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NASA NTRS1981

Using ecological zones to increase the detail of Landsat classifications

Changes in classification detail of forest species descriptions were made for Landsat data on 2.2 million acres in northwestern California. Because basic forest canopy structures may exhibit very similar E-M energy reflectance patterns in different environmental regions, classification labels based on Landsat spectral signatures alone become very generalized when mapping large heterogeneous ecological regions. By adding a seven ecological zone stratification, a 167% improvement in classification detail was made over the results achieved without it. The seven zone stratification is a less costly alternative to the inclusion of complex collateral information, such as terrain data and soil type, into the Landsat data base when making inventories of areas greater than 500,000 acres.

Fox, L., III↗

NASA NTRS1981

Use of collateral information to improve LANDSAT classification accuracies

Methods to improve LANDSAT classification accuracies were investigated including: (1) the use of prior probabilities in maximum likelihood classification as a methodology to integrate discrete collateral data with continuously measured image density variables; (2) the use of the logit classifier as an alternative to multivariate normal classification that permits mixing both continuous and categorical variables in a single model and fits empirical distributions of observations more closely than the multivariate normal density function; and (3) the use of collateral data in a geographic information system as exercised to model a desired output information layer as a function of input layers of raster format collateral and image data base layers.

Strahler, A. H.↗

NASA NTRS1981

Lake classification in Vermont

In order to comply with the Federal Clean Water Act and, in so doing, develop a procedure to periodically update the classification, the State of Vermont evaluated the ability of LANDSAT to detect general water quality and specific water quality parameters in Vermont lakes. Unsupervised and supervised classifications as well as regression analyses were used to examine LANDSAT data from Lake Champlain and from four small nearby lakes. Unsupervised and supervised classifications were found to be of somewhat limited value. Regression analyses revealed a good correlation between depth-integrated total phosphorus concentrations and LANDSAT band 4 data (r2= 0.92) and between Secchi disk transparencies and LANDSAT band 4 data (r2 - 0.85). No correlation was found between depth-integrated chlorophyll-a samples and LANDSAT data. Vermont is expanding this LANDSAT evaluation to include the remaining lakes in the state greater than twenty acres and steps are being taken to incorporate LANDSAT into the state's ongoing water quality monitoring programs.

Garrison, V.↗

NASA NTRS1983

Analysis of multispectral data using an unsupervised classification technique: Application to VAS

A statistical classification method based on clustering of multidimensional histograms was applied to several channels of the VAS multispectral imagery. The method automatically discriminates and classifies atmospheric ground features such as cloud types, atmospheric moisture patterns, ocean, or ground. Such a clustering method has the advantage of forming natural data groupings, without a priori classification. Clusters are not limited by straight lines or plane surfaces as is the case in threshold methods. The method was applied to simultaneous full resolution images from channels 8 (11.2 micron), 10 (6.7 micron), and 12 (3.9 micron). Twenty image segments of 64 by 64, 12 image segments of 128 by 128, and 4 image segments of 254 by 254 picture elements were analyzed. In addition, normal VISSR mode images at 1800, 1830, and 2000 GMT were used to identify the classes. The gray levels measured along a scan line and the result of the classification scheme (dashed curves) for the three channels investigated are shown. Each point of the image is affected to a class. Each class is identified by a center of gravity that is represented by a vector in the three dimensional space of gray levels.

Szejwach, G.↗

NASA NTRS1981

Crop classification using airborne radar and LANDSAT data

Airborne radar data acquired with a 13.3 GHz scatterometer over a test-site near Colby, Kansas were used to investigate the statistical properties of the scattering coefficient of three types of vegetation cover and of bare soil. A statistical model for radar data was developed that incorporates signal-fading and natural within-field variabilities. Estimates of the within-field and between-field coefficients of variation were obtained for each cover-type and compared with similar quantities derived from LANDSAT images of the same fields. The classification accuracy provided by LANDSAT alone, radar alone, and both sensors combined was investigated. The results indicate that the addition of radar to LANDSAT improves the classification accuracy by about 10; percentage-points when the classification is performed on a pixel basis and by about 15 points when performed on a field-average basis.

Ulaby, F. T.↗

NASA NTRS1982

Optimization of a Non-traditional Unsupervised Classification Approach for Land Cover Analysis

The conditions under which a hybrid of clustering and canonical analysis for image classification produce optimum results were analyzed. The approach involves generation of classes by clustering for input to canonical analysis. The importance of the number of clusters input and the effect of other parameters of the clustering algorithm (ISOCLS) were examined. The approach derives its final result by clustering the canonically transformed data. Therefore the importance of number of clusters requested in this final stage was also examined. The effect of these variables were studied in terms of the average separability (as measured by transformed divergence) of the final clusters, the transformation matrices resulting from different numbers of input classes, and the accuracy of the final classifications. The research was performed with LANDSAT MSS data over the Hazleton/Berwick Pennsylvania area. Final classifications were compared pixel by pixel with an existing geographic information system to provide an indication of their accuracy.

