Automatic processing of multi-spectral images
Statistical decision theory for automatic classification of multiple spectral image points
STATISTICAL DECISION THEORY↗
SEARCH · Engineering Papers
Results for “multi-spectral images”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Multi-spectral image dissector camera system
The image dissector sensor for the Earth Resources Program is evaluated using contrast and reflectance data. The ground resolution obtainable for low contrast at the targeted signal to noise ratio of 1.8 was defined. It is concluded that the system is capable of achieving the detection of small, low contrast ground targets from satellites.
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Solid state high resolution multi-spectral imager CCD test phase
The program consisted of measuring the performance characteristics of charge coupled linear imaging devices, and a study defining a multispectral imaging system employing advanced solid state photodetection techniques.
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Structural geology investigation in the republics of Dahomey and Togoland, Africa, using ERTS-1 multi-spectral images
The author has identified the following significant results. Recent geological studies in the Republics of Dahomey and Togoland put in light a new chronology and propose a schema indicating that the structural geology of this region is very complicated. The new observations made possible by the ERTS images concern the main orientations, the folded units, and the lithology. The correlation between different types of laterite and the petrology of the basement seems possible, and is the most significant result of this investigation but unfortunately conducted with poor quality images because of atmospheric haze.
Weecksteen, G.↗
FUNSTAT and statistical image representations
General ideas of functional statistical inference analysis of one sample and two samples, univariate and bivariate are outlined. ONESAM program is applied to analyze the univariate probability distributions of multi-spectral image data.
Parzen, E.↗
The Earth Resources Technology Satellites ERTS-A and -B.
The Earth Resources Technology Satellites, ERTS-A and -B, are a significant part of the NASA Earth Resources Survey Program. A general description of the requirements and implementation of the ERTS system - sensors, spacecraft, operational control, and data processing developed to acquire, process, and distribute multi-spectral images of the earth's surface is presented.
Scull, W. E.↗
Cluster compression algorithm: A joint clustering/data compression concept
The Cluster Compression Algorithm (CCA), which was developed to reduce costs associated with transmitting, storing, distributing, and interpreting LANDSAT multispectral image data is described. The CCA is a preprocessing algorithm that uses feature extraction and data compression to more efficiently represent the information in the image data. The format of the preprocessed data enables simply a look-up table decoding and direct use of the extracted features to reduce user computation for either image reconstruction, or computer interpretation of the image data. Basically, the CCA uses spatially local clustering to extract features from the image data to describe spectral characteristics of the data set. In addition, the features may be used to form a sequence of scalar numbers that define each picture element in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. Various forms of the CCA are defined and experimental results are presented to show trade-offs and characteristics of the various implementations. Examples are provided that demonstrate the application of the cluster compression concept to multi-spectral images from LANDSAT and other sources.
Hilbert, E. E.↗
The use of VAS satellite data in weather analysis, prediction and diagnosis
Imagery available with the Goes satellite visible-IR spin-scan radiometer (VISSR) atmospheric sounders (VAS) are examined in terms of mid- and low-tropospheric moisture sensing and mesoscale soundings. The VAS can be operated in a Dwell Sounding mode (DS) involving preprogrammed scanning of a specific area using any number of combinations of 12 channels between 4-15 microns. A second, multi-spectral imaging mode (MSI) comprises operation of two IR channels simultaneously when time constraints are in effect. Case studies are presented to demonstrate the effectiveness of VAS imagery for characterizing mesoscale moisture conditions when identifying severe storms. The moisture patterns of the upper and lower troposphere are made visible, and a 6.7 micron channel image can be overlaid on a low level moisture field to delineate fields of potential instability.
Petersen, R. A.↗
Use of VAS multispectral data for sea surface temperature determination
The Visible Infrared Spin Scan Radiometer Atmospheric Sounder (VAS) is a radiometer possessing eight visible channel detectors and six thermal detectors that sense infrared radiation in 12 spectral bands. Housed in the GOES satellite, VAS spins in a west to east direction at 100 rpm and achieves spatial coverage at resolutions of 1 km in the visible and 7 or 14 km in the infrared by stepping a scan mirror in a north to south direction. Designed for multipurpose applications, the VAS can be operated in two different modes: (1) a multi-spectral imaging (MSI) mode, and (2) a dwell sounding (DS) mode. The MSI mode of operation is used for sea surface temperature (SST) determination. Currently, a full-disk MSI image for SST determination is received every hour, 18 hours a day during weekdays. This MSI mode of operation for SST consists of data obtained from wavelengths centered at 3.9 microns (channel 12), 11.6 microns (channel 8), and 12.6 microns (channel 7) as well as visible data.
