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Amel Oubara on Muck Rack

Amel Oubara

As seen in: MDPI, Taylor & Francis Online
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Articles

Adversarial learning network for multi-source change detection in built-up areas: integrating VHR RGB and multispectral imagery

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By Amel Oubara, Falin Wu, Guoxin Qu, Reza Maleki
| Taylor & Francis Online
?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom. ABSTRACT The rapid advancement of remote sensing technologies and the increasing availability of satellite and aerial imagery have created new opportunities for change detection (CD).
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Adaptive Month Matching: A Phenological Alignment Method for Transfer Learning in Cropland Segmentation

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By Reza Maleki, Falin Wu, Amel Oubara, Guoxin Qu
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Refinement of Cropland Data Layer with Effective Confidence Layer Interval and Image Filtering

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By Reza Maleki, Falin Wu, Amel Oubara, Loghman Fathollahi
| MDPI
1. Introduction The agricultural sector has historically met food production demands; however, concerns about its ability to sustain this role for an expanding global population persist in food security discussions [1]. While significant strides have been made in reducing hunger globally, substantial challenges remain [2,3].
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