FMT (Flight Software Memory Tracker) for Cassini Mission Operations - A Ground Data Analysis Tool
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ground data analysis↗
Engineering topics
Kan, E. P.
Publications and source records attributed to Kan, E. P..
Report on Computer Programs for Robotic Vision
Collection of programs supports robotic research. Report describes computer-vision software library NASA's Jet Propulsion Laboratory. Programs evolved during past 10 years of research into robotics. Collection includes low- and high-level image-processing software proved in applications ranging from factory automation to spacecraft tracking and grappling. Programs fall into several overlapping categories. Image utilities category are low-level routines that provide computer access to image data and some simple graphical capabilities for displaying results of image processing.
Cunningham, R. T.↗
Integrated multi-sensory control of space robot hand
Dexterous manipulation of a robot hand requires the use of multiple sensors integrated into the mechanical hand under distributed microcomputer control. Where space applications such as construction, assembly, servicing and repair tasks are desired of smart robot arms and robot hands, several critical drives influence the design, engineering and integration of such an electromechanical hand. This paper describes a smart robot hand developed at the Jet Propulsion Laboratory for experimental use and evaluation with the Protoflight Manipulator Arm (PFMA) at the Marshall Space Flight Center (MSFC).
Bejczy, A. K.↗
Postprocessing classification images
Program cleans up remote-sensing maps. It can be used with existing image-processing software. Remapped images closely resemble familiar resource information maps and can replace or supplement classification images not postprocessed by this program.
Kan, E. P.↗
The Ten-Ecosystem Study - Landsat ADP mapping of forest and rangeland in the United States
The Ten-Ecosystem Study was designed to assess the maximum information content of Landsat data and its utility for large area classification using a uniform technical approach on the 10 generalized forest and rangeland ecosystems of the United States. Conclusions on the feasibility of using Landsat remote sensing automatic data processing methods, selecting the best seasons, analyzing costs and the effects of site complexity, miscellaneous analysis, problems, and recommendations were derived from 2 years of study, the project being three-fourths completed.
Kan, E. P.↗
The ten-ecosystem study investigation plan
With the continental United States divided into ten forest and grassland ecosystems, the Ten Ecosystem Study (TES) is designed to investigate the feasibility and applicability of state-of-the-art automatic data processing remote sensing technology to inventory forest, grassland, and water resources by using Land Satellite data. The study will serve as a prelude to a possible future nationwide remote sensing application to inventory forest and rangeland renewable resources. This plan describes project design and phases, the ten ecosystem, data utilization and output, personnel organization, resource requirements, and schedules and milestones.
Kan, E. P.↗
A new computer approach to mixed feature classification for forestry application
A computer approach for mapping mixed forest features (i.e., types, classes) from computer classification maps is discussed. Mixed features such as mixed softwood/hardwood stands are treated as admixtures of softwood and hardwood areas. Large-area mixed features are identified and small-area features neglected when the nominal size of a mixed feature can be specified. The computer program merges small isolated areas into surrounding areas by the iterative manipulation of the postprocessing algorithm that eliminates small connected sets. For a forestry application, computer-classified LANDSAT multispectral scanner data of the Sam Houston National Forest were used to demonstrate the proposed approach. The technique was successful in cleaning the salt-and-pepper appearance of multiclass classification maps and in mapping admixtures of softwood areas and hardwood areas. However, the computer-mapped mixed areas matched very poorly with the ground truth because of inadequate resolution and inappropriate definition of mixed features.
Kan, E. P.↗
An ad hoc map evaluation procedure
An ad hoc map evaluation procedure is proposed which is most suitable for evaluating low-resolution classification maps against high resolution ground truth maps, such as maps against interpreted aircraft photographs. Commonly practiced sampling and evaluation procedures are impracticable in this context because of difficulties in registration and in comparing the samples. This ad hoc procedure is designed to overcome these two major problems, and its practicability is discussed. Two widely accepted parameters are estimated by the new procedure; namely, the probability of correct classification and the proportion biases. Statistical qualifications are also provided.