Bates, J.↗
Hydrogeology of closed basins and deserts of South America, ERTS-1 interpretations
Images from the Earth Resources Technology Satellite (ERTS-1) contain data useful in studies of hydrogeology, geomorphology, and paleoclimatology. Sixteen Return Beam Vidicon (RBV) images and 15 Multi-Spectral Scanner (MSS) images were studied. These covered deserts and semidesert areas in southwestern Bolivia, northwestern Argentina, northern Chile, and southeastern Peru from July 30 to November 17, 1972. During the first 3 months after launching, high-quality cloud-free imagery was obtained over approximately 90 percent of the region of interior drainage, or an area of 170,000 square miles.
Stoertz, G. E.↗
Quaternary geologic map of Minnesota
The Quaternary Geologic Map of Minnesota is a compilation based both on the unique characteristics of satellite imagery and on the results of previous field investigations, both published and unpublished. The use of satellite imagery has made possible the timely and economical construction of this map. LANDSAT imagery interpretation proved more useful than expected. Most of the geologic units could be identified by extrapolating from specific sites where the geology had been investigated into areas where little was known. The excellent geographic registry coupled with the multi-spectral record of these images served to identify places where the geologic materials responded to their ecological environment and where the ecology responded to the geologic materials. Units were well located on the map at the scale selected for the study. Contacts between till units could be placed with reasonable accuracy. The reference points that were used to project delineations between units (rivers, lakes, hills, roads and other features), which had not been accurately located on early maps, could be accurately located with the help of the imagery. The tonal and color contrasts, the patterns reflecting geologic change and the resolution of the images permitted focusing attention on features which could be represented at the final scale of the map without distraction by other interesting but site-specific details.
Goebel, J. E.↗
Simultaneous on-chip generation of violet, blue, cyan, green, yellow, orange, and red light from an octave-spanning infrared frequency comb
An integrated, multi-spectral visible-light source could significantly benefit technologies such as displays, medical imaging, spectroscopy, visible-light communications, and astrophysics. However, despite recent advances in chip-scale visible lasers, simultaneously generating light of all colors in a single chip has been challenging. Existing solutions are either not suitable for full chip-scale integration, or are fundamentally difficult to scale. Here we demonstrate the simultaneous on-chip generation of infrared, red, orange, yellow, green, cyan, blue, and violet light. Leveraging the low loss, low dispersion, and high density of modes of an adiabatic multimode silicon nitride (SiN) microresonator, we use a single infrared pump of moderate power (~130 mW) to produce an octave-spanning infrared frequency comb that is then converted to different portions of the visible spectrum. We measure non-mode-locked combs and soliton steps corresponding to mode-locked states, making our comb generator suitable for applications that demand either low or high coherence. Since the required pump power is compatible with high-power lasers demonstrated in the same SiN platform, our multi-octave light generator can be fully integrated in a chip-scale form factor. We envision that such a light source will be a catalyst for the development and deployment of miniaturized multi-spectral technologies for quantum systems, medical imaging, displays, and spectroscopy.
47 OTHER INSTRUMENTATION↗
Hyper Spectral Anomaly Detection
Anomaly detection is a common machine learning (ML) task with growing importance in the fields of imaging, quality assurance, and multiple security related disciplines. Anomaly detection is more difficult than traditional machine learning methods due to the inherent unlabeled nature of the datasets. Existing anomaly detection architectures commonly face challenges with explainability, retaining information related to the relational structure of the data, and false positive rates. Hyperspectral Imaging Anomaly Detection (HSI) is a statistical model that employs vertex and edge weighted graphs to preserve the data’s relationships on different topographical scales. The model is able to generalize from anomaly detection in 2D images to novel datasets related to cyber-security. Furthermore, the use of multi-spectral and other filtering methods results in fewer false positives and increases the explainability of model predictions. When applying HSI to cyber-security datasets, we are able to successfully detect malicious activity with a relatively high degree of accuracy.
