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The AgRISTARS supporting research projects in the areas of data systems, scene radiation, and pattern recognition are reviewed. The objectives, activities, and accomplishments of FY-80 and the objectives and status of FY-81 programs are described.
Data obtained by HCMM satellite over a complex area in eastern Spain were evaluated and found to be most useful in studying macrostructures in geology and in analyzing marine currents, layers, and areas (although other satellites provide more data). The upper scale to work with HCMM data appears to be 1:2.000.000. Techniques used in preprocessing, processing, and analyzing imagery are discussed as well as methods for pattern recognition. Surface temperatures obtained for soils, farmlands, forests, geological structures, and coastal waters are discussed. Suggestions are included for improvements needed to achieve better results in geographic areas similar to the study area.
The Mark II airborne spectroradiometer system was flown over several geologic test sites in western Nevada. The infrared mineral absorption bands were observed and recorded for the first time using an airborne system with high spectral resolution in the 2.0 to 2.5 micron region. The data show that the hydrothermal alteration zone minerals, carbonates, and other minerals are clearly visible in the airborne survey mode. The finer spectral features that distinguish the various minerals with infrared bands are also clearly visible in the airborne survey data. Using specialized computer pattern recognition methods, it is possible to identify mineralogy and map alteration zones and lithologies by airborne spectroradiometer survey techniques.
Human decision making in multiple process monitoring situations is considered. It is proposed that human decision making in many multiple process monitoring situations can be modeled in terms of the human's detection of process related events and his allocation of attention among processes once he feels event have occurred. A mathematical model of human event detection and attention allocation performance in multiple process monitoring situations is developed. An assumption made in developing the model is that, in attempting to detect events, the human generates estimates of the probabilities that events have occurred. An elementary pattern recognition technique, discriminant analysis, is used to model the human's generation of these probability estimates. The performance of the model is compared to that of four subjects in a multiple process monitoring situation requiring allocation of attention among processes.
The improvement in land cover classification achieved by merging microwave data with Landsat MSS data is examined. To produce a merged data set for analysis and comparison, a registration procedure by which a set of Seasat SAR digital data was merged with the MSS data is described. The Landsat MSS data and the merged Landsat/Seasat data sets were processed using conventional multichannel spectral pattern recognition techniques. An analysis of the classified data sets indicates that while Landsat data delineate different forest types (i.e., deciduous/coniferous) and allow some species separation, SAR data provide additional information related to plant canopy configuration and vegetation density as associated with varying water regimes, and therefore allow for further subdivision in the classification of forested wetlands of the coastal region of the southern United States.
Robots are defined and described for various applications. The key feature of robots is programmability, which allows teleoperation, repair work in hazardous situations, and unsupervised operation in industrial functions. Two types of robots now exist: special purpose, with equipment for a specific task; and general purpose, which include nonservo-controlled robots, servo-controlled robots, and sensory control robots. Sensory robots are the most sophisticated, and are equipped with both internal control sensors and external sensors such as TV cameras, pressure detectors, laser range finders, etc. Sensory feedback to a central computer enables the robots to make appropriate modifications to the control program to adapt to new situations. Pattern recognition and scans for size are features of the TV sensors, and programs to develop a universal effector (hand) are outlined. Finally, robot programming in terms of manual, walkthrough, and textual methods are described, and the potential uses of robots for space and undersea construction and repair are discussed.
It is proposed that human decisionmaking performance in multiple process monitoring situations can be modeled in terms of the detection of process related events and the allocation of attention among processes once events are felt to have occurred. An elementary pattern recognition technique, discriminant analysis, is used to generate estimates of event occurrence probability. A queueing theory framework is then utilized to incorporate these probabilities as well as other task characteristics into the solution of the attention allocation problem. The performance of the model is compared with that of subjects in two experiments.
Existing modeling methods in distributed parameter systems are directed to field problems for which a single equation gives an adequate representation. As a result, the techniques are severely taxed when coefficients of the governing partial differential equation (PDE) have relatively complex functional forms. The field is postulated to consist of contiguous regions that are easily modeled. During the modeling process itself, the regional boundaries are identified using a method based on pattern recognition (PR) techniques. The method was tested on computer simulations of a water quality resource system. Quantitative measures are developed and used to demonstrate the feasibility of applying the methodology to realistic distributed parameter systems (DPS's).
Monolayer properties of O2 on graphite are calculated using a pattern recognition, optimization scheme. Equilibrium monolayers are predicted at two different densities with properties in agreement with recent X-ray diffraction, specific heat, and neutron scattering data. Properties of the extremely low density regime are calculated using a model based upon a distribution of two-dimensional O2 clusters. The results are consistent with experimental evidence.
