{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T03:00:42Z","timestamp":1760151642900,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T00:00:00Z","timestamp":1649116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Remotely sensed data are essential for understanding environmental dynamics, for their forecasting, and for early detection of disasters. Microwave remote sensing sensors complement the information provided by observations in the optical spectrum, with the advantage of being less sensitive to adverse atmospherical conditions and of carrying their own source of illumination. On the one hand, new generations and constellations of Synthetic Aperture Radar (SAR) sensors provide images with high spatial and temporal resolution and excellent coverage. On the other hand, SAR images suffer from speckle noise and need specific models and information extraction techniques. In this sense, the G0 family of distributions is a suitable model for SAR intensity data because it describes well areas with different degrees of texture. Information theory has gained a place in signal and image processing for parameter estimation and feature extraction. Entropy stands out as one of the most expressive features in this realm. We evaluate the performance of several parametric and non-parametric Shannon entropy estimators as input for supervised and unsupervised classification algorithms. We also propose a methodology for fine-tuning non-parametric entropy estimators. Finally, we apply these techniques to actual data.<\/jats:p>","DOI":"10.3390\/e24040509","type":"journal-article","created":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T06:01:34Z","timestamp":1649138494000},"page":"509","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Entropy Estimators in SAR Image Classification"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5703-9430","authenticated-orcid":false,"given":"Julia","family":"Cassetti","sequence":"first","affiliation":[{"name":"Instituto del Desarrollo Humano, Universidad Nacional de General Sarmiento, Los Polvorines B1613, Provincia de Buenos Aires, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6550-4726","authenticated-orcid":false,"given":"Daiana","family":"Delgadino","sequence":"additional","affiliation":[{"name":"Instituto de Ciencias, Universidad Nacional de General Sarmiento, Los Polvorines B1613, Provincia de Buenos Aires, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9185-1382","authenticated-orcid":false,"given":"Andrea","family":"Rey","sequence":"additional","affiliation":[{"name":"Centro de Procesamiento de Se\u00f1ales e Im\u00e1genes, Department of Mathematics, Universidad Tecnol\u00f3gica Nacional Facultad Regional Buenos Aires, Ciudad de Buenos Aires C1179AAQ, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8002-5341","authenticated-orcid":false,"given":"Alejandro C.","family":"Frery","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1109\/36.581981","article-title":"A model for extremely heterogeneous clutter","volume":"35","author":"Frery","year":"1997","journal-title":"IEEE Trans. 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