97 - MATHEMATICS AND COMPUTING↗
Multiple-frame, full resolution Landsat mosaicking to standard map projections
Landsat digital data are presently available by frames whose size and location are determined by satellite orbit. The utility of Landsat data was increased by the image processing support for JPL's planetary program which provided the basic software and procedures necessary for image mosaicking. The computer software has been extended to perform this task on Landsat Multi-spectral scanner imagery and a ten frame digital mosaic of the Southern California desert has been completed. Major processing steps include location of geographic points in the digital frame and of common geographic points in adjacent frames, conversion to a map projection, lateral 'rubber sheet' correction of the digital frames, brightness correction of adjacent frames, and mosaicking of the frames to eliminate overlap and produce a single large frame.
Zobrist, A. L.↗
Some aspects of adaptive transform coding of multispectral data
This paper concerns a data compression study pertaining to multi-spectral scanner (MSS) data. The motivation for this undertaking is the need for securing data compression of images obtained in connection with the Landsat Follow-On Mission, where a compression of at least 6:1 is required. The MSS data used in this study consisted of four scenes: Tristate, consisting of 256 pels per row and a total of 512 rows - i.e., (256x512), (2) Sacramento (256x512), (3) Portland (256x512), and (4) Bald Knob (200x256). All these scenes were on digital tape at 6 bits/pel. The corresponding reconstructed scenes of 1 bit/pel (i.e., a 6:1 compression) are included.
Ahmed, N.↗
Measurement of the earth resources technology satellite /ERTS-1/ multi-spectral scanner OTF from operational imagery
The optical transfer function (OTF) of some typical ERTS-1 multispectral imagery was obtained by comparison of matched sets of aircraft underflight and ERTS photographic and digital images. One-dimensional OTF analysis consisted in obtaining U-2 and ERTS microdensitometer scans followed by density to transmission conversion, microdensitometer aperture correction, exposure calibration, scan correlation scale optimization, OTF calculation, obtaining a form weighted average of the OTFs, transformation of the OTFs back to the spatial domain (giving the line spread function or LSF), and application of a window function to the LSF resulting in a smoothed OTF. Date-to-date comparison of ERTS OTFs showed a drop in quality on April 4, 1973, compared with January 4, 1973.
Schowengerdt, R. A.↗
Oregon trails revisted
Oregon State University's Environmental Remote Sensing Applications Laboratory (ERSAL) has six full-time researchers with expertise in a variety of biological, Earth, atmospheric and computer sciences as well as image interpretation and statistical techniques. The primary emphasis of the ERSAL research and demonstration program is the development and application of remote sensing technology in operational resource management programs. LANDSAT multi-spectral, multi-date digital data and imagery are utilized in concert with high altitude NASA-acquired photography, low altitude ERSAL-acquired photography, and field observations and data to provide customized, inexpensive and useful final products. Synopses are given of 9 applications projects conducted in Oregon.
Lewis, A. J.↗
Coordinated aircraft and ship surveys for determining impact of river inputs on great lakes waters. Remote sensing results
The remote sensing results of aircraft and ship surveys for determining the impact of river effluents on Great Lakes waters are presented. Aircraft multi-spectral scanner data were acquired throughout the spring and early summer of 1976 at five locations: the West Basin of Lake Erie, Genesee River - Lake Ontario, Menomonee River - Lake Michigan, Grand River - Lake Michigan, and Nemadji River - Lake Superior. Multispectral scanner data and ship surface sample data are correlated resulting in 40 contour plots showing large-scale distributions of parameters such as total suspended solids, turbidity, Secchi depth, nutrients, salts, and dissolved oxygen. The imagery and data analysis are used to determine the transport and dispersion of materials from the river discharges, especially during spring runoff events, and to evaluate the relative effects of river input, resuspension, and shore erosion. Twenty-five LANDSAT satellite images of the study sites are also included in the analysis. Examples of the use of remote sensing data in quantitatively estimating total particulate loading in determining water types, in assessing transport across international boundaries, and in supporting numerical current modeling are included. The importance of coordination of aircraft and ship lake surveys is discussed, including the use of telefacsimile for the transmission of imagery.
Raquet, C. A.↗