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dc.contributor.authorZerrouki, Yacine
dc.contributor.authorHarrou, Fouzi
dc.contributor.authorZerrouki, Nabil
dc.contributor.authorDairi, Abdelkader
dc.contributor.authorSun, Ying
dc.date.accessioned2020-12-09T11:53:28Z
dc.date.available2020-12-09T11:53:28Z
dc.date.issued2020
dc.identifier.citationZerrouki, Y., Harrou, F., Zerrouki, N., Dairi, A., & Sun, Y. (2020). Desertification Detection using an Improved Variational AutoEncoder-Based Approach through ETM-Landsat Satellite Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1–1. doi:10.1109/jstars.2020.3042760
dc.identifier.issn2151-1535
dc.identifier.doi10.1109/JSTARS.2020.3042760
dc.identifier.urihttp://hdl.handle.net/10754/666315
dc.description.abstractThe accurate land cover change detection is critical to improve landscape dynamics analysis and mitigate desertification problems efficiently. Desertification detection is a challenging problem because of the high degree of similarity between some desertification cases and like-desertification phenomena, such as deforestation. This paper provides an effective approach to detect deserted regions based on Landsat imagery and Variational AutoEncoder (VAE). The VAE model, as a deep learning-based model, has gained special attention in features extraction and modeling due to its distribution-free assumptions and superior nonlinear approximation. Here, a VAE approach is applied to spectral signatures for detecting pixels affected by the land cover change. The considered features are extracted from multi-temporal images and include multi-spectral information, and no prior image segmentation is required. The proposed method was evaluated on the publicly available remote sensing data using multi-temporal Landsat optical images taken from the freely available Landsat program. The arid region around Biskra in Algeria is selected as a study area since it is well-known that desertification phenomena strongly influence this region. The VAE model was evaluated and compared with restricted Boltzmann machines, deep learning model, and binary clustering algorithms, including Agglomerative, Birch, expected maximization, KMean clustering algorithms, and one-class support vector machine. The comparative results showed that the VAE consistently outperformed the other models for detecting changes to land cover, mainly deserted regions. This study also showed that VAE outperformed the state of the art algorithms.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9285171/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9285171
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectdesertification detection
dc.subjectfeature extraction
dc.subjectLandsat sensors
dc.subjectVAE classification
dc.titleDesertification Detection using an Improved Variational AutoEncoder-Based Approach through ETM-Landsat Satellite Data
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionConservatoire National des Formations a l'Environnement, Algiers, Algeria, Conservatoire National des Formations a l'Environnement, Algiers, Algeria,
dc.contributor.institutionDIIM, Centre for Development of Advanced Technologies, 230154 Algiers, Algeria,
dc.contributor.institutionComputer Science department, University of Science and Technology of Oran Mohamed Boudiaf, 202108 Oran, Algeria,
dc.identifier.pages1-1
kaust.personHarrou, Fouzi
kaust.personSun, Ying
refterms.dateFOA2020-12-09T11:54:59Z


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