Ground cover maps are important tools to a wide array of users. Over the past three decades, much progress has been made in supplementing planimetric and topographic maps with ground cover details obtained from aerial photographs. The present investigation evaluates the feasibility of using computer maps of ground cover from satellite input tapes. Attention is given to the selection of test sites, a satellite data processing system, a multispectral image analyzer, general purpose computer-generated maps, the preliminary evaluation of computer maps, a test for areal correspondence, the preparation of overlays and acreage estimation of land cover types on the Landsat computer maps. There is every indication to suggest that digital multispectral image processing systems based on Landsat input data will play an increasingly important role in pattern recognition and mapping land cover in the years to come.
The term remote sensing is defined as well as ideas such as class, pattern, feature, pattern recognition, feature extraction, and theme. The electromagnetic spectrum is examined especially those wavelength regions available to remote sensing. Relevant energy and wave propagation laws are discussed and the characteristics of emitted and reflected radiation and their detection are investigated. The identification of classes by their spectral signatures, the multispectral approach, and the principal types of sensors and platforms used in remote sensing are also considered.
Background information and exercises are provided to: (1) establish or expand understanding of the concepts, methods, and terminology of computer processing of image producing data; (2) develop insight into the advantages of computer based image processing compared with the photointerpretation approach for processing, classifying, interpreting, and applying remote sensing data; (3) foster a broad perspective on the principal of the main techniques for image enhancement, pattern recognition, and thematic classification; (4) appreciate the pros and cons of batch and interactive modes of image analysis; (5) examine and evaluate some specific computer generated products for subscenes in Pennsylvania and New Jersey; and (6) interrelate these particular examples of output with more theoretical explanations of computer processing strategies and procedures.
Research results and accomplishments of sixteen tasks in the following areas are described: (1) corn and soybean scene radiation research; (2) soil moisture research; (3) sampling and aggregation research; (4) pattern recognition and image registration research; and (5) computer and data base services.
Scene radiation and atmospheric effects, mathematical pattern recognition and image analysis, information evaluation and utilization, and electromagnetic measurements and signal handling are considered. Research issues in sensors and signals, including radar (SAR) reflectometry, SAR processing speed, registration, including overlay of SAR and optical imagery, entire system radiance calibration, and lack of requirements for both sensors and systems, etc. were discussed.
The feasibility of intrusive rock mapping was investigated and criteria for regional geological mapping established at the scale of 1:500,00 in polycyclic and polymetamorphic areas using the logic method of photointerpretation of LANDSAT imagery and radar from the RADAMBRASIL project. The spectral behavior of intrusive rocks, was evaluated using the interactive multispectral image analysis system (Image-100). The region of Campos (city) in northern Rio de Janeiro State was selected as the study area and digital imagery processing and pattern recognition techniques were applied. Various maps at the 2:250,000 scale were obtained to evaluate the results of automatic data processing.
An important question in cluster analysis and pattern recognition is the determination of the number of clusters into which a given population should be divided. Frequently, particularly when certain specific clustering methods are being used, the number of clusters is taken to be equal to the number of modes, or local maxima, in the probability density function underlying the given data set. The use of kernal density estimates in mode estimation is discussed. The test statistic to be used is defined and a bootstrap technique for assessing significance is given. An illustrative application is followed by an examination of the asymptotic behavior of the test statistic.
Kershaw County, South Carolina was selected as the study site for analyzing simulated thematic mapper MSS data and dual-polarized X-band synthetic aperture radar (SAR) data. The impact of the improved spatial and spectral characteristics of the LANDSAT D thematic mapper data on computer aided analysis for forest cover type mapping was examined as well as the value of synthetic aperture radar data for differentiating forest and other cover types. The utility of pattern recognition techniques for analyzing SAR data was assessed. Topics covered include: (1) collection and of TMS and reference data; (2) reformatting, geometric and radiometric rectification, and spatial resolution degradation of TMS data; (3) development of training statistics and test data sets; (4) evaluation of different numbers and combinations of wavelength bands on classification performance; (5) comparison among three classification algorithms; and (6) the effectiveness of the principal component transformation in data analysis. The collection, digitization, reformatting, and geometric adjustment of SAR data are also discussed. Image interpretation results and classification results are presented.
Although remote sensing is currently multidisciplinary in its applications, many of its terms come from the engineering sciences, particularly from the field of pattern recognition. Scholars from fields such as the social sciences, botany, and biology, may experience initial difficulty with remote sensing terminology, even though parallel concepts exist in their own fields. Some parallel concepts and terminologies from nonengineering fields, which might enhance the understanding of remote sensing concepts in an interdisciplinary situation are identified. Feedbacks which this analogue strategy might have on remote sensing itself are explored.