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At least 325 records · Page 18

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NASA NTRS1974

Automatic land use classification using Skylab S-192 multispectral data

Investigation of the accuracy attainable in automatic land use classification using 13 bands of multispectral data from the Skylab S-192 scanner. Classification to levels containing seven urban classes, five agricultural, and three water classes is shown to be achievable. With 17 classes, a classification accuracy of 72% was obtained. A wide spectral range, including the thermal band, appears to be most useful for distinguishing urban classes. Agricultural and water classes can be separated using spectral bands covering the visible to far IR.

Kirvida, L.↗

NASA NTRS1974

Implementation of an advanced table look-up classifier for large area land-use classification

Software employing Eppler's improved table look-up approach to pattern recognition has been developed, and results from this software are presented. The look-up table for each class is a computer representation of a hyperellipsoid in four dimensional space. During implementation of the software Eppler's look-up procedure was modified to include multiple ranges in order to accommodate hollow regions in the ellipsoids. In a typical ERTS classification run less than 6000 36-bit computer words were required to store tables for 24 classes. Classification results from the improved table look-up are identical with those produced by the conventional method, i.e., by calculation of the maximum likelihood decision rule at the moment of classification. With the new look-up approach an entire ERTS MSS frame can be classified into 24 classes in 1.3 hours, compared to 22.5 hours required by the conventional method. The new software is coded completely in FORTRAN to facilitate transfer to other digital computers.

Jones, C.↗

NASA NTRS1974

STANSORT - Stanford Remote Sensing Laboratory pattern recognition and classification system

The principal barrier to routine use of the ERTS multispectral scanner computer compatible tapes, rather than photointerpretation examination of the images, has been the high computing costs involved due to the large quantity of information (4 Mbytes) contained in a scene. STANSORT, the interactive program package developed at Stanford Remote Sensing Laboratories alleviates this problem, providing an extremely rapid, flexible and low cost tool for data reduction, scene classification, species searches and edge detection. The primary classification procedure, utilizing a search with variable gate widths, for similarities in the normalized, digitized spectra is described along with associated procedures for data refinement and extraction of information. The more rigorous statistical classification procedures are also explained.

Honey, F. R.↗

NASA NTRS1974

Computer derived coastal water classifications via spectral signatures

In April 1973, the National Environmental Satellite Service conducted a remote sensing investigation within the coastal waters of the New York Bight. Remote sensor records acquired from the ERTS-1 Multispectral Scanner and the Bendix 24 Channel Multispectral Scanner records flown on the NASA C-130 were used for water mass classification. Computer-derived classifications are discussed and compared. Such features as the Hudson River's turbid discharge plumes, acid waste and shelf water are examined in terms of their distribution of suspended particulates (2-203 microns), transmissivity, diffuse attenuation, incident and returned spectral irradiances. The characteristics of these features and their relationship to the computer derived classifications are presented and discussed with respect to radiative transfer theory.

Clark, D. K.↗

NASA NTRS1975

Enhancement of LANDSAT imagery by combination of multispectral classification and principal component analysis

Digital enhancement of LANDSAT imagery was obtained by application of principal component analysis separately on each of the classes previously determined in a multispectral classification step. Each part of the image is thus enhanced whatever its spectral signature may be. A document was obtained which is a synthesis between a conventional image and an ordinary computerized classification. The interpreter can, at the same time, take into account not only the classification but also other features such as context and structure. An example is discussed with the help of geological interpretation.

Fontanel, A.↗

NASA NTRS1976

Lacie phase 1 Classification and Mensuration Subsystem (CAMS) rework experiment

An experiment was designed to test the ability of the Classification and Mensuration Subsystem rework operations to improve wheat proportion estimates for segments that had been processed previously. Sites selected for the experiment included three in Kansas and three in Texas, with the remaining five distributed in Montana and North and South Dakota. The acquisition dates were selected to be representative of imagery available in actual operations. No more than one acquisition per biophase were used, and biophases were determined by actual crop calendars. All sites were worked by each of four Analyst-Interpreter/Data Processing Analyst Teams who reviewed the initial processing of each segment and accepted or reworked it for an estimate of the proportion of small grains in the segment. Classification results, acquisitions and classification errors and performance results between CAMS regular and ITS rework are tabulated.

