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At least 127 records · Page 7

Land use classification using texture information in ERTS-A MSS imagery

The author has identified the following significant results. Preliminary digital analysis of ERTS-1 MSS imagery reveals that the textural features of the imagery are very useful for land use classification. A procedure for extracting the textural features of ERTS-1 imagery is presented and the results of a land use classification scheme based on the textural features are also presented. The land use classification algorithm using textural features was tested on a 5100 square mile area covered by part of an ERTS-1 MSS band 5 image over the California coastline. The image covering this area was blocked into 648 subimages of size 8.9 square miles each. Based on a color composite of the image set, a total of 7 land use categories were identified. These land use categories are: coastal forest, woodlands, annual grasslands, urban areas, large irrigated fields, small irrigated fields, and water. The automatic classifier was trained to identify the land use categories using only the textural characteristics of the subimages; 75 percent of the subimages were assigned correct identifications. Since texture and spectral features provide completely different kinds of information, a significant increase in identification accuracy will take place when both features are used together.

Haralick, R. M.

Operational earth resources data handling system for the 1980's

Results of a recent study of data handling requirements for future operational earth observation systems are reported. Such systems in the 1980's may have 10-20 meter resolution and generate .2 tecabits of data per day, with peak rates of 0.8 gigabits per second based on the dominant requirements of agriculture. System alternatives are considered that will handle such data. Data relayed to the facility are recorded at high rates and then processed at lower speed by computer. Hardwired special digital logic computers may be used with an appropriate classification algorithm for crop recognition. Optical mass memories now in the prototype stage will handle the 10 tecabits and 800 megabit per second read-in rate required.

Van Vleck, E. M.

Classification of physiography from ERTS imagery

The potential application of optical data processing to ERTS imagery as a means for automatic identification of large-scale ground patterns was investigated. Spatial frequency distribution and orientational information were derived from ERTS-1 imagery of Kansas for each of 80 sample areas, each 37 km in diameter. The application of classification algorithms to this data reveals that a high degree of correlation exists between the physiography of a sample area and its frequency information. Specifically, the band of frequencies between 1.1 and 2.8 cycles/km appear to contain most of the information needed in distinguishing different physiographic regions.

Ulaby, F. T.

Comparison of Skylab and LANDSAT images for geologic mapping in Northern Arizona

The author has identified the following significant results. It was found that based on resolution, the Skylab S190A products were superior to LANDSAT images. Based on measurements of shoreline features in Lake Mead S190A images had 1.5 - 3 times greater resolution than LANDSAT. In general, the higher resolution of the Skylab data yielded better discrimination among rock units, but in the case of structural features, lower sun angle LANDSAT images (50 deg) were superior to higher sun angle Skylab images (77 deg). The most valuable advantage of the Skylab over the LANDSAT image products is the capability of producing stereo images. Field spectral reflectance measurements on the Coconino Plateau were made in an effort to determine the best spectral band for discrimination of the six geologic units in question, and these bands were 1.3, 1.2, 1.0, and 0.5 microns. The EREP multispectral scanner yielded data with a low signal to noise ratio which limited its usefulness for image enhancement work. Sites that were studied in Arizona were Shivwits Plateau, Verde Valley, Coconino Plateau, and Red Lake. Thematic maps produced by the three classification algorithms analyzed were not as accurate as the maps produced by photointerpretation of composites of enhanced images.

Goetz, A. F. H.

Regional tectonic evaluation of the Tuscan Apenine, vulcanism, thermal anomalies and the relation to structural units

The author has identified the following significant results. The geological interpretation on data exhibiting the Italian peninsula led to the recognition of tectonic features which are explained by a clockwise rotation of various blocks along left-handed transform faults. These faults can be interpreted as resulting from shear due to main stress directed north-eastwards. A land use map of the mountainous regions of Italy was produced on a scale of 1:250,000. For the digital treatment of MSS-CCTs an image processing software was written in FORTRAN 4. The software package includes descriptive statistics and also classification algorithms.

Bodechtel, J.

