ASSESSMENT OF MULTIRESOLUTION SEGMENTATION FOR EXTRACTING GREENHOUSES FROM WORLDVIEW-2 IMAGERY
The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote sensing applications. In this way, object based image analysis (OBIA) approach has been proved as the best option when working with VHR satellite imagery. OBIA considers spectra...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/145/2016/isprs-archives-XLI-B7-145-2016.pdf |
| Summary: | The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote sensing
applications. In this way, object based image analysis (OBIA) approach has been proved as the best option when working with VHR
satellite imagery. OBIA considers spectral, geometric, textural and topological attributes associated with meaningful image objects.
Thus, the first step of OBIA, referred to as segmentation, is to delineate objects of interest. Determination of an optimal
segmentation is crucial for a good performance of the second stage in OBIA, the classification process. The main goal of this work is
to assess the multiresolution segmentation algorithm provided by eCognition software for delineating greenhouses from WorldView-
2 multispectral orthoimages. Specifically, the focus is on finding the optimal parameters of the multiresolution segmentation
approach (i.e., Scale, Shape and Compactness) for plastic greenhouses. The optimum Scale parameter estimation was based on the
idea of local variance of object heterogeneity within a scene (ESP2 tool). Moreover, different segmentation results were attained by
using different combinations of Shape and Compactness values. Assessment of segmentation quality based on the discrepancy
between reference polygons and corresponding image segments was carried out to identify the optimal setting of multiresolution
segmentation parameters. Three discrepancy indices were used: Potential Segmentation Error (PSE), Number-of-Segments Ratio
(NSR) and Euclidean Distance 2 (ED2). |
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| ISSN: | 1682-1750 2194-9034 |