Boyd, R. K.↗

NASA NTRS1981

Contextual classification on PASM

The use of N microprocessors in the SIMD mode of parallel processing to do classifications almost N times faster than a single microprocessor is discussed. Examples of contextual classifiers are given, uniprocessor algorithms for performing contextual classifications are presented, and their computational complexity is analyzed. The SIMD mode of parallel processing is defined and PASM is overviewed. The presented uniprocessor algorithms are used as a basis for developing parallel algorithms for performing computationally intensive contextual classifications.

Siegel, H. J.↗

NASA NTRS1982

Hierarchical modeling for image classification

As part of the California Integrated Remote Sensing System's (CIRSS) San Bernardino County Project, the use of data layers from a geographic information system (GIS) as an integral part of the Landsat image classification process was investigated. Through a hierarchical modeling technique, elevation, aspect, land use, vegetation, and growth management data from the project's data base were used to guide class labeling decisions in a 1976 Landsat MSS land cover classification. A similar model, incorporating 1976-1979 Landsat spectral change data in addition to other data base elements, was used in the classification of a 1979 Landsat image. The resultant Landsat products were integrated as additional layers into the data base for use in growth management, fire hazard, and hydrological modeling.

Likens, W.↗

NASA NTRS1983

Classification and area estimation of land covers in Kansas using ground-gathered and LANDSAT digital data

Ground-gathered data and LANDSAT multispectral scanner (MSS) digital data from 1981 were analyzed to produce a classification of Kansas land areas into specific types called land covers. The land covers included rangeland, forest, residential, commercial/industrial, and various types of water. The analysis produced two outputs: acreage estimates with measures of precision, and map-type or photo products of the classification which can be overlaid on maps at specific scales. State-level acreage estimates were obtained and substate-level land cover classification overlays and estimates were generated for selected geographical areas. These products were found to be of potential use in managing land and water resources.

May, G. A.↗

NASA NTRS1983

Analysis and classification of human error

The literature on human error is reviewed with emphasis on theories of error and classification schemes. A methodology for analysis and classification of human error is then proposed which includes a general approach to classification. Identification of possible causes and factors that contribute to the occurrence of errors is also considered. An application of the methodology to the use of checklists in the aviation domain is presented for illustrative purposes.

Rouse, W. B.↗

NASA NTRS1984

Atmospheric effect on classification of finite fields

The atmospheric effect on the upward radiance of sunlight scattered from the earth-atmosphere system is strongly influenced by the contrasts between fields and their sizes. In this paper, the radiances above finite fields are computed to simulate radiances measured by a satellite. A simulation case including 11 agricultural fields and four natural fields (water, soil, savanah, and forest) is used to test the effect of field size, background reflectance, and optical thickness of the atmosphere on the classification accuracy. For a given atmospheric turbidity, the atmospheric effect on classification of surface features may be much stronger for nonuniform surfaces than for uniform surfaces. Therefore, the classification accuracy of agricultural fields and urban areas is dependent not only on the optical characteristics of the atmosphere, but also on the size of the surface elements to be classified and their contrasts. It is concluded that new atmospheric correction methods, which take into account the finite size of the fields, are needed.

Kaufman, Y. J.↗

NASA NTRS1984

Improvements in forest classification and inventory using remotely sensed data

A Forest Classification and Inventory System (Focis) has been developed for large area forest inventories on the basis of Landsat and digital terrain data. It is a potential advantage of Focis that it can provide timely inventories at a reduced cost which are easily updated. The Klamath National Forest in Northern California was employed as test area for the initial development of Focis. Focis is constantly being changed and improved. Two recent additions to the inventory system include a spatial filtering algorithm which improves the spatial coherence in the final classified image, and a modification to the classification procedure designed to reduce the adverse effects of local topography on classification accuracy. Attention is given to a Focis overview, spatial filtering, the interface with the forest service geographic information system, and efforts to reduce the influence of topography.