LACIE performance predictor FOC users manual

The LACIE Performance Predictor (LPP) is a computer simulation of the LACIE process for predicting worldwide wheat production. The simulation provides for the introduction of various errors into the system and provides estimates based on these errors, thus allowing the user to determine the impact of selected error sources. The FOC LPP simulates the acquisition of the sample segment data by the LANDSAT Satellite (DAPTS), the classification of the agricultural area within the sample segment (CAMS), the estimation of the wheat yield (YES), and the production estimation and aggregation (CAS). These elements include data acquisition characteristics, environmental conditions, classification algorithms, the LACIE aggregation and data adjustment procedures. The operational structure for simulating these elements consists of the following key programs: (1) LACIE Utility Maintenance Process, (2) System Error Executive, (3) Ephemeris Generator, (4) Access Generator, (5) Acquisition Selector, (6) LACIE Error Model (LEM), and (7) Post Processor.

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Natural resources inventory and land evaluation in Switzerland

The author has identified the following significant results. Using MSS channels 5 and 7 and a supervised classification system with a PPD classification algorithm, it was possible to map the exact areal extent of the snow cover and of the transition zone with melting snow patches and snow free parts of various sizes over a large area under different aspects such as relief, exposure, shadows etc. A correlation of the data from ground control, areal underflights and earth resources satellites provided a very accurate interpretation of the melting procedure of snow in high mountains.

Haefner, H.

A procedure used for a ground truth study of a land use map of North Alabama generated from LANDSAT data

A land use map of a five county area in North Alabama was generated from LANDSAT data using a supervised classification algorithm. There was good overall agreement between the land use designated and known conditions, but there were also obvious discrepancies. In ground checking the map, two types of errors were encountered - shift and misclassification - and a method was developed to eliminate or greatly reduce the errors. Randomly selected study areas containing 2,525 pixels were analyzed. Overall, 76.3 percent of the pixels were correctly classified. A contingency coefficient of correlation was calculated to be 0.7 which is significant at the alpha = 0.01 level. The land use maps generated by computers from LANDSAT data are useful for overall land use by regional agencies. However, care must be used when making detailed analysis of small areas. The procedure used for conducting the ground truth study together with data from representative study areas is presented.

Downs, S. W., Jr.

Snow mapping and land use studies in Switzerland

The author has identified the following significant results. A system was developed for operational snow and land use mapping, based on a supervised classification method using various classification algorithms and representation of the results in maplike form on color film with a photomation system. Land use mapping, under European conditions, was achieved with a stepwise linear discriminant analysis by using additional ratio variables. On fall images, signatures of built-up areas were often not separable from wetlands. Two different methods were tested to correlate the size of settlements and the population with an accuracy for the densely populated Swiss Plateau between +2 or -12%.

Haefner, H.

LANDSAT information for state planning

The transfer of remote sensing technology for the digital processing of LANDSAT data to state and local agencies in Georgia and other southeastern states is discussed. The project consists of a series of workshops, seminars, and demonstration efforts, and transfer of NASA-developed hardware concepts and computer software to state agencies. Throughout the multi-year effort, digital processing techniques have been emphasized classification algorithms. Software for LANDSAT data rectification and processing have been developed and/or transferred. A hardware system is available at EES (engineering experiment station) to allow user interactive processing of LANDSAT data. Seminars and workshops emphasize the digital approach to LANDSAT data utilization and the system improvements scheduled for LANDSATs C and D. Results of the project indicate a substantially increased awareness of the utility of digital LANDSAT processing techniques among the agencies contracted throughout the southeast. In Georgia, several agencies have jointly funded a program to map the entire state using digitally processed LANDSAT data.

Faust, N. L.

A first interpretation of East African swiddening via computer-assisted analysis of 3 Landsat tapes

A preliminary application of the machine processing of Landsat data for the identification of swidden farming in East Africa is discussed. Three sets of Landsat data were analyzed: the 1972 mid-dry season, the 1973 late dry season, and the 1975 early wet season. The analysis procedure consisted of: (1) a preprocessing step to de-skew, rotate, and rescale the data, (2) a geometric correction process, (3) photographic enlargement, and (4) a procedure to obtain spectral response values for training the classification algorithm.

Conant, F. P.

