Plastic and Glass Greenhouses Detection and Delineation From Worldview-2 Satellite Imagery

Abstract

Commission VII, WG VII/4KEY WORDS: Greenhouses, SVM, RF, Boundary Tracing, Line Simplification, WorldView-2 ABSTRACT:Greenhouse detection using remote sensing technologies is an important research area for yield estimation, sustainable development, urban and rural planning and management. An approach was developed in this study for the detection and delineation of greenhouse areas from high resolution satellite imagery. Initially, the candidate greenhouse patches were detected using supervised classification techniques. For this purpose, Maximum Likelihood (ML), Random Forest (RF), and Support Vector Machines (SVM) classification techniques were applied and compared. Then, sieve filter and morphological operations were performed for improving the classification results. Finally, the obtained candidate plastic and glass greenhouse areas were delineated using boundary tracing and Douglas Peucker line simplification algorithms. The proposed approach was implemented in the Kumluca district of Antalya, Turkey utilizing pan-sharpened WorldView-2 satellite imageries. Kumluca is the prominent district of Antalya with greenhouse cultivation and includes both plastic and glass greenhouses intensively. When the greenhouse classification results were analysed, it can be stated that the SVM classification provides most accurate results and RF classification follows this. The SVM classification overall accuracy was obtained as 90.28%. When the greenhouse boundary delineation results were considered, the plastic greenhouses were delineated with 92.11% accuracy, while glass greenhouses were delineated with 80.67% accuracy. The obtained results indicate that, generally plastic and glass greenhouses can be detected and delineated successfully from WorldView-2 satellite imagery.

The Effects of Land Cover Changes on Land Surface Temperatures

“…Confusion matrix is a frequently and effectively used accuracy assessment technique to evaluate the performance of classification accuracy (Congalton, 1991;Koc-San and Sonmez, 2016). It is a good rule of thumb to collect at least 50 samples for each land cover category in the error matrix unless the area is very large and there are not many (i.e., less than 12 categories) land cover class (Congalton, 1991).…”