Woodcock, C. E.↗

NASA NTRS1985

Comparison of MSS and TM Data for Landcover Classification in the Chesapeake Bay Area: a Preliminary Report

An area bordering the Eastern Shore of the Chesapeake Bay was selected for study and classified using unsupervised techniques applied to LANDSAT-2 MSS data and several band combinations of LANDSAT-4 TM data. The accuracies of these Level I land cover classifications were verified using the Taylor's Island USGS 7.5 minute topographic map which was photointerpreted, digitized and rasterized. The the Taylor's Island map, comparing the MSS and TM three band (2 3 4) classifications, the increased resolution of TM produced a small improvement in overall accuracy of 1% correct due primarily to a small improvement, and 1% and 3%, in areas such as water and woodland. This was expected as the MSS data typically produce high accuracies for categories which cover large contiguous areas. However, in the categories covering smaller areas within the map there was generally an improvement of at least 10%. Classification of the important residential category improved 12%, and wetlands were mapped with 11% greater accuracy.

Mulligan, P. J.↗

NASA NTRS1983

Spaceborne SAR data for land-cover classification and change detection

Supervised maximum-likelihood classifications of Seasat, SIR-A, and Landsat pixel data demonstrated that SIR-A data provided the most accurate discrimination (72 percent) between five land-cover categories. Spatial averaging of the SAR data improved classification accuracy significantly due to a reduction in both fading and within-field variability. The best multichannel classification accuracy (97.5 percent) was achieved by combining the SIR-A data with two Seasat images (ascending and descending orbits). In addition, semiquantitative analysis of Seasat-A digital data shows that orbital SAR imagery can be successfully used for multitemporal detection of change related to hydrologic and agronomic conditions by using simple machine processing techniques.

Brisco, B.↗

NASA NTRS1983

Petrography and classification of refractory inclusions in the Allende and Mokoia CV3 chondrites

Results are reported for a comprehensive petrographic survey of the refractory inclusions in the Allende and Mokoia CV3 chondrites. More than 600 refractory inclusions in 22 thin sections of the meteorites were studied by optical and scanning-electron microscopy. Olivine-rich inclusions and Ca, Al-rich inclusions (CAIs) are aggregates of various combinations of three fundamental petrographic constituents: rimmed concentric objects, Ca, Si-rich chaotic material, and mafic inclusion matrix. A new classification system for refractory inclusions is developed that is based on the size and abundance of these three fundamental constituents. The new classification system avoids several problems that are inherent in other classification systems, which use the term 'coarse-grained' too restrictively for many simple CAIs and inaccurately for most mililite-rich complex CAIs.

Kornacki, A. S.↗

NASA NTRS1985

Evaluation of space SAR as a land-cover classification

The multidimensional approach to the mapping of land cover, crops, and forests is reported. Dimensionality is achieved by using data from sensors such as LANDSAT to augment Seasat and Shuttle Image Radar (SIR) data, using different image features such as tone and texture, and acquiring multidate data. Seasat, Shuttle Imaging Radar (SIR-A), and LANDSAT data are used both individually and in combination to map land cover in Oklahoma. The results indicates that radar is the best single sensor (72% accuracy) and produces the best sensor combination (97.5% accuracy) for discriminating among five land cover categories. Multidate Seasat data and a single data of LANDSAT coverage are then used in a crop classification study of western Kansas. The highest accuracy for a single channel is achieved using a Seasat scene, which produces a classification accuracy of 67%. Classification accuracy increases to approximately 75% when either a multidate Seasat combination or LANDSAT data in a multisensor combination is used. The tonal and textural elements of SIR-A data are then used both alone and in combination to classify forests into five categories.

Brisco, B.↗

NASA NTRS1985

Continental land cover classification using satellite data

Four different approaches to the classification of land cover for whole continents using multitemporal images of the normalized difference vegetation index derived from the Advanced Very High Resolution Radiometer of the NOAA series of satellites are discussed. The first approach uses only two dates from different seasons and classification dependent upon subdivision of the resultant two-dimensional feature space by an analyst using a track ball. The second approach involves a similar method of partitioning the feature space, but with the two dimensions being the first and second principal components derived from 13 four-week composite images. The third approach uses the maximum likelihood rule to derive the classified map. In the fourth approach, the amount of deviation from characteristic curves is used as a basis for classification.

Townshend, J. R. G.↗

NASA NTRS1984

Using spatial logic in classification of Landsat TM data

A strategy for spatial/spectral classification of Landsat TM data is presented. The strategy is founded upon 'spatial logic', a logic that seeks to emulate important aspects of visual image interpretation. The carefully structured classification process begins with spectral stratification of the data into water, vegetated and non-vegetated pixels. A region growing algorithm is then used to define 'fields' of similar land cover composition. Fields are characterized by cover composition, size and neighborhood characteristics. A supervised iterative contextual classification algorithm is developed to assign final land use/land cover labels. Maps are generalized using a spatial post-processing technique. Positive, though preliminary, results are presented.

Merchant, J. W.↗