Investigating the impact of spatial and spectral resolution of satellite images on segmentation quality

Nika Mesner; Kristof Ostir

doi:10.1117/1.JRS.8.083696

6 January 2014 Investigating the impact of spatial and spectral resolution of satellite images on segmentation quality

Nika Mesner, Kristof Ostir

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Journal of Applied Remote Sensing, Vol. 8, Issue 1, 083696 (January 2014). https://doi.org/10.1117/1.JRS.8.083696

Abstract

Segmentation, the first step of object-based classification, is crucial to the quality of the final classification results. A poor quality of the segmentation leads directly to a low quality of the classification. Therefore, it is very important to evaluate the segmentation results using quantitative methods and to know how to obtain the best results. To obtain the best possible segmentation results, it is important to choose the right input data resolution as well as the best algorithm and its parameters for a specific remote sensing application. The impact of the segmentation algorithm, the parameter settings, as well as the spatial and spectral resolution of the data is investigated. To describe these impacts, we performed more than 70 segmentations of a Worldview-2 image. The impact of the spectral resolution was tested with 10 combinations of data on different spectral channels, and the impact of the spatial resolution was tested on an original and downsampled test image to four different spatial resolutions. We investigated these impacts on the segmentation of objects that belong to the classes urban, forest, bare soil, vegetation, and water. The impacts on the segmentation are described using a common methodology for the evaluation of segmentation.

© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Nika Mesner and Kristof Ostir "Investigating the impact of spatial and spectral resolution of satellite images on segmentation quality," Journal of Applied Remote Sensing 8(1), 083696 (6 January 2014). https://doi.org/10.1117/1.JRS.8.083696

Published: 6 January 2014

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