Parameterization of Shape and Compactness in Object-based Image Classification Using Quickbird-2 Imagery

In recent years, object based image analysis (OBIA) has spread out and become a widely accepted technique for the analysis of remotely sensed data. OBIA deals with grouping pixels into homogenous objects based on spectral, spatial and textural features of contiguous pixels in an image. The first stage of OBIA, named as image segmentation, is the most prominent part of object recognition. In this study, multiresolution segmentation, which is a region-based approach, was employed to construct image objects. In the application of multi-resolution, three parameters, namely shape, compactness and scale must be set by the analyst. Segmentation quality remarkably influences the fidelity of the thematic maps and accordingly the classification accuracy. Therefore, it is of great importance to search and set optimal values for the segmentation parameters. In the literature, main focus has been on the definition of scale parameter, assuming that the effect of shape and compactness parameters is limited in terms of achieved classification accuracy. The aim of this study is to deeply analyze the influence of shape/compactness parameters by varying their values while using the optimal scale parameter determined by the use of Estimation of Scale Parameter (ESP-2) approach. A pansharpened Qickbird-2 image covering Trabzon, Turkey was employed to investigate the objectives of the study. For this purpose, six different combinations of shape/compactness were utilized to make deductions on the behavior of shape and compactness parameters and optimal setting for all parameters as a whole. Objects were assigned to classes using nearest neighbor classifier in all segmentation observations and equal number of pixels was randomly selected to calculate accuracy metrics. The highest overall accuracy (92.3%) was achieved by setting the shape/compactness criteria to 0.3/0.3. The results of this study indicate that shape/compactness parameters can have significant effect on classification accuracy with 4% change in overall accuracy. Also, statistical significance of differences in accuracy was tested using the McNemar's test and found that the difference between poor and optimal setting of shape/compactness parameters was statistically significant, suggesting a search for optimal parameterization instead of default setting.