Spatial Statistics and Digital Image Analysis (1991) / Chapter Skim
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1. Introduction
1. Introduction
Pages 1-8
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The eventual aim might be to convert this vast quantity of two-dimensional, multivariate data into a simple crop inventory that can be usecI, for instance, to estimate the total potential winter-wheat harvest of a country. Satellite images are also used for other purposes, such as locating and monitoring the condition of rocket silos in foreign territories; here, there are analogies in computer vision, where object recognition is one of many
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Other examples inclucle the CAT-scan, in which X-ray images taken from several different positions are combined to reconstruct views of cross-sections of the anatomy of a patient; positron emission tomography (PET) and single photon emission computed tomography (SPECT)
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Incidentally, a somewhat similar problem occurs in the epidemiology of rare, noncommunicable diseases, such as particular forms of cancer, when incidence rates are observed over a specific period of time in a large number of contiguous administrative regions and the objective is to estimate underlying differences in risk. In each region, the number of cases can be viewed as an observation from a Poisson distribution, with mean proportional both to the population and to the risk there.
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Computer resources are also required to execute the large number of repetitive operations typically required in the application of a technique of spatial statistics or digital image analysis to a field of science. Advances in work station technology, data base management, data compression, and data archiving, coupled with the expansion of computer network topologies, now provide the necessary technical infrastructure for the (levelopment of joint university curricula in spatial statistics and digital image analysis and in its cross-disciplinary application of methods to a broad range of scientific, engineering, and medical problems.
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, near-surface velocity computation from image sequences, and ice boundary detection in satellite data are given. Chapter 4 applies methods of spatial statistics to a broad range of environmental science issues: spatial variation in solar radiation, environmental impact design, and modeling of precipitation using space-time point processes.
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Statistical models, which generally are noncausal in nature, draw conclusions about data sets without necessarily providing future predictive capability. Physical models, which generally include time dependence, attempt to provide a prognostic capability about a physical process based on available data sets.
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Often such applications of NDE involve ultrasound detection and tomographic reconstruction, and/or two-dimensional signal processing and methods of digital image analysis. Methods of digital image analysis and image reconstruction also wiD be used to analyze the large-volume spatial data sets necessary to study a variety of issues (e.g., acid rain, ozone depletion, and global warming)
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D Ripley, Using spatial models as priors in astronomical image analysis, '7.
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