Purpose. Development and testing of an information technology for automated processing of mediumresolution satellite im ages acquired at different times for allweather monitoring of oil and gas production areas. methodology. The information technology implies the use of two algorithms. The algorithm for automated recognition of new oil extraction sites is based on computing the normalized difference index (NDI) of temporal changes for a given pair of satellite images in visible and nearIR bands acquired at different times and selected spectral cannels with subsequent multithreshold binarization. The algorithm for automated recognition of large metal objects uses bipolarized Cband radar images from Sentinel1A/B satellites. findings. The proposed information technology made it possible to automatically detect the new oil extraction sites and rec ognition of large metal objects. The overall recognition accuracy, averaged for 20 test areas, was from 87 to 91 % with the kappa coefficient ranging from 0.82 to 0.85. For cloudcovered areas the big metal objects (an extraction units, automotive equipment for oil transportation, etc.) were recognized using SAR imagery from Sentinel1A/B satellites only. originality. Unlike the existing methods for detection of anthropogenic changes of the Earth's surface by satellite images, the proposed information technology uses immediate calculation of the normalized difference index of temporal changes NDI for a given pair of satellite images acquired at different times, which considerably reduces requirements to the computing power while ensuring higher accuracy of the allocation of the boundaries of new oil production sites. Allweather monitoring is provided using radar data. Practical value. Owing to high degree of automation, the developed technology can be implemented as a geoinformation web service for allweather uptothedate monitoring of oil extraction areas. This web service can be used to determine the area of fields, control production activity and estimate oil production, supervise development and production activities and assess anthro pogenic load in oil production areas.
Purpose. The purpose of this article is to develop a preprocessing method for digital multispectral remote sensing images obtained through optical and infrared means in the electromagnetic spectrum. The method aims to ensure invariance with respect to positional formation conditions that determine spatial and radiometric resolution. By implementing homomorphic filtering in this method, we can significantly increase the informative value of processed imagery. Methodology. The problem solving, including the development of the spatial and radiometric resolution increase ways for multispectral geospatial data are based on the methods of brightness spatial distribution fusion, methods of data dimension reduction, de-correlation techniques and geometric correction of image spatial distributions. Findings. The method of preprocessing digital remote sensing data has been developed, which is a component of the methodology for identifying geometric shapes (GS) of objects in multi-channel aerospace images, allowing for a significant improvement in their recognition efficiency when noise is present. Originality. The method of preprocessing photogrammetric scenes using homomorphic filtering to enhance their informational significance is proposed. The method ensures invariance to positional conditions of fixation, improves the accuracy of further recognition, eliminates the drawbacks of known methods associated with the existence of parametric uncertainty dependence, the features of fixation of species information, low values of information indices of synthesized images, and computational process peculiarities. Practical value. Practical value is consists in improving of identification accuracy of objects GS in digital geospatial data, in significant increasing of raster multispectral images information value and in rising of automated image processing efficiency. The use of the method can greatly enhance the value and usefulness of multispectral photogrammetric images in a wide range of applications, from environmental monitoring to urban planning.
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