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In this letter, we address the characterization of objects in 3-D sonar images of the water column obtained by a multibeam echo sounder. Compared with classic 2-D images from a monobeam echo sounder, these 3-D images provide finer scale observation of the pelagic biomasses and new tools to characterize 3-D distributions. By viewing object patterns as realizations of spatial point processes, we investigate descriptive spatial statistics. This method is then applied to 3-D fisheries acoustics data set for characterization of the distribution of pelagic fish schools. Reported experiments illustrate the relevance of the proposed descriptors. The comparison of our method with 2-D sonar data analysis further demonstrates the information gain from using 3-D sonar imagery.