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Assessment of combining convolutional neural networks and object based image analysis to land cover classification using Sentinel 2 satellite imagery (Tenes region, Algeria) / N. Zaabar (2021)  

Public Titre : Assessment of combining convolutional neural networks and object based image analysis to land cover classification using Sentinel 2 satellite imagery (Tenes region, Algeria) Type de document : Article/Communication Auteurs : N. Zaabar, Auteur ; Simona Niculescu, Auteur ; M.K. Mihoubi, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2021 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2-2021 Conférence : ISPRS 2021, Commission 2, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice Virtuel France OA Archives Commission 2 Importance : pp 383 - 389 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Algérie [Termes IGN] analyse d'image orientée objet [Termes IGN] carte d'occupation du sol [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image Sentinel-MSI [Termes IGN] littoral méditerranéen [Termes IGN] ville Résumé : (auteur) Land cover maps can provide valuable information for various applications, such as territorial monitoring, environmental protection, urban planning and climate change prevention. In this purpose, remote sensing based on image classification approaches undergoing a high revolution can be dedicated to land cover mapping tasks. Similarly, deep learning models are considerably applied in remote sensing applications; which can automatically learn features from large amounts of data. Prevalently, the Convolutional Neural Network (CNN), have been increasingly performed in image classification. The aim of this study is to apply a new approach to analyse land cover, and extract its features. Experiments carried out on a coastal town located in north-western Algeria (Ténès region). The study area is chosen because of its importance as a part of the national strategy to combat natural hazards, specifically floods. As well as, a simple CNN model with two hidden layers was constructed, combined with an Object-Based Image Analysis (OBIA). In this regard, a Sentinel-2 image was used, to perform the classification, using spectral index combinations. Furthermore, to compare the performance of the proposed approach, an OBIA based on machines learning algorithms mainly Random Forest (RF) and Support Vector Machine (SVM), was provided. Results of accuracy assessment of classification showed good values in terms of Overall accuracy and Kappa Index, which reach to 93.1% and 0.91, respectively. As a comparison, CNN-OBIA approach outperformed OBIA based on RF algorithm. Therefore, Final land cover maps can be used as a support tool in regional and national decisions. Numéro de notice : C2021-020 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Communication DOI : 10.5194/isprs-archives-XLIII-B3-2021-383-2021 Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-383-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98072