Automatic Land Use Classification in Minnesota
Automatic land use classification in Minnesota
Cheung, M.↗
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Terrain Classification Maps of Yellowstone National Park
Terrain classification maps of Yellowstone National Park
Thomson, F. J.↗
Identification of phenological stages and vegetative types for land use classification
The author has identified the following significant results. Multispectral reflectance signatures have been obtained from computer printouts from scene 1049-20505. Those signatures have been grouped for 13 dominant physical features in the Matanuska Susitna Valley area of Alaska. The derived signatures will be validated and tested by requesting another symbol-coded computer printout for the same area. It is expected that ERTS-1 imagery collected during late autumn and early spring will provide separations of signatures that are confounded in the 1049-20505 scene. Separating bogs and forests should be much improved when either snow covered or when grasses are brown. Photographs have been made of projections on the color additive viewer, each scene being projected with six different filter combinations. The products will be analyzed and those best suited used for producing color transparencies at a scale of 1:250,000 for overlaying on standard quadrangle maps. Color prints will be processed for use in the field. From digital MSS signatures derived to date, identification has been made of which bands are the most powerful for automated classification of dominant physical features. It has been found that separations among density ranges increase with longer wavelengths. Deriving MSS digital signatures has proven valuable to both digital and visual processing of ERTS-1 data.
Branton, C. I.↗
Notes for the improvement of a remote sensing multispectral data non-supervised classification and mapping technique
Examined are: (1) the sequential clustering technique for the unsupervised automatic classification and mapping of earth resources satellite data, (2) theoretical analysis of the tests which were used, and (3) derivation of an alternative set of tests and their necessary algorithm.
Dalton, C. C.↗
Change in land use in the Phoenix (1:250,000) Quadrangle, Arizona between 1970 and 1972: Successful use of proposed land use classification system
Changes in land use in the Phoenix (1:250,000 scale) Quadrangle in Arizona have been mapped using only the images from ERTS-1, tending to verify the utility of a land use classification system proposed for use with ERTS images. The period of change investigated was from November 1970 to late summer or early fall, 1972. Seasonal changes also were studied using successive ERTS images. Types of equipment used to aid interpretation included a color additive viewer, a twenty-power magnifier, a density slicer, and a diazo copy machine for making ERTS color composites in hard copy. Types of changes detected have been: (1) cropland or rangeland developed for new residential areas; (2) rangeland converted to new cropland; and (3) possibly new areas of industrial or commercial development. A map of land use previously compiled from air photos was updated in this manner.
Place, J. L.↗
Land use classification and change analysis using ERTS-1 imagery in CARETS
Land use detail in the CARETS area obtainable from ERTS exceeds the expectations of the Interagency Steering Committee and the USGS proposed standardized classification, which presents Level 1 categories for ERTS and Level 2 for high altitude aircraft data. Some Levels 2 and 3, in addition to Level 1, categories were identified on ERTS data. Significant land use changes totaling 39.2 sq km in the Norfolk-Portsmouth SMSA were identified and mapped at Level 2 detail using a combination of procedures employing ERTS and high altitude aircraft data.
Alexander, R. H.↗
Classification of ERTS-1 MSS data by canonical analysis
The objective of canonical analysis is to obtain the maximum separability among a number of catergories. The application of canonical analysis was investigated using the merged MSS ERTS-1 data for one area viewed on two dates. The effect of threshold values on classification regions and confusion regions was investigated.
Lachowski, H. M.↗
Remote sensing in Iowa agriculture: Identification and classification of Iowa crop lands using ERTS-1 and complimentary underflight imagery
The author has identified the following significant results. Results of the continuing analysis of ERTS-1 imagery covering Iowa during 1972 and periods during 1973 are covered. Emphasis is placed on the identification and classification of major crop types at two test sites in Iowa. Standard photointerpretive methods were used in this analysis including the direct enlargement of black and white single-band products and additive color multi-band procedures using a miniadcol system. The use of sequential coverage during the crop growing season is emphasized as a means to improve the effectiveness of ERTS-1 photointerpretations of crop land acreage estimates in Iowa. Illustrative black and white and color prints of both ERTS-1 and underflight imagery are included. In addition, forest land inventories at one test site are reported. A new method for the inventory of forest lands using ERTS-1 imagery is reported and compared with estimates obtained using earlier underflight imagery.
