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This semi-tutorial paper describes the process of using orthogonal transforms for the purposes of encoding TV picture data. Results pertaining to a 6:1 data compression experiment using the Walsh-Hadamard transform are included.
The Return Beam Vidicon (RBV) is a high-performance electronic image sensor and electrical storage component. It can accept continuous or discrete exposures. Information can be read out with a single scan or with many repetitive scans for either signal processing or display. Resolution capability is 10,000 TV lines/height, and at 100 lp/mm, performance matches or exceeds that of film, particularly with low-contrast imagery. Electronic zoom can be employed effectively for image magnification and data compression. The high performance and flexibility of the RBV permit wide application in systems for reconnaissance, scan conversion, information storage and retrieval, and automatic inspection and test. This paper summarizes the characteristics and performance parameters of the RBV and cites examples of feasible applications.
The author has identified the following significant results. Effects of terrain topography in mountainous forested regions on LANDSAT signals and classifier training were found to be significant. The aspect of sloping terrain relative to the sun's azimuth was the major cause of variability. A relative insolation factor could be defined which, in a single variable, represents the joint effects of slope and aspect and solar geometry on irradiance. Forest canopy reflectances were bound, both through simulation, and empirically, to have nondiffuse reflectance characteristics. Training procedures could be improved by stratifying in the space of ancillary variables and training in each stratum. Application of the Tasselled-Cap transformation for LANDSAT data acquired over forested terrain could provide a viable technique for data compression and convenient physical interpretations.
A basic concept of Multispectral Scanner data processing was developed for use in agricultural inventories; namely, to introduce spatial coordinates of each pixel into the vector description of the pixel and to use this information along with the spectral channel values in a conventional unsupervised clustering of the scene. The result is to isolate spectrally homogeneous field-like patches (called blobs). The spectral mean vector of a blob can be regarded as a defined feature and used in a conventional pattern recognition procedure. The benefits of use are: ease in locating training units in imagery; data compression of from 10 to 30 depending on the application; reduction of scanner noise and consequently potential improvements in classification/proportion estimation performances.
A basic concept of MSS data processing has been developed for use in agricultural inventories; namely, to introduce spatial coordinates of each pixel into the vector description of the pixel and to use this information along with the spectral channel values in a conventional unsupervised clustering of the scene. The result is to isolate spectrally homogeneous field-like patches (called 'blobs'). The spectral mean vector of a blob can be regarded as a defined feature and used in a conventional pattern recognition procedure. The benefits of use are: ease in locating training units in imagery; data compression of from 10 to 30 depending on the application; reduction of scanner noise and consequently potential improvements in classification/proportion estimation performances.
The general problem of image data compression is discussed briefly with attention given to the use of Karhunen-Loeve transforms, suboptimal systems, and block quantization. A survey is then conducted encompassing the four categories of adaptive systems: (1) adaptive transform coding (adaptive sampling, adaptive quantization, etc.), (2) adaptive predictive coding (adaptive delta modulation, adaptive DPCM encoding, etc.), (3) adaptive cluster coding (blob algorithms and the multispectral cluster coding technique), and (4) adaptive entropy coding.
The source parameters of digitized analog data are discussed. Different data compression schemes are outlined and analysis of their implementation are presented. Finally, bandwidth compression techniques are given for video signals.
Computer programs have been developed for use in collecting and processing data from a PDS scanning microdensitometer. The goal is to obtain fast and simple algorithms for handling an entire astronomical photograph with one-pass digitization. This capability is realized by a real-time detection scheme that provides a data compression of a factor of 100, and a processing program that produces a catalog of magnitudes, color indices, and positions for up to 90,000 multicolor stellar images.
The Pioneer Venus large probe neutral mass spectrometer (LNMS) uses a single focusing magnetic sector field mass analyzer with mass range of 1-208 amu, resolution sufficient to separate the mercury isotopes, and sensitivity sufficient to detect minor constituents in the 1 ppm range relative to the CO2 in the Venus atmosphere. A combination of ion and chemical pumping is used to maintain a vacuum in the mass analyzer and to remove the atmosphere gases which enter the ion source chamber through a special leak. A microprocessor controls the operation of the instrument through a highly efficient peak stepping and data compression program, permitting acquisition of a complete mass spectrum roughly once each minute.
