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Using Nimbus 3 medium resolution infrared radiometer measurements, a multispectral cloud type identification method is developed. This method includes a fourth spectral region (20-23 microns), the use of radiative transfer theory, and a semiquantitative evaluation of satellite cloud type estimates with concurrent high resolution photography from aircraft flights.
Description of the technology applied to a 24-channel multispectral scanner developed as a tool to investigate the portion of the electromagnetic spectrum between 0.34 and 13.0 microns. Energy reflected or emitted from the terrain and system calibration sources is collected by the scan mirror, reflected into the collecting optics, and brought to focus in a plane containing a square aperture. A dichroic optical element splits the energy which passes through the aperture into two wavelength bands in which wavelengths are greater than and less than 2.0 microns, respectively. These two wavelength bands are dispersed spectrally by two grating spectrometers. The energy from 24 distinct spectral intervals is transformed into electrical signals by 24 detector/preamplifier combinations. These signals then become the inputs to a video processor in an airborne electronics console.
Tests have been made to compare linear and quadratic techniques for processing multispectral scanner data. The tests have been limited to a few selected sets of agricultural data. Two aspects of processing were studied. The first, the selection of a subset of channels to be used in the decision function, was found to be faster by a factor of 50 when a linear method was used. Second, in recognition processing, our linear decision rule produced a lower error rate and utilized a larger number of channels for equal processing times. Nevertheless, when the criterion is lowest possible error rate, irregardless of processing time, the quadratic rule is preferable.
This paper presents the definitions of texture dependent features which can be obtained in terms of the spatial frequencies of small sections of remotely sensed multispectral data. The features are made independent of the direction of view by defining them as symmetric functions of the spatial frequencies sensed with various viewing directions. Several textural features are defined and experimental results indicating existence of signatures in these features are presented. Preliminary experiments have been performed on the classification of 60 samples, 10 from each of the following 6 categories - grass, trees, water, staked tomatoes, treated ground tomatoes, and untreated ground tomatoes. Classifications of the training samples using only one feature at a time indicate that several of the features yield classification efficiencies higher than 65%. The efficiency increases considerably when combinations of these features are used.
The multispectral scanner system installed in the NASA C-130 aircraft for use in the Earth Observations Aircraft Program constitutes a 24-channel imaging spectrometer that senses electromagnetic energy in the spectral interval from .34 to 13 microns. Energy reflected or emitted from the terrain and system calibration sources is collected by a scan mirror, reflected into collecting optics, and brought to a focus in a plane containing a 0.09 inch square aperture. A dichroic optical element splits the energy which passes through the aperture into two wavelength bands (wavelengths above and below 2 microns). These wavelength bands are then dispersed spectrally into 24 distinct spectral bands by two grating spectrometers. The spectral intervals are transformed into electrical signals by separate detector-preamplifier combinations, and the signals are used as inputs to a video processor in an airborne electronics console. System operation and performance are described.
Description of a two-part clustering technique consisting of (a) a sequential statistical clustering, which is essentially a sequential variance analysis, and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum-likelihood classification techniques.
An experiment is described which is designed to produce visible-reflective infrared ratio images from ERTS satellite data. The ERTS-A multispectral scanner has two channels in the visible-reflective infrared wavelength region which should be capable of duplicating aircraft scanner results. The average reflectances in the four ERTS-A MSS spectral bands for a few minerals and some vegetation are tabulated. The mineral laboratory samples were granular, with a particle diameter range of 250 to 1200 micrometer. If there were no spectral atmospheric or illumination variation effects, the ratio of energies in band 1 to band 4 would permit discrimination of pyroxenes (found in basic and ultrabasic rocks), quartz (found in acidic rocks), limonite and hematite (iron oxides), and green vegetation, on the basis of their respective ratio magnitudes.
Multispectral scanner reflectance data were sampled for bare soil, cotton, sorghum, corn, and citrus at four dates during a growing season (April, May, June, and July 1969) to develop a time-dependent signature for crop and soil discrimination. Discrimination tests were conducted for single-date and multidate formats using training and test data sets. For classifications containing several crops, the multidate or temporal approach improved discrimination compared with the single-date approach. The multidate approach also preserved recognition accuracy better in going from training fields to test fields than the single-date analysis. The spectral distinctiveness of bare soil versus vegetation resulted in essentially equal discrimination using single-date versus multidate data for those two categories.
