Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data

World Multidisciplinary Earth Sciences Symposium 2016

Elhag, Mohamed^1 ; Boteva, Silvena^2

Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University Jeddah, 21589, Saudi Arabia^1

Department of Ecology and Environmental Protection, Faculty of Biology, Sofia University, Sofia

1164, Bulgaria^2

关键词: Classification performance;    Classification results;    Conventional classification methods;    High spatial resolution;    Land-use and land cover classifications;    Landscape fragmentation;    Object-based classifications;    Satellite image classification;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/44/4/042032/pdf
DOI  :  10.1088/1755-1315/44/4/042032

来源: IOP

【 摘 要 】

Landscape fragmentation is noticeably practiced in Mediterranean regions and imposes substantial complications in several satellite image classification methods. To some extent, high spatial resolution data were able to overcome such complications. For better classification performances in Land Use Land Cover (LULC) mapping, the current research adopts different classification methods comparison for LULC mapping using Sentinel-2 satellite as a source of high spatial resolution. Both of pixel-based and an object-based classification algorithms were assessed; the pixel-based approach employs Maximum Likelihood (ML), Artificial Neural Network (ANN) algorithms, Support Vector Machine (SVM), and, the object-based classification uses the Nearest Neighbour (NN) classifier. Stratified Masking Process (SMP) that integrates a ranking process within the classes based on spectral fluctuation of the sum of the training and testing sites was implemented. An analysis of the overall and individual accuracy of the classification results of all four methods reveals that the SVM classifier was the most efficient overall by distinguishing most of the classes with the highest accuracy. NN succeeded to deal with artificial surface classes in general while agriculture area classes, and forest and semi-natural area classes were segregated successfully with SVM. Furthermore, a comparative analysis indicates that the conventional classification method yielded better accuracy results than the SMP method overall with both classifiers used, ML and SVM.