Edge Dependent Chinese restaurant process for Very High Resolution (VHR) satellite image over-segmentation

Abstract

Image over-segmentation aims to partition an image into spatially adjacent and spectrally homogeneous regions. It could reduce the complexity of image representation and enhance the efficiency of subsequent image processing. Previously, many methods for image over-segmentation have been proposed, but almost of them need to assign model parameters in advance, e.g., the number of segments. In this paper, a nonparametric clustering model is employed to the over-segmentation of Very High Resolution (VHR) satellite images, in which the number of segments can automatically be inferred from the observed data. The proposed model is called the Edge Dependent Chinese restaurant process (EDCRP), which extends the distance dependent Chinese restaurant process to make full use of local image structure information, i.e., edges. Experimental results show that the presented methods outperform state of the art methods for image over-segmentation in terms of both metrics based direct evaluation and classification based indirect evaluation.