Optimum channel selection among 12 channels of multispectral scanner imagery identified six as providing the best information for computerized classification of 11 plant communities and two nonvegetation classes. Intensive preprocessing of the spectral data was required to eliminate bidirectional reflectance effects of the spectral imagery caused by scanner view angle and varying geometry of the plant canopy. Generalized plant community types - forest, grassland, and hydrophytic systems - were acceptably classified based on ecological analysis. Serious, but soluble, errors occurred with attempts to classify specific community types within the grassland system. However, special clustering analyses provided for improved classification of specific grassland communities.
A linear decision rule to reduce the time required for processing multispectral scanner data is developed. Test results are presented which justify the use of the new rule for digital processing whenever both accuracy and processing time are important. A method of evaluating the performance of the rule is also developed and applied to the problem of choosing a subset of channels. A technique used to find linear combinations of channels is described. The ability to extend signatures throughout a small area of approximately fifty square miles is tested. After preprocessing, signatures derived from the first of seven overlapping data sets are applied to all data sets. The test results show that the average probability of misclassification tends to increase with an increase in the number of data sets over which the signatures are extended.
A review was made of infrared and multispectral scanners. Operating principles, sensitivity, and costs are discussed.
The results of flight tests to evaluate the performance of the 24 channel multispectral scanner are reported. The flight plan and test site are described along with the time response and channel registration. The gain and offset drift, and moire patterns are discussed. Aerial photographs of the test site are included.
Adaptive data processing procedures are applied to the problem of classifying objects in a scene scanned by multispectral sensor. These procedures show a performance improvement over standard nonadaptive techniques. Some sources of error in classification are identified and those correctable by adaptive processing are discussed. Experiments in adaptation of signature means by decision-directed methods are described. Some of these methods assume correlation between the trajectories of different signature means; for others this assumption is not made.
Special emphasis was on corn plants, and the healthy targets were differentiated from stressed ones by remote sensing. Infrared radiometry of plants is reviewed thoroughly with emphasis on agricultural crops. Theory and error analysis of the determination of emittance of a natural target by radiometer is discussed. Experiments were conducted on corn (Zea mays L.) plants with long wavelength spectroradiometer under field conditions. Analysis of multispectral scanner data of ten selected flightlines of Corn Blight Watch Experiment of 1972 indicated: (1) There was no regular pattern of the mean response of the higher level/levels blighted corn vs. lower level/levels blighted corn in any of the spectral channels. (2) The greater the difference between the blight levels, the more statistically separable they usually were in subsets of one, two, three and four spectral channels.
Multispectral mapping accomplishments using the M7 airborne scanner are summarized. The M7 system is described and overall results of specific data collection flight operations since June 1971 are reviewed. A major advantage of the M7 system is that all spectral bands of the scanner are in common spatial registration, whereas in the M5 they were not.
The author has identified the following significant results. Range vegetation types have been successfully mapped on a portion of the 68,000 acre study site located west of Baggs, Wyoming, using ERTS-1 imagery. These types have been ascertained from field transects over a five year period. Comparable studies will be made with EREP imagery. Above-ground biomass estimation studies are being conducted utilizing double sampling techniques on two similar study sites. Information obtained will be correlated with percent relative reflectance measurements obtained on the ground which will be related to image brightness levels. This will provide an estimate of above-ground green biomass with multispectral imagery.
The improvement and extension of the capabilities of the Environmental Research Institute of Michigan processing facility in handling multispectral data are discussed. Improvements consisted of implementing hardware modifications which permitted more rapid access to the recorded data through improved numbering and indexing of such data. In addition, techniques are discussed for handling data from sources other than the ERIM M-5 and M-7 scanner systems.
The basic information is presented, which is required for start-up and operation of two long-wavelength focal-plane and cooler assemblies, including the amplifiers and temperature control systems. The focal plane systems, referred to as the long wavelength spectrometer (LWS) were developed for direct replacement of Arrays 3 and 4 into the multispectral scanner presently being operated by the NASA Manned Spacecraft Center Facility, and Laboratory Support Branch. The equipment is comprised of two major sub-assemblies: Array 3 with three indium antimonide detector channels and Array 4 with seven mercury doped Germanium detector channels. Each array is mounted on a cryogenic cooler and includes the vacuum housings, mounting hardware (x, y, z translation and rotation stages) and detector signal conditioning, temperature control and monitoring electronics. The two arrays were designed to operate independently and do not share common equipment (viz power supplies, housings, mounts, etc.).