Irregular Tessellation and Statistical Modeling Based Regionalized Segmentation for SAR Intensity Image

This paper presents a regionalized segmentation method for synthetic aperture radar (SAR) intensity images based on tessellation with irregular polygons. In the proposed method, the image domain is partitioned into a collection of irregular polygons, which are constructed using sets of nodes and are used to fit homogeneous regions with arbitrary shapes. Each partitioned polygon is taken as the basic processing unit. Assuming the intensities of the pixels in the polygon follow an independent and identical gamma distribution, the likelihood of the image intensities is modeled. After defining the prior distributions of the tessellation and the parameters for the likelihood model, a posterior probability model can be built based on the Bayes theorem as a segmentation model. To obtain optimal segmentation, a reversible-jump Markov chain Monte Carlo (RJMCMC) algorithm is designed to simulate from the segmentation model, where the move operations include updating the gamma distribution parameter, updating labels, moving nodes, merging polygons, splitting polygons, adding nodes, and deleting nodes. Experiments were carried out on synthetic and real SAR intensity images using the proposed method while the regular and Voronoi tessellation-based methods were also preformed for comparison. Our results show the proposed method overcomes some intrinsic limitations of current segmentation methods and is able to generate good results for homogeneous regions with different shapes.

Low Precipitation Observational Bias at Cooperative Climatological Stations1

An upward bias exists in probabilities of 0 or trace weekly total precipitation since small amounts often occur undetected and are recorded as 0 or trace at climatological stations where observations are made only once a day. Exact evaluation and correction of this bias is difficult, but individual estimates of 1-week P(0,T) for substations in the north-central region of the United States can be multiplied by a factor of 0.8 to reduce them to more reasonable values. Although the opportunity for low precipitation bias decreases with increasing length of period, significant bias still persists in the 2- and 3-week estimates in the western part of the north-central region during the winter season. Since the probability of measurable precipitation is 1–P(0,T), the P(0,T) bias is carried into the precipitation probabilities, but compensating biases in the gamma-distribution parameter estimates apparently contain most of the bias in the 0.01 to 0.09-inch interval.