œcryosphere. 10(3). 941–960. 28 indexed citations
7.
8.
Prieto, D.F., et al.. (2010). ESA's STSE WACMOS Project: Towards a Water Cycle Multimission Observation Strategy. EGUGA. 537. 1 indexed citations
9.
Prieto, D.F., et al.. (2004). Operational wetlands monitoring for the ramsar convention: TESEO powers a breakthrough. 3. 1486–1489. 2 indexed citations
10.
Bruzzone, Lorenzo & D.F. Prieto. (2003). An MRF approach to unsupervised change detection. 1. 143–147. 9 indexed citations
11.
Bruzzone, Lorenzo & D.F. Prieto. (2002). An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images. IEEE Transactions on Image Processing. 11(4). 452–466. 303 indexed citations
12.
Prieto, D.F.. (2002). Change detection in multisensor remote-sensing data for desertification monitoring. 475. 255–260. 4 indexed citations
13.
Bruzzone, Lorenzo & D.F. Prieto. (2002). A combined supervised and unsupervised approach to classification of multitemporal remote sensing images. 1. 162–164. 1 indexed citations
14.
Bruzzone, Lorenzo & D.F. Prieto. (2001). Unsupervised retraining of a maximum likelihood classifier for the analysis of multitemporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing. 39(2). 456–460. 199 indexed citations
15.
Bruzzone, Lorenzo & D.F. Prieto. (2000). A minimum-cost thresholding technique for unsupervised change detection. International Journal of Remote Sensing. 21(18). 3539–3544. 47 indexed citations
16.
Bruzzone, Lorenzo & D.F. Prieto. (2000). An adaptive parcel-based technique for unsupervised change detection. International Journal of Remote Sensing. 21(4). 817–822. 58 indexed citations
17.
Bruzzone, Lorenzo & D.F. Prieto. (1999). A technique for the selection of kernel-function parameters in RBF neural networks for classification of remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing. 37(2). 1179–1184. 122 indexed citations
18.
Bruzzone, Lorenzo & D.F. Prieto. (1999). An incremental-learning neural network for the classification of remote-sensing images. Pattern Recognition Letters. 20(11-13). 1241–1248. 40 indexed citations
19.
Bruzzone, Lorenzo, D.F. Prieto, & Sebastiano B. Serpico. (1999). A neural-statistical approach to multitemporal and multisource remote-sensing image classification. IEEE Transactions on Geoscience and Remote Sensing. 37(3). 1350–1359. 177 indexed citations
20.
Bruzzone, Lorenzo & D.F. Prieto. (1998). Supervised training technique for radial basis functionneural networks. Electronics Letters. 34(11). 1115–1116. 13 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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