Download or read book Tests of Spectral Cloud Classification Using DMSP Fine Mode Satellite Data written by James T. Bunting and published by . This book was released on 1980 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: A computer-based processor for satellite imagery was tested on samples of DMSP visible and IR imagery data smoothed to 0.6 n mi resolution. The data were displayed on the AFGL Man-computer Interactive Data Access System so that meteorologists could label small areas (25 x 25 n mi) with one of nine possible cloud categories from the AF 3D Nephanalysis Program (3DNEPH). The computer-based processor labeled the same areas by computing a two-dimensional fast Fourier transform (FFT) and comparing the results to average wavenumber spectra for the cloud categories. Classification accuracies were 65% for visible, 65% for IR and 81% for combined data. The classification accuracies were appreciably better than chance and a simplified processor which used only the averaged values of satellite data over an area. Accuracies improved is some categories were merged. The results were also compared to a cloud typing procedure in the 3DNEPH and to some earlier studies. The results were generally good for categories with small-scale cloud features such as cumulus or cirrus clouds, but the overall accuracy of classification for all cloud categories was not significantly better than verifications cited in earlier studies. Two potential refinements to the spectral processors, namely, removing the effects of backgrounds such as land, water, and snow cover and minimizing sensitivity to varying fractional cloud cover from case to case, are also discussed. (Author).

Download or read book Tests of Spectral Cloud Classification Using DMSP Fine Mode Satellite Data written by James T. Bunting and published by . This book was released on 1980 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A computer-based processor for satellite imagery was tested on samples of DMSP visible and IR imagery data smoothed to 0.6 n mi resolution. The data were displayed on the AFGL Man-computer Interactive Data Access System so that meteorologists could label small areas (25 x 25 n mi) with one of nine possible cloud categories from the AF 3D Nephanalysis Program (3DNEPH). The computer-based processor labeled the same areas by computing a two-dimensional fast Fourier transform (FFT) and comparing the results to average wavenumber spectra for the cloud categories. Classification accuracies were 65% for visible, 65% for IR and 81% for combined data. The classification accuracies were appreciably better than chance and a simplified processor which used only the averaged values of satellite data over an area. Accuracies improved is some categories were merged. The results were also compared to a cloud typing procedure in the 3DNEPH and to some earlier studies. The results were generally good for categories with small-scale cloud features such as cumulus or cirrus clouds, but the overall accuracy of classification for all cloud categories was not significantly better than verifications cited in earlier studies. Two potential refinements to the spectral processors, namely, removing the effects of backgrounds such as land, water, and snow cover and minimizing sensitivity to varying fractional cloud cover from case to case, are also discussed. (Author)

Download or read book Automatic Cloud Classification from Multi Spectral Satellite Data written by Catherine Gautier and published by . This book was released on 1991 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project was aimed at developing an operational 'expert' system to perform the classification of satellite images into cloud types. The approach we have used is based on a number of assumptions. The first one is that such a classification is possible with satellite images of 1 km (or more) resolution. A second assumption, which lays the foundations for all classifications, is that there exists a parameter space wherein some clustering of the data occurs, so the task is to identify this parameter space from the data. An additional assumption necessary to physically interpret the results, but not necessary for the classification itself, is that the clusters found in this parameter space can be related to cloud types or physical features. We chose a Bayesian classifier for our classification. We believe that this type of classifier is best suited for the task because clouds are fuzzy objects which have overlapping characteristics. Also, with a Bayesian classifier, each point in the parameter space has a probability to belong to each class, although this probability may be anywhere between zero and one.

Download or read book Automatic Cloud Classification from Multi Spectral Satellite Data Over Oceanic Regions written by and published by . This book was released on 1992 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project was aimed at developing an operational "expert" system to perform the classification of satellite images into cloud types. The approach we have used is based on a number of number of assumptions. The first one is that such a classification is possible with satellite images of 1 km (or more) resolution. A second assumption, which lays the foundations for all classifications, is that there exists a parameter space wherein some clustering of the data occurs, so the task is to identify this parameter space from the data. An additional assumption necessary to physically interpret the results, but not necessary for the classification itself, is that the clusters found in this parameter space can be related to cloud types or physical features. We chose a Bayesian classifier for our classification. We believe that this type of classifier is best suited for the task because clouds are fuzzy objects which have overlapping characteristics. Also, with a Bayesian classifier, each point in the parameter space has a probability to belong to each class, although this probability may be anywhere between zero and one.

