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Greenhouse detection is important with respect to urban and rural planning, yield estimation and crop planning, sustainable development, natural resource management, risk analysis and damage assessment. The fast and accurate detection of greenhouses automaticaly from remote sensing imagery saves labour and time. The aim of this study is to detect greenhouse areas by using color and infrared orthophoto (RGB-NIR), topographic map and Digital Surface Model (DSM). The study was implemented in Kumluca district of Antalya, Turkey which includes intensive greenhouse areas. In this study, color and infrared orthophotos, normalized Digital Surface Model (nDSM), Normalized Difference Vegetation Index (NDVI) and Visible Red-based Built-up Index (VrNIR_BI) were used and the greenhouse areas were detected using Object Based Image Analysis (OBIA). In this process, the optimum scale parameter was determined automatically by the Estimation of Scale Parameter2 (ESP2) tool and Multi Resolution Segmentation (MRS) was used as the segmentation algorithm. In the classification stage, K-Nearest Neighbor (K-NN), Random Forest (RF) and Support Vector Machine (SVM) classification techniques were used and the accuracies of the classification results were compared. The classification with the highest accuracy was determined and the class numbers were reduced to two classes as greenhouse and non-greenhouse areas. Obtained results showed that greenhouse areas can be determined from color and infrared orthophoto and DSM data successfully by using the OBIA. The highest overall accuracy was obtained when the SVM classifier was used with 94.80%. |