A statistical technique for determining rainfall over land employing Nimbus-6 ESMR measurements

Statistical analysis is performed by first sampling three categories of Nimbus 6 ESMR brightness temperatures (representing rain over land, wet land surfaces without rain, and dry land surfaces), then testing these populations for uniqueness. A classification algorithm to delineate rain over land is developed. It is found that synoptic-scale rainfall over land, where surface thermodynamic temperatures are greater than 5 C and the vegetation is bereft of dew, can indeed be delineated despite the large ESMR-6 instantaneous field of view. However, some ambiguity exists in distinguishing between rainfall areas and wet land surfaces.

Rodgers, E.

A statistical technique for determining rainfall over land employing Nimbus 6 ESMR measurements

Statistical analysis of the Nimbus 6 ESMR measurements for remote monitoring of active rainfall data over land is presented. Horizontally and vertically polarized brightness temperature pairs from ESMR 6 were sampled for areas of rainfall over land as determined from the rain recording stations and the WSR 57 radar, and wet and dry ground over the southeastern U.S. These three categories of brightness temperatures were significantly different so that the possibilities of the mean vectors of any two populations coinciding were less than 1 in 100, so that classification algorithms were then developed. The Fisher linear classifier, the Bayesian quadratic classifier, and a non-parametric linear classifier were examined, and the Bayesian algorithm performed best. It was concluded that a rainfall area delineated by the Bayesian classifier coincided well with the synoptic-scale rainfall area mapped by ground recording rain data and radar echoes.

Rodgers, E.

An investigation of vegetation and other Earth resource/feature parameters using LANDSAT and other remote sensing data. 1: LANDSAT. 2: Remote sensing of volcanic emissions

A fanning technique based on a simplistic physical model provided a classification algorithm for mixture landscapes. Results of applications to LANDSAT inventory of 1.5 million acres of forest land in Northern Maine are presented. Signatures for potential deer year habitat in New Hampshire were developed. Volcanic activity was monitored in Nicaragua, El Salvador, and Guatemala along with the Mt. St. Helens eruption. Emphasis in the monitoring was placed on the remote sensing of SO2 concentrations in the plumes of the volcanoes.

Birnie, R. W.

Multispectral data acquisition and classification - Computer modeling for smart sensor design

In this paper a model of the processes involved in multispectral remote sensing and data classification is developed as a tool for designing and evaluating smart sensors. The model has both stochastic and deterministic elements and accounts for solar radiation, atmospheric radiative transfer, surface reflectance, sensor spectral reponses, and classification algorithms. Preliminary results are presented which indicate the validity and usefulness of this approach. Future capabilities of smart sensors will ultimately be limited by the accuracy with which multispectral remote sensing processes and their error sources can be computationally modeled.

Park, S. K.

CCD data processor for maximum likelihood feature classification

The paper describes an advanced technology development which utilizes a high speed analog/binary CCD correlator to perform the matrix multiplications necessary to implement onboard feature classification. The matrix manipulation module uses the maximum likelihood classification algorithm assuming a Gaussian probability density function. The module will process 16 element multispectral vectors at rates in excess of 500 thousand multispectral vector elements per second. System design considerations for the optimum use of this module are discussed, test results from initial device fabrication runs are presented, and the performance in typical processing applications is described

Benz, H. F.

Vegetation survey in Amazonia using LANDSAT data

Automatic Image-100 analysis of LANDSAT data was performed using the MAXVER classification algorithm. In the pilot area, four vegetation units were mapped automatically in addition to the areas occupied for agricultural activities. The Image-100 classified results together with a soil map and information from RADAR images, permitted the establishment of the final legend with six classes: semi-deciduous tropical forest; low land evergreen tropical forest; secondary vegetation; tropical forest of humid areas, predominant pastureland and flood plains. Two water types were identified based on their sediments indicating different geological and geomorphological aspects.

Parada, N. D. J.

Low-cost digital image processing on a university mainframe computer

The advantages and limitations of using university mainframe computers in digital image processing instruction are listed. Aspects to be considered when designing software for this purpose include not only two general audience, but also the capabilities of the system regarding the size of the image/subimage, preprocessing and enhancement functions, geometric correction and registration techniques; classification strategy, classification algorithm, multitemporal analysis, and ancilliary data and geographic information systems. The user/software/hardware interaction as well as acquisition and operating costs must also be considered.

Williams, T. H. L.