A Tool Assessing Optimal Multi-Scale Image Segmentation

Image segmentation to create representative objects by region growing image segmentation techniques such as multi resolution segmentation (MRS) is mostly done through interactive selection of scale parameters and is still a subject of great research interest in object-based image analysis. In this s...

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Bibliographic Details
Published in:Journal of the Indian Society of Remote Sensing 2018, Vol.46 (1), p.31-41
Main Authors: Mohan Vamsee, A., Kamala, P., Martha, Tapas R., Vinod Kumar, K., Jai sankar, G., Amminedu, E.
Format: Article
Language:English
Subjects:
Agricultural land
Earth and Environmental Science
Earth Sciences
Image analysis
Image resolution
Image segmentation
Landslides
Multiscale analysis
Optimization
Remote Sensing/Photogrammetry
Research Article
Satellite imagery
Software
Spatial distribution
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Summary:Image segmentation to create representative objects by region growing image segmentation techniques such as multi resolution segmentation (MRS) is mostly done through interactive selection of scale parameters and is still a subject of great research interest in object-based image analysis. In this study, we developed an optimum scale parameter selector (OSPS) tool for objective determination of multiple optimal scales in an image by MRS using eCognition software. The ready to use OSPS tool consists of three modules and determines optimum scales in an image by combining intrasegment variance and intersegment spatial autocorrelation. The tool was tested using WorldView-2 and Resourcesat-2 LISS-IV Mx images having different spectral and spatial resolutions in two areas to find optimal objects for ground features such as water bodies, trees, buildings, road, agricultural fields and landslides. Quality of the objects created for these features using scale parameters obtained from the OSPS tool was evaluated quantitatively using segmentation goodness metrics. Results show that OSPS tool is able determine optimum scale parameters for creation of representative objects from high resolution satellite images by MRS method.
ISSN:0255-660X
0974-3006
DOI:10.1007/s12524-017-0685-7