ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 251–258, 2022
https://doi.org/10.5194/isprs-annals-V-2-2022-251-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 251–258, 2022
https://doi.org/10.5194/isprs-annals-V-2-2022-251-2022
 
17 May 2022
17 May 2022

MULTIRESOLUTION SEGMENTATION FOR EXTRACTING PLASTIC GREENHOUSES FROM DEIMOS-2 IMAGERY

R. Jiménez-Lao1, M. A. Aguilar1, C. Ladisa2, F. J. Aguilar1, and A. Nemmaoui1 R. Jiménez-Lao et al.
  • 1University of Almería, Department of Engineering and Research Centre CIAIMBITAL, Almería 04120, Spain
  • 2Politecnico di Bari, DICATECh, Via Orabona 4, Bari 70125, Italy

Keywords: Plastic Covered Greenhouse, Multiresolution Segmentation, VHR Satellite, OBIA, Deimos-2

Abstract. Accurate greenhouse mapping can support environment monitoring and resource management. In an object-based image analysis (OBIA) approach focused on plastic covered greenhouses (PCG) classification, the segmentation is a crucial step for the goodness of the final results. Multiresolution segmentation (MRS) is one of the most used algorithms in OBIA approaches, being greatly enabled by the advent of the commercial software eCognition. Therefore, in addition to the segmentation algorithm used, it is very important to count on tools to assess the quality of segmentation results from digital images in order to obtain the most similar segments to the real PCG objects. In this work, several factors affecting MRS such as the type of input image and the best MRS parameters (i.e., scale, compactness and shape), have been analysed. In this regard, more than 2800 segmentations focused on PCG land cover were conducted from four pre-processed Deimos-2 very high-resolution (VHR) satellite orthoimages taken in the Southeast of Spain (Almería). Specifically, one multispectral and one pansharpened Deimos-2 orthoimages, both with and without atmospheric correction were tested in this work. The free access AssesSeg command line tool, based on a modified version of the supervised discrepancy measure named Euclidean Distance 2 (ED2), was used to determine the best MRS parameters for all the VHR satellite images. According to both the supervised discrepancy measure ED2 and visual perception, the best segmentation on PCG was obtained over the atmospherically corrected pansharpened Deimos-2 orthoimage, achieving very good results.