A new approach to the analysis of satellite imagery is presented. The central part of this approach is an algorithm which compresses information stored in the ordinary six or eight bits per picture element into only one bit. The quality of this compression is demonstrated by examples of its application to high resolution visual imagery. Both visual inspection and rms difference criterion are used for this evaluation. There are four objectives of this report which are: to review the status of processing techniques which remove redundant information, to show the need for redundance reduction in the processing of satellite images, to present the development of an algorithm for reducing it, and to show results obtained by application of the algorithm to visual imagery. Also, comments are made on needed developments of the technique and its potential application to problems of analysis of satellite imagery data. (Author).

A new approach to the analysis of satellite imagery is presented. The central part of this approach is an algorithm which compresses information stored in the ordinary six or eight bits per picture element into only one bit. The quality of this compression is demonstrated by examples of its application to high resolution visual imagery. Both visual inspection and rms difference criterion are used for this evaluation. There are four objectives of this report which are: to review the status of processing techniques which remove redundant information, to show the need for redundance reduction in the processing of satellite images, to present the development of an algorithm for reducing it, and to show results obtained by application of the algorithm to visual imagery. Also, comments are made on needed developments of the technique and its potential application to problems of analysis of satellite imagery data. (Author)

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).

Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists’ demands for more efficient and higher-quality classification in real time. This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recent developments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remote sensing engineering.

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