Mathematical Modelling of Segmentation Synthetic Aperture Radar Data for Military Purposes

Abstract

Digital image processing is the use of algorithms and mathematical models to process and analyze digital images. The goal of digital image processing is to enhance the quality of images, extract meaningful information from images, and automate image-based tasks. In this contribution we discuss and use mathematical modelling for segmentation of image data. We focus on Synthetic Aperture Radar (SAR) data, which plays an important role in military area. Our own approach brings our own software for segmentation of SAR images. We use discrete mathematical models, graph cut, grab cut and random walker. Our own approach is in implementation of these algorithms and their implementation in programming languages as C, C++, and Python. We provide segmentation of noise images, and we focus on segmentation of paths, roads, objects, rivers etc. We provide segmentation of SAR images. We do preprocess and post processing of data based on requirements of authors. The advantage of our methods is in the better and clearer segmentations with better boundaries. Our solution can also proceed noise data, what is the big problem by SAR data analyses. We deal with SAR data, and we try to segment objects. There are some limitations for processing real image data. We cannot deal with data which has too many lines, or too big distances in shadow colors. Another limitation brings scaling of images and too big and too noisy data.