A Spectral Band Based Comparison of Unsupervised Segmentation Evaluation Methods for Image Segmentation Parameter Optimization

Year 2020, Volume: 7 Issue: 2, 132 - 139, 15.08.2020

Hasan Tonbul , Taskin Kavzoglu

https://doi.org/10.30897/ijegeo.641216

Cited By: 1

Abstract

Very high-resolution images obtained
with recently launched satellite sensors have been used intensively in the
remote sensing area. The widespread use of high-resolution images has greatly
facilitated the creation and updating of land use/land cover (LULC) maps. Traditional
pixel-based image analysis methods that extract information based solely on the
spectral values of pixels are generally not suitable for high-resolution
images. Unlike pixel-based approaches, object-based image analysis (OBIA) uses
pixel clustering (image objects) instead of pixels by considering the shape,
texture, context and spectral features and provide richer information
extraction. Image segmentation is an important process and prerequisite for the
OBIA process. It is essential to evaluate the performance of segmentation
algorithms for the determination of effective segmentation methods and optimization
of segmentation parameters. In this study, the multi-resolution segmentation
algorithm is used for the segmentation process. The effect of spectral bands on
segmentation quality was analysed using a Worldview-2 high-resolution satellite
image. In order to analyze segmentation quality, two unsupervised quality
metrics, namely, F-measure and Plateau
Objective Function (POF) values were calculated for
each band separately. In this manner, optimum parameter values were determined
using different variations of Moran's I Index and variance values. Image
segmentation was performed by using different scale, shape and compactness
parameter values. In this context, 30 segmentation analysis was performed
considering three different spectral bands (red, green and near-infrared
bands).  The results showed that the highest segmentation quality was acquired
for the NIR band among the spectral bands for the F-measure method, while the
highest segmentation quality value was achieved for the green band for the POF metric. In addition, the optimum segmentation parameter values of the
scale, shape and compactness were determined as 30-0.3-0.5 and 50-0.1-0.3 for F-measure and POF
approaches, respectively.

Keywords

OBIA, Segmentation, POF, F-measure, Worldview-2, Moran's I

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