Abstract
Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.
Highlights
Traditional pixel-based classification methods have become less effective given the magnitude of heterogeneity existing in VHR imagery
Outputs of estimation of scale parameter (ESP)-2 tool were imported into MS Excel and local variance/rate of change (LV-rate of change (RoC)) graphs were drawn
Afterwards, three scale values were estimated for each extracted regions using the peaks in the local variance (LV)-RoC graphs as a result of visual interpretation (Figure 3)
Summary
Traditional pixel-based classification methods have become less effective given the magnitude of heterogeneity existing in VHR imagery. It is a region growing algorithm that depends on grouping pixels of initial single pixels and produces homogeneous image objects (Baatz and Schäpe, 2000) It comprises several user dependent segmentation parameters namely, scale, shape, compactness and band weights. Scale parameter that controls the segment sizes depends on various factors including characteristics of study area, image resolution and land cover types (Myint et al, 2011). It is considered crucially important for determining the closest real world objects (Witharana and Civco, 2014)
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