Application of Convolutional Neural Networks With Object-Based Image Analysis for Land Cover and Land Use Mapping in Coastal Areas: A Case Study in Ain Témouchent, Algeria
Author:
Affiliation:
1. CNRS, LETG Brest UMR 6554 CNRS, University of Western Brittany, Brest, France
2. Laboratoire Mobilisation et Valorisation des Ressources en Eau, Ecole Nationale Supérieure d'Hydraulique, Blida, Algeria
Funder
Hubert Curien
Interdisciplinary Graduate School
ERASMUS+ Chair Jean Monnet European Spatial Studies of Sea and Coastal Zones
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9656571/09803238.pdf?arnumber=9803238
Reference57 articles.
1. Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data
2. Land Cover Classification Based on Sentinel-2 Satellite Imagery Using Convolutional Neural Network Model: A Case Study in Semarang Area, Indonesia
3. Mapping and monitoring of land cover/land use (LCLU) changes in the crozon peninsula (Brittany, France) from 2007 to 2018 by machine learning algorithms (support vector machine, random forest, and convolutional neural network) and by post-classification comparison (PCC);xie;Remote Sens,2021
4. ASSESSMENT OF COMBINING CONVOLUTIONAL NEURAL NETWORKS AND OBJECT BASED IMAGE ANALYSIS TO LAND COVER CLASSIFICATION USING SENTINEL 2 SATELLITE IMAGERY (TENES REGION, ALGERIA)
5. Object-Oriented Method Combined with Deep Convolutional Neural Networks for Land-Use-Type Classification of Remote Sensing Images
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