Key Issues in the Analysis of Remote Sensing Data: A report on the workshop

The procedures of a workshop assessing the state of the art of machine analysis of remotely sensed data are summarized. Areas discussed were: data bases, image registration, image preprocessing operations, map oriented considerations, advanced digital systems, artificial intelligence methods, image classification, and improved classifier training. Recommendations of areas for further research are presented

Bayesian classification in a time-varying environment

The problem of classifying a pattern based on multiple observation made in a time-varying environment is analyzed. The identity of the pattern may itself change. A Bayesian solution is derived, after which the conditions of the physical situation are invoked to produce a cascade classifier model. Experimental results based on remote sensing data demonstrate the effectiveness of the classifier

Implementation and evaluation of ILLIAC 4 algorithms for multispectral image processing

Data concerning a multidisciplinary and multi-organizational effort to implement multispectral data analysis algorithms on a revolutionary computer, the Illiac 4, are reported. The effectiveness and efficiency of implementing the digital multispectral data analysis techniques for producing useful land use classifications from satellite collected data were demonstrated

A method for classification of multisource data using interval-valued probabilities and its application to HIRIS data

A method of classifying multisource data in remote sensing is presented. The proposed method considers each data source as an information source providing a body of evidence, represents statistical evidence by interval-valued probabilities, and uses Dempster's rule to integrate information based on multiple data source. The method is applied to the problems of ground-cover classification of multispectral data combined with digital terrain data such as elevation, slope, and aspect. Then this method is applied to simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data by dividing the dimensionally huge data source into smaller and more manageable pieces based on the global statistical correlation information. It produces higher classification accuracy than the Maximum Likelihood (ML) classification method when the Hughes phenomenon is apparent

A method of classification for multisource data in remote sensing based on interval-valued probabilities

An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method

A multiprocessor implementation of a contextual image processing algorithm

There are no author-identified significant results in this report

Evaluating Growth Proposals

Professor Foster's invitation to this symposium hinted pretty strongly that he would like me to say something trenchant about continued federal fiscal responsibility, after the initial pump-priming, in a growth center strategy. At the time I quite happily agreed. Later reflection, however, convinced me that this was not one of the key questions for the Halifax region nor in fact a very important one at all. What I propose to discuss instead is a structured way for a community to study and debate its future. The twin assumptions, that the future is malleable in important ways -- "ours to design" -- and that its designers ought to be the present residents, are now part of the conventional planning wisdom. Just how far they are (or ought to be) true, though, is still a good question. Part of this paper has to do with shortcomings in present ways in which communities now study their futures, especially in the more analytic methods propounded by fellow professionals, and with suggestions for alternative. approaches. Along the way, and in the blissful absence of real data, I shall mention aspects of some potential futures that may be disquieting and maybe mildly provocative, and allude from time to time to intergovernmental finance. The heart of the argument is that every alternative proposal for this region's future ought to be examined for its feasibility, its implications, and community valuations of those implications; and that in addition to the interests of the groups party to the decision, a prudent community will sequentially evaluate a proposal in terms of resilience, equity and (only then) efficiency. The order is important. There is little sense in arguing about the desirability of infeasible policies or unattainable goals. Structuring the learning process in this fashion allows many more options to be addressed with the same resources of community time and energy. Most of the rest of this paper is concerned with explaining the italicized words above. Let me begin by defining one of the many possible alternative futures of the Halifax-Dartmouth metropolitan region as an exemplary straw horse

The decision tree approach to classification

A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers

Layered classification techniques for remote sensing applications

The layered classifier method is outlined and several applications to pattern classification for which the approach is suited are discussed