Abstract
Abstract. Detection and localization problem of extended small-scale objects with different shapes appears in radio observation systems which use SAR, infra-red, lidar and television camera. Intensive non-stationary background is the main difficulty for processing. Other challenge is low quality of images, blobs, blurred boundaries; in addition SAR images suffer from a serious intrinsic speckle noise. Statistics of background is not normal, it has evident skewness and heavy tails in probability density, so it is hard to identify it. The problem of extraction small-scale objects is solved here on the basis of directional filtering, adaptive thresholding and morthological analysis. New kind of masks is used which are open-ended at one side so it is possible to extract ends of line segments with unknown length. An advanced method of dynamical adaptive threshold setting is investigated which is based on isolated fragments extraction after thresholding. Hierarchy of isolated fragments on binary image is proposed for the analysis of segmentation results. It includes small-scale objects with different shape, size and orientation. The method uses extraction of isolated fragments in binary image and counting points in these fragments. Number of points in extracted fragments is normalized to the total number of points for given threshold and is used as effectiveness of extraction for these fragments. New method for adaptive threshold setting and control maximises effectiveness of extraction. It has optimality properties for objects extraction in normal noise field and shows effective results for real SAR images.
Highlights
The task of detection and localization of small extended objects in noisy images occur in the electronic surveillance systems using radars with SAR, infrared and laser systems, as well as television cameras (Gao, 2010; Gonzalez, Woods, Eddins, 2004)
The aim of this paper is to develop method for segmentation and extraction of extensive objects with unknown sizes and orientations
The purpose of this paper is to study adaptive method threshold segmentation for the detection and selection of objects based on structural decomposition of a binary image into elementary, isolated objects, analysis of the impact of the threshold on the results of the decomposition, and algorithm development for installation and changes the threshold in accordance with the results of the decomposition
Summary
The task of detection and localization of small extended objects in noisy images occur in the electronic surveillance systems using radars with SAR, infrared and laser systems, as well as television cameras (Gao, 2010; Gonzalez, Woods, Eddins, 2004) This task is relevant, because these facilities typically have an artificial origin and are of Prime interest. Statistics background is very different from a Gaussian, the distribution is clearly asymmetric, and the tails of the distributions like lognormal density normal or mixed (contaminated-normal), and when small volumes of samples are identified with difficulty Such a character background virtually eliminates the use of the known methods of thresholding, since improper formation thresholds can cause loss of useful objects at a very early stage of processing. The basic principles that allow solving this difficult problem, are the location-based filtering, adaptive thresholding and selection of useful sites on the connectivity of neighboring pixels given the length of the useful structures (Gonzalez, Woods., Eddins, 2004)
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