Download or read book Cloud Classification from Satellite Data Using a Fuzzy Sets Algorithm written by National Aeronautics and Space Administration (NASA) and published by Createspace Independent Publishing Platform. This book was released on 2018-06-30 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine likely areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier. Key, J. R. and Maslanik, J. A. and Barry, R. G. Unspecified Center NAG5-898...

Download or read book Cloud Classification Using Multispectral Weather Satellite Imagery written by Boon Han Lim and published by . This book was released on 1996 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Cloud Classification Using Whole sky Imager Data written by and published by . This book was released on 1995 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clouds are one of the most important moderators of the earth radiation budget and one of the least understood. The effect that clouds have on the reflection and absorption of solar and terrestrial radiation is strongly influenced by their shape, size, and composition. Physically accurate parameterization of clouds is necessary for any general circulation model (GCM) to yield meaningful results. The work presented here is part of a larger project that is aimed at producing realistic three-dimensional (3D) volume renderings of cloud scenes, thereby providing the important shape information for parameterizing GCMs. The specific goal of the current study is to develop an algorithm that automatically classifies (by cloud type) the clouds observed in the scene. This information will assist the volume rendering program in determining the shape of the cloud. Much work has been done on cloud classification using multispectral satellite images. Most of these references use some kind of texture measure to distinguish the different cloud types and some also use topological features (such as cloud/sky connectivity or total number of clouds). A wide variety of classification methods has been used, including neural networks, various types of clustering, and thresholding. The work presented here utilizes binary decision trees to distinguish the different cloud types based on cloud feature vectors.

Download or read book Cloud Cover Determination in Polar Regions from Satellite Imagery written by National Aeronautics and Space Administration (NASA) and published by Createspace Independent Publishing Platform. This book was released on 2018-07-23 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objectives are to develop a suitable validation data set for evaluating the effectiveness of the International Satellite Cloud Climatology Project (ISCCP) algorithm for cloud retrieval in polar regions, to identify limitations of current procedures and to explore potential means to remedy them using textural classifiers, and to compare synoptic cloud data from model runs with observations. Toward the first goal, a polar data set consisting of visible, thermal, and passive microwave data was developed. The AVHRR and SMMR data were digitally merged to a polar stereographic projection with an effective pixel size of 5 sq km. With this data set, two unconventional methods of classifying the imagery for the analysis of polar clouds and surfaces were examined: one based on fuzzy sets theory and another based on a trained neural network. An algorithm for cloud detection was developed from an early test version of the ISCCP algorithm. This algorithm includes the identification of surface types with passive microwave, then temporal tests at each pixel location in the cloud detection phase. Cloud maps and clear sky radiance composites for 5 day periods are produced. Algorithm testing and validation was done with both actural AVHRR/SMMR data, and simulated imagery. From this point in the algorithm, groups of cloud pixels are examined for their spectral and textural characteristics, and a procedure is developed for the analysis of cloud patterns utilizing albedo, IR temperature, and texture. In a completion of earlier work, empirical analyses of arctic cloud cover were explored through manual interpretations of DMSP imagery and compared to U.S. Air Force 3D-nephanalysis. Comparisons of observed cloudiness from existing climatologies to patterns computed by the GISS climate model were also made. Barry, R. G. and Key, J. Unspecified Center NASA-CR-186096, NAS 1.26:186096 NAG5-898...

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1991 with total page 1460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.

Download or read book Report on Research at AFCRL written by Air Force Cambridge Research Laboratories (U.S.) and published by . This book was released on 1976 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Cloud Classification and Its Application to Statistically Derived Radiative Energy Budgets written by Michael Adam Friedman and published by . This book was released on 1998 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Classification and Analysis of Surface and Clouds at High Latitudes from AVHRR Multispectral Satellite Data written by Elizabeth Eby Ebert and published by . This book was released on 1987 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Digest written by and published by . This book was released on 1987 with total page 898 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Digital Image Processing and Visual Communications Technologies in Meteorology written by Paul Janota and published by . This book was released on 1987 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Automated Recognition of Oceanic Cloud Patterns and Its Application to Remote Sensing of Meteorological Parameters written by Louis Garand and published by Ann Arbor, Mich. : University Microfilms International. This book was released on 1986 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Proceedings written by and published by . This book was released on 1988 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Government reports annual index written by and published by . This book was released on 199? with total page 1192 pages. Available in PDF, EPUB and Kindle. Book excerpt: