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The authors propose the method of automatically separation of components of waste objects and decomposition of the object surface to the components by space images. The technique allows constructing a model of surface of structural objects, such as solid waste landfills and municipal landfill sites, made up of several components, given by the different surface textures. The paper used the matrix of information signs, identifying a particular component, their mathematical model and various presentation forms are given. The paper introduces the concepts of surface component images and overall surface component images, which reflects in details the detection areas corresponding to different surface textures. The authors have described the criterion of pixel belonging to one or another component, characterized by a complex of matrices of information signs. There is an opportunity of processing the earth's surface area of ??any size and process automation. The main aim of the study is to develop a method of automatic detection of components of waste objects for space images. The methods used in the study: the regression analysis methods, statistical methods and processing of satellite images. Results. The paper introduces the results of the method on the example of landfill Kuchino (village Saltykov, Balashikha District of Moscow Region) and representation in projection of the information space of standard values ??of the covering area of a given texture. The example of component decomposition of landfill waste section fragment: images of six components and the total component image. The authors constructed the component decomposition of the full waste area: differentiation of the original visible image WorldView2 of a dump cover into small sections, component decomposition of each sites and its integration.

The authors propose the method of automatically separation of components of waste objects and decomposition of the object surface to the components by space images. The technique allows constructing a model of surface of structural objects, such as solid waste landfills and municipal landfill sites, made up of several components, given by the different surface textures. The paper used the matrix of information signs, identifying a particular component, their mathematical model and various presentation forms are given. The paper introduces the concepts of surface component images and overall surface component images, which reflects in details the detection areas corresponding to different surface textures. The authors have described the criterion of pixel belonging to one or another component, characterized by a complex of matrices of information signs. There is an opportunity of processing the earth's surface area of ??any size and process automation. The main aim of the study is to develop a method of automatic detection of components of waste objects for space images. The methods used in the study: the regression analysis methods, statistical methods and processing of satellite images. Results. The paper introduces the results of the method on the example of landfill Kuchino (village Saltykov, Balashikha District of Moscow Region) and representation in projection of the information space of standard values ??of the covering area of a given texture. The example of component decomposition of landfill waste section fragment: images of six components and the total component image. The authors constructed the component decomposition of the full waste area: differentiation of the original visible image WorldView2 of a dump cover into small sections, component decomposition of each sites and its integration.

The relevance of the work due to the simplification of the procedures for receiving and processing satellite images through processing automation and calculation of various indices of the underlying surface, and their temporal changes. The proposed method allows a high speed, and on a large scale to detect signs of soil degradation, in particular, the size of the waste disposal facilities, the multiple spatial resolution images, calculating respective indices underlying surface. The main drawback of the existing detection methods of waste disposal sites is that their search and discovery, environmental assessment carried out by ground, «manual» methods. All this is primarily self identifying objects gives low productivity in landfills monitoring. As a result of the automation of security detection method of waste disposal facilities can be significantly improved protection of the territory from the impact of negative factors. These factors, inter alia, debris environmental factor, in terms of space images may be expressed in a variety of indices of the underlying surface. The main aim of the study is to develop a method for obtaining automatically the underlying surface indices and time series using satellite images linked to UTM geographical projection and their metadata. The methods used in the study: methods of getting cloud masks (threshold filtering method) and regression analysis (method of standard deviation) for temporal processing. The results. The paper introduces the algorithm of automatic data processing and describes the main steps of its operation. The authors define the model of conversion parameters of underlying surface indices change when adding new values ??of the spectral brightness coefficients or deleting the previous ones. The algorithm operation is shown by the example of calculation of NDVI time-series of the given observation area and their application to the analyze the processing data, in particular, the NDVI images are automatically calculated in neighborhood landfill of Torbeevo Lyubertsky district for 2003-2011. The paper illustrates structural changes in soil bio-productivity within four municipal landfills of Zheleznodorozhny urban district in Moscow region: Kuchino, Savvino, Licyi Gory, Nekrasovka.

The relevance of the work due to the simplification of the procedures for receiving and processing satellite images through processing automation and calculation of various indices of the underlying surface, and their temporal changes. The proposed method allows a high speed, and on a large scale to detect signs of soil degradation, in particular, the size of the waste disposal facilities, the multiple spatial resolution images, calculating respective indices underlying surface. The main drawback of the existing detection methods of waste disposal sites is that their search and discovery, environmental assessment carried out by ground, «manual» methods. All this is primarily self identifying objects gives low productivity in landfills monitoring. As a result of the automation of security detection method of waste disposal facilities can be significantly improved protection of the territory from the impact of negative factors. These factors, inter alia, debris environmental factor, in terms of space images may be expressed in a variety of indices of the underlying surface. The main aim of the study is to develop a method for obtaining automatically the underlying surface indices and time series using satellite images linked to UTM geographical projection and their metadata. The methods used in the study: methods of getting cloud masks (threshold filtering method) and regression analysis (method of standard deviation) for temporal processing. The results. The paper introduces the algorithm of automatic data processing and describes the main steps of its operation. The authors define the model of conversion parameters of underlying surface indices change when adding new values ??of the spectral brightness coefficients or deleting the previous ones. The algorithm operation is shown by the example of calculation of NDVI time-series of the given observation area and their application to the analyze the processing data, in particular, the NDVI images are automatically calculated in neighborhood landfill of Torbeevo Lyubertsky district for 2003-2011. The paper illustrates structural changes in soil bio-productivity within four municipal landfills of Zheleznodorozhny urban district in Moscow region: Kuchino, Savvino, Licyi Gory, Nekrasovka.