Chhikara, R. S.↗

NASA NTRS1976

Canonical analysis for increased classification speed and channel selection

The quadratic form can be expressed as a monotonically increasing sum of squares when the inverse covariance matrix is represented in canonical form. This formulation has the advantage that, in testing a particular class hypothesis, computations can be discontinued when the partial sum exceeds the smallest value obtained for other classes already tested. A method for channel selection is presented which arranges the original input measurements in that order which minimizes the expected number of computations. The classification algorithm was tested on data from LARS Flight Line C1 and found to reduce the sum-of-products operations by a factor of 6.7 in comparison with the conventional approach. In effect, the accuracy of a twelve-channel classification was achieved using only that CPU time required for a conventional four-channel classification.

Eppler, W.↗

NASA NTRS1975

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.↗

NASA NTRS1976

Forest Classification Accuracy as Influenced by Multispectral Scanner Spatial Resolution

The author has identified the following significant results. A supervised classification within two separate ground areas of the Sam Houston National Forest was carried out for two sq meters spatial resolution MSS data. Data were progressively coarsened to simulate five additional cases of spatial resolution ranging up to 64 sq meters. Similar processing and analysis of all spatial resolutions enabled evaluations of the effect of spatial resolution on classification accuracy for various levels of detail and the effects on area proportion estimation for very general forest features. For very coarse resolutions, a subset of spectral channels which simulated the proposed thematic mapper channels was used to study classification accuracy.

Nalepka, R. F.↗

NASA NTRS1976

The use of spatial characteristics for the improvement of multispectral classification of remotely sensed data

Two techniques for the classification of multispectral remotely sensed data - the image processing technique of texture features, which is modeled after the human visual system, and the ECHO (Extraction and Classification of Homogeneous Objects) numerical technique - are examined. These two spatial analysis techniques are compared using Landsat imagery of an area of Indiana as an example, and it is found that the numerical approach is superior in classification accuracy and more efficient computationally.

Wiersma, D. J.↗

NASA NTRS1977

Application of LANDSAT data to wetland study and land use classification in west Tennessee

The Obion-Forked Deer River Basin in northwest Tennessee is confronted with several acute land use problems which result in excessive erosion, sedimentation, pollution, and hydrologic runoff. LANDSAT data was applied to determine land use of selected watershed areas within the basin, with special emphasis on determining wetland boundaries. Densitometric analysis was performed to allow numerical classification of objects observed in the imagery on the basis of measurements of optical densities. Multispectral analysis of the LANDSAT imagery provided the capability of altering the color of the image presentation in order to enhance desired relationships. Manual mapping and classification techniques were performed in order to indicate a level of accuracy of the LANDSAT data as compared with high and low altitude photography for land use classification.

Jones, N. L.↗

NASA NTRS1977

The influence of multispectral scanner spatial resolution on forest feature classification

Inappropriate spatial resolution and corresponding data processing techniques may be major causes for non-optimal forest classification results frequently achieved from multispectral scanner (MSS) data. Procedures and results of empirical investigations are studied to determine the influence of MSS spatial resolution on the classification of forest features into levels of detail or hierarchies of information that might be appropriate for nationwide forest surveys and detailed in-place inventories. Two somewhat different, but related studies are presented. The first consisted of establishing classification accuracies for several hierarchies of features as spatial resolution was progressively coarsened from (2 meters) squared to (64 meters) squared. The second investigated the capabilities for specialized processing techniques to improve upon the results of conventional processing procedures for both coarse and fine resolution data.

Sadowski, F. G.↗

NASA NTRS1978

Analytical models and system topologies for remote multispectral data acquisition and classification

Simple analytical models are presented of the radiometric and statistical processes that are involved in multispectral data acquisition and classification. Also presented are basic system topologies which combine remote sensing with data classification. These models and topologies offer a preliminary but systematic step towards the use of computer simulations to analyze remote multispectral data acquisition and classification systems.