The paper surveys three specific large space structures which have been analyzed to determine the technology needed to achieve a fully operational system. These are the multipurpose platforms; satellite power systems, and a deployable antenna. Further attention is also given to those technology needs which can be satisfied by ground based technology (simulation), such as payload carrier modification or design, platform element connectors and fittings, data compression and storage equipment, man-machine interface and productivity assessment, and astronaut aids. Also covered are those technology and performance parameters which require demonstration in orbit.
Two characteristics of remote sensing - data volume and information volatility - are discussed. Emphasis is placed on the NASA Applications Data Service and Remotely Piloted Vehicle (RPV) television. It is noted that the data volume and information volatility in a given remote sensing system require a careful choice of the point in the end-to-end system at which data compression and/or information extraction take place.
The elimination of redundancy in data to maximize processing speed and minimize storage requirements were objectives in a feasibility study of a microprocessor based oculometer system that would be portable in size and flexible in use. The appropriate architectural design of the signal processor, improved optics, and the reduction of size, weight, and power to the system were investigated. A flow chart is presented showing the strategy of the design. The simulation for developing microroutines for the high speed algorithmic processor subsystem is discussed as well as the Karhunen-Loeve transform technique for data compression.
A systematic introduction to the concepts and techniques of computer image processing and recognition is presented. Consideration is given to such topics as image formation and perception; computer representation of images; image enhancement and restoration; reconstruction from projections; digital television, encoding, and data compression; scene understanding; scene matching and recognition; and processing techniques for linear systems.
Two evolutionary infrared remote sensing experiments provide the basis for the development of an operational mapping capability for geology exploration. A 10-band radiometer scheduled for an early Shuttle flight has completed an aircraft flight program which demonstrated the utility of a number of narrow spectral channels. A multispectral mapper utilizing an infrared area array detector to acquire simultaneous images in multiple wavelengths is being assembled. A design concept for an operational sensor which employs area arrays for registered multispectral image data acquisition is under study. The sensor would utilize onboard spectral band selection, radiometric correction, and data compression to satisfy the demanding requirements of the user community.
The NASA NEEDS program goals present a requirement for on-board signal processing to achieve user-compatible, information-adaptive data acquisition. This volume addresses the impact of data set selection on data formatting required for efficient telemetering of the acquired satellite sensor data. More specifically, the FILE algorithm developed by Martin-Marietta provides a means for the determination of those pixels from the data stream effects an improvement in the achievable system throughput. It will be seen that based on the lack of statistical stationarity in cloud cover, spatial distribution periods exist where data acquisition rates exceed the throughput capability. The study therefore addresses various approaches to data compression and truncation as applicable to this sensor mission.
An instrument concept that uses solid-state array imaging has been developed for a future land observing system. The design concept is responsive to a variety of use needs and provides improved capabilities over the planned Landsat Thematic Mapper. A comparison of the differing approaches to the instrument design was made, resulting in the selection of a concept which uses a spectrograph coupled to a line-array imager to provide simultaneous spatial and spectral resolution. The design provides an inherent solution to the problem of achieving precise registration among the spectral bands. Data processing on the focal plane is used to select the spectral bands and their band widths. Onboard capabilities include radiometric correction, selection of instantaneous field-of-view and swath width, and data compression.
The utility of the multispectral resource sampler (MRS) was examined by users in the following disciplines: agriculture, atmospheric studies, engineering, forestry, geology, hydrology/oceanography, land use, and rangelands/soils. Modifications to the sensor design were recommended and the desired types of products and number of scenes required per month were indicated. The history, design, capabilities, and limitations of the MRS are discussed as well as the multilinear spectral array technology which it uses. Designed for small area inventory, the MRS can provide increased temporal, spectral, and spatial resolution, facilitate polarization measurement and atmospheric correction, and test onboard data compression techniques. The advantages of using it along with the thematic mapper are considered.
A model for recording, processing, presentation, and analysis of radar images in digital form is presented. The observed image is represented as having two random components, one which models the variation due to the coherent addition of electromagnetic energy scattered from different objects in the illuminated areas. This component is referred to as fading. The other component is a representation of the terrain variation which can be described as the actual signal which the radar is attempting to measure. The combination of these two components provides a description of radar images as being the output of a linear space-variant filter operating on the product of the fading and terrain random processes. In addition, the model is applied to a digital image processing problem using the design and implementation of enhancement scene. Finally, parallel approaches are being employed as possible means of solving other processing problems such as SAR image map-matching, data compression, and pattern recognition.