Unraveling Segmentation Quality of Remotely Sensed Images on Plastic-Covered Greenhouses: A Rigorous Experimental Analysis from Supervised Evaluation Metrics

Abstract

Plastic-covered greenhouse (PCG) segmentation represents a significant challenge for object-based PCG mapping studies due to the spectral characteristics of these singular structures. Therefore, the assessment of PCG segmentation quality by employing a multiresolution segmentation algorithm (MRS) was addressed in this study. The structure of this work is composed of two differentiated phases. The first phase aimed at testing the performance of eight widely applied supervised segmentation metrics in order to find out which was the best metric for evaluating image segmentation quality over PCG land cover. The second phase focused on examining the effect of several factors (reflectance storage scale, image spatial resolution, shape parameter of MRS, study area, and image acquisition season) and their interactions on PCG segmentation quality through a full factorial analysis of variance (ANOVA) design. The analysis considered two different study areas (Almeria (Spain) and Antalya (Turkey)), seasons (winter and summer), image spatial resolution (high resolution and medium resolution), and reflectance storage scale (Percent and 16Bit formats). Regarding the results of the first phase, the Modified Euclidean Distance 2 (MED2) was found to be the best metric to evaluate PCG segmentation quality. The results coming from the second phase revealed that the most critical factor that affects MRS accuracy was the interaction between reflectance storage scale and shape parameter. Our results suggest that the Percent reflectance storage scale, with digital values ranging from 0 to 100, performed significantly better than the 16Bit reflectance storage scale (0 to 10,000), both in the visual interpretation of PCG segmentation quality and in the quantitative assessment of segmentation accuracy.

Keywords

• greenhouse segmentation
• multiresolution segmentation (MRS)
• object-based image analysis (OBIA)
• segmentation quality
• supervised evaluation