Özet:

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.

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