Kan, E. P.↗
A new computer approach to map mixed forest features and postprocess multispectral data
A computer technique for mapping mixed softwood and hardwood stands in multispectral satellite imagery of forest regions is described. The purpose of the technique is to obtain smoother resource maps useful in timber harvesting operations. The computer program relies on an algorithm which assesses the size and similarity of adjacent sections on satellite imagery (Landsat-1 data is used) and constructs, through an iteration of the basic algorithm, a more general map of timber mixtures, eliminating the mottled appearance of the raw imagery. Despite difficulties in the experimental analysis of a Texas forest, apparently due to relatively low resolution of the Landsat data, the computer classification approach outlined is suggested as a generally applicable method of creating serviceable maps from multispectral imagery.
Kan, E. P.↗
Timber type separability in Southeastern United States on LANDSAT-1 MSS data
A quantitative, computer-aided study was made on the spectral separability of timber types and condition classes in the Southeastern United States, using LANDSAT-1 multispectral scanner data. It was concluded that LANDSAT-1 could be used effectively to discriminate the gross forest features of softwood, hardwood, and regeneration. The only significant detectable age difference would be between an established forest versus a young (or denuded) forest. The red or near infrared bands would be better for discrimination; phenological early and late spring data would be better than winter. And a temporal analysis would be superior to single-season analysis. Lastly, two spectral bands would be most cost effective for computer analysis. The study site was Sam Houston National Forest of East Texas, a typical forest in the Flatwoods Zone, Southern Region, U. S. Forest Service.
Kan, E. P.↗
Data resolution versus forestry classification and modeling
This paper examines the effects on timber stand computer classification accuracies caused by changes in the resolution of remotely sensed multispectral data. This investigation is valuable, especially for determining optimal sensor and platform designs. Theoretical justification and experimental verification support the finding that classification accuracies for low resolution data could be better than the accuracies for data with higher resolution. The increase in accuracy is constructed as due to the reduction of scene inhomogeneity at lower resolution. The computer classification scheme was a maximum likelihood classifier.
Kan, E. P.↗
A new image enhancement algorithm with applications to forestry stand mapping
The theory and applications are presented of a new image enhancement algorithm which refines computer classification maps of multispectral data. The refinement eliminates connected sets smaller than a prespecified size and merges them to the surrounding area. Conventional practices in forestry timber stand mapping requires small geographic areas to be absorbed by surrounding large areas to form homogeneous stands. This homogeneity is often incompatible with the statistical formulation of homogeneity. Elements within a timber stand which should be labeled as one feature often correspond to more than one class mapped by existing computer classification techniques. The new algorithm is designed to postprocess classification maps to result in more usable timber stand maps. The new image enhancement technique is compared with an accepted neighbor-checking postprocessing technique, demonstrating the superiority of the new technique for forestry stand mapping.
Kan, E. P.↗
Multispectral scanner data processing over Sam Houston National Forest
The Edit 9 forest scene, a computer processing technique, and its capability to map timber types in the Sam Houston National Forest, are evaluated. Special efforts were made to evaluate existing computer processing techniques in mapping timber types using ERTS-1 and aircraft data, and to provide an opportunity to open up new research and development areas in forestry data.
Reeves, C. A.↗
The JSC clustering program ISOCLS and its applications
The clustering program ISOCLS developed at the Johnson Space Center, Houston, Texas, has been extensively used in the pattern analysis and classification of remote sensor data collected by aircraft and by the Earth Resources Technology Satellite ERTS-1. This paper discusses the theory behind this clustering algorithm. Several new ideas that have been incorporated in ISOCLS are discussed. Among these are the novel philosophy of operation behind the procedure, which assumes that a population (i.e., a class or a cluster) can be treated as the union of an appropriate number of subpopulations, and the termination of the clustering program by a 'chaining algorithm.' Finally, this paper reports the results of the application of ISOCLS to an investigation on rangeland vegetation mapping using ERTS-1 data.
Kan, E. P.↗