Метод оценки эффективности сегментации текстурных областей изображений

Abstract

RU: Дистанционное зондирование земной поверхности обеспечивает уникальные возможности оперативного сбора данных с высоким пространственным, спектральным и временным разрешением. При этом автоматизированная обработка изображений в реальном масштабе времени позволяет эффективно решать научные и прикладные задачи в области картографии, исследования природной среды, океанологии, поиска и освоения полезных ископаемых и во многих других областях. Статья посвящена разработке метода оценки эффективности сегментации текстурных информативных областей изображений, близких по цвету и структуре, полученных в результате аэрофотосъемки. Входными параметрами являются аэрофотоснимок, априорные сведения об областях, которые требуется сегментировать, и метеорологические и навигационнотехнические условия аэрофотосъемки. Выходными параметрами являются изображения с локализованными информативными областями.

EN: The article is devoted to the development of a method for evaluating the efficiency of segmentation of textual informative image areas, similar in color and structure, obtained as a result of aerial photography. Remote sensing of the Earth's surface provides unique opportunities for rapid acquisition of data with high spatial, spectral and temporal resolution. This fact determines the great information potential of such systems. Automated image processing of the Earth's surface allows us to solve scientific and applied problems in the field of cartography, research of the natural environment, oceanology, mineral exploration and development, agriculture and forestry and many other areas effectively. For example, the thematic classification of soil and vegetation cover based on aerospace survey data is a necessary stage of most tasks of landscapeecological mapping. The article deals with models and methods for describing digital textural images. The current state of automated image processing in air monitoring systems is reviewed. The concept of a model of a textural image is defined. For the mathematical description of textures, two main approaches are used: structural and statistical. The first approach describes the basic areas of textures and the spatial relationships between them, which is well suited for describing artificial and regular textures. According to the second approach, the quantitative and statistical distribution of pixel intensity values in the image area is used as the texture description. Three main ways of image segmentation are considered in the article: threshold segmentation, segmentation by scaling up areas and segmentation by the boundaries detachment. Threshold segmentation consists in combining the image regions which are similar by characteristics into a small number of segments. If the brightness exceeds the threshold, then the region is assigned to one segment, and to another one in the opposite case. A review of existing methods for segmentation of textured image areas allowed us to formulate requirements for image processing algorithms for the operative processing of visual data in aerospace monitoring systems. . The input parameters are an aerial photograph, a priori information about the areas to be segmented, and meteorological and navigation-technical conditions for aerial photography. Output parameters are images with localized informative areas.