{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:14:06Z","timestamp":1776183246186,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T00:00:00Z","timestamp":1710288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"BPI France"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Convolutional neural networks (CNN) have shown great results for object-detection tasks by learning texture and pattern-extraction filters. However, object-level interactions are harder to grasp without increasing the complexity of the architectures. On the other hand, Point Process models propose to solve the detection of the configuration of objects as a whole, allowing the factoring in of the image data and the objects\u2019 prior interactions. In this paper, we propose combining the information extracted by a CNN with priors on objects within a Markov Marked Point Process framework. We also propose a method to learn the parameters of this Energy-Based Model. We apply this model to the detection of small vehicles in optical satellite imagery, where the image information needs to be complemented with object interaction priors because of noise and small object sizes.<\/jats:p>","DOI":"10.3390\/rs16061019","type":"journal-article","created":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:08:43Z","timestamp":1710335323000},"page":"1019","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning Point Processes and Convolutional Neural Networks for Object Detection in Satellite Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3133-9405","authenticated-orcid":false,"given":"Jules","family":"Mabon","sequence":"first","affiliation":[{"name":"Inria, Universit\u00e9 C\u00f4te d\u2019Azur, 06902 Sophia Antipolis, France"}]},{"given":"Mathias","family":"Ortner","sequence":"additional","affiliation":[{"name":"Airbus Defence and Space, 31400 Toulouse, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7444-0856","authenticated-orcid":false,"given":"Josiane","family":"Zerubia","sequence":"additional","affiliation":[{"name":"Inria, Universit\u00e9 C\u00f4te d\u2019Azur, 06902 Sophia Antipolis, France"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object Detection in 20 Years: A Survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. 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