Huck, F. O.↗

NASA NTRS1979

Spectral feature classification and spatial pattern recognition

This paper introduces a spatial pattern recognition processing concept involving the use of spectral feature classification technology and coherent optical correlation. The concept defines a hybrid image processing system incorporating both digital and optical technology. The hybrid instrument provides simplified pseudopattern images as functions of pixel classification from information embedded within a real-scene image. These pseudoimages become simplified inputs to an optical correlator for use in a subsequent pattern identification decision useful in executing landmark pointing, tracking, or navigating functions. Real-time classification is proposed as a research tool for exploring ways to enhance input signal-to-noise ratio as an aid in improving optical correlation. The approach can be explored with developing technology, including a current NASA Langley Research Center technology plan that involves a series of related Shuttle-borne experiments. A first-planned experiment, Feature Identification and Location Experiment (FILE), is undergoing final ground testing, and is scheduled for flight on the NASA Shuttle (STS2/flight OSTA-1) in 1980. FILE will evaluate a technique for autonomously classifying earth features into the four categories: bare land; water; vegetation; and clouds, snow, or ice.

Sivertson, W. E., Jr.↗

NASA NTRS1979

On evaluating clustering procedures for use in classification

The problem of evaluating clustering algorithms and their respective computer programs for use in a preprocessing step for classification is addressed. In clustering for classification the probability of correct classification is suggested as the ultimate measure of accuracy on training data. A means of implementing this criterion and a measure of cluster purity are discussed. Examples are given. A procedure for cluster labeling that is based on cluster purity and sample size is presented.

Pore, M. D.↗

NASA NTRS1980

Evaluating LANDSAT wildland classification accuracies

Procedures to evaluate the accuracy of LANDSAT derived wildland cover classifications are described. The evaluation procedures include: (1) implementing a stratified random sample for obtaining unbiased verification data; (2) performing area by area comparisons between verification and LANDSAT data for both heterogeneous and homogeneous fields; (3) providing overall and individual classification accuracies with confidence limits; (4) displaying results within contingency tables for analysis of confusion between classes; and (5) quantifying the amount of information (bits/square kilometer) conveyed in the LANDSAT classification.

Toll, D. L.↗

NASA NTRS1980

Evaluation of several schemes for classification of remotely sensed data

Various numerical analysis schemes for the classification of remotely sensed data are evaluated with respect to their capabilities for crop identification. A per point Gaussian maximum likelihood classifier, per point sum-of-normal-densities classifier, per point linear classifier, per point Gaussian maximum likelihood decision tree classifier and a texture-sensitive per field Gaussian maximum likelihood classifier were applied to seven sets of Landsat MSS data on several crop types and regions. The results of the implementation of the classifiers indicate that, given a representative set of training statistics, the choice of classification algorithm of the differentiation of corn and soybeans from one another and from other crop types made relatively little difference in accuracy, whereas the use of a different training method affected the accuracy significantly. In addition, the linear classifier is found to be the easiest for the analyst to use and to cost least in computer time per classification.

Hixson, M.↗

NASA NTRS1981

A comparison of remote sensing techniques for Minnesota wetlands classification

A wetland classification study in a typically complex 650 sq km test site in east central Minnesota compared the time, cost and accuracy of manually interpreted 1:24,000 scale color infrared aerial photographs with digital analysis of Landsat data. The comparison was between the same general wetland and non-wetland classes; accuracy of both systems was evaluated with intensive ground verification. For the same general classes, the overall mapping accuracy was 96 percent for the aerial photo interpretation and 71 percent for Landsat double-date classification. Results with maximum likelihood and SECHO classifiers were the same, 71 percent, while mapping accuracy with a layered classifier was only 66 percent. Compared to a general geometric correction and a single-date data set, geographic position and classification accuracy improved when a precision geometric correction and a double-date data set were used. Landsat digital analysis was faster, 24 vs. 90 days, but photointerpretation was more economical at $0.15/hectare (including all costs of procurement, processing and analysis), compared with Landsat costs of $0.35/hectare (not including costs of data procurement, processing prior to delivery to user and related overhead).

Werth, L. F.↗