Mahlstede, J. P.↗
Statistical studies of pattern classification and recognition, volume 1
Statistical methods for pattern recognition and classification applications
Source record↗
Image interpretation for a multilevel land use classification system
The potential use is discussed of three remote sensors for developing a four level land use classification system. Three types of imagery for photointerpretation are presented: ERTS-1 satellite imagery, high altitude photography, and medium altitude photography. Suggestions are given as to which remote sensors and imagery scales may be most effectively employed to provide data on specific types of land use.
Source record↗
Notes for the improvement of the spatial and spectral data classification method
This report examines the spatial and spectral clustering technique for the unsupervised automatic classification and mapping of earth resources satellite data, and makes theoretical analysis of the decision rules and tests in order to suggest how the method might best be applied to other flight data such as Skylab and Spacelab.
Dalton, C. C.↗
Identification of phenological stages and vegetative types for land use classification
The author has identified the following significant results. Recent signature identification and refinement techniques indicate that with automated classification of MSS CCT data commercial stands of cottonwood and white spruce can be identified with 80% accuracy in the Bonanza Creek experimental forest. Since that forest is representative of the vast interior Alaska forests, this finding has substantial economic importance to public and private forestry interests in Alaska.
Mckendrick, J. D.↗
Wheat classification exercise, using 11 June 1973, ERTS MSS data for Fayette County, Illinois (for CITARS task)
The prime emphasis was on classification of pixels in field centers, away from boundary effects. Results were encouraging in both training and test field centers for wheat and other major types of vegetation present. However, the location of fields was found to be a serious problem and it was even more difficult to select field-center pixels for fields of sizes less than 20 acres (or even larger, depending upon field shape) for use in the field-center analysis. The majority of fields in the segment are less than 20 acres in size. ERTS-1 data were received on 12 September 1973. Ground truth information and aerial photography were received on 9 and 15 September. The data were analyzed and processed digitally using the ERIM multispectral software system.
Malila, W. A.↗
The utility of ERTS-1 data for applications in land use classification
A comprehensive study has been undertaken to determine the extent to which conventional image interpretation and computer-aided (spectral pattern recognition) analysis techniques using ERTS-1 data could be used to detect, identify (classify), locate, and measure current land use over large geographic areas. It can be concluded that most of the level 1 and 2 categories in the USGS Circular no. 671 can be detected in the Houston-Gulf Coast area using a combination of both techniques for analysis. These capabilities could be exercised over larger geographic areas, however, certain factors such as different vegetative cover, topography, etc. may have to be considered in other geographic regions. The best results in identification (classification), location, and measurement of level 1 and 2 type categories appear to be obtainable through automatic data processing of multispectral scanner computer compatible tapes.
Dornbach, J. E.↗
Classification by means of B-spline potential functions with applications to remote sensing
A method is presented for using B-splines as potential functions in the estimation of likelihood functions (probability density functions conditioned on pattern classes), or the resulting discriminant functions. The consistency of this technique is discussed. Experimental results of using the likelihood functions in the classification of remotely sensed data are given.
Bennett, J. O.↗
Unsupervised classification techniques as components of a data and information system
The phenomenal increase in the amount of data and information being generated by remote sensing systems is stressed. A total system design approach as a solution to this problem is discussed with specific reference to the data and information system needs for Sortie Lab - a multiple use payload for the Shuttle. The development of a multispectral data processing system as a needed component of such a system is reviewed with emphasis on unsupervised multispectral classification techniques developed and presently in use at Marshall Space Flight Center.
Cummings, R. E.↗
Extraction and classification of objects in multispectral images
Presented here is an algorithm that partitions a digitized multispectral image into parts that correspond to objects in the scene being sensed. The algorithm partitions an image into successively smaller rectangles and produces a partition that tends to minimize a criterion function. Supervised and unsupervised classification techniques can be applied to partitioned images. This partition-then-classify approach is used to process images sensed from aircraft and the ERTS-1 satellite, and the method is shown to give relatively accurate results in classifying agricultural areas and extracting urban areas.
Robertson, T. V.↗
Machine boundary finding and sample classification of remotely sensed agricultural data
A method based on the use of spectral variations in combination with spatial variations is developed for automatic boundary finding and sample classification of remotely sensed multispectral data. Preliminary applications of the method to agricultural data show significant improvements in accuracy as compared to the use of spectral data alone.
Gupta, J. N.↗