Extraction of Small Objects from Ground-based Multi-static SAR Images using CFAR Algorithm with Generalized Gamma Distribution
| Autor: | Andreas Heinzel, Stephan Dill, Eric Schreiber, Markus Peichl |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Synthetic aperture radar
010504 meteorology & atmospheric sciences Pixel Computer science Generalized gamma distribution ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies 02 engineering and technology CFAR 01 natural sciences Constant false alarm rate law.invention Generalized Gamma Distribution Automatic target recognition law Radar imaging Groundbased SAR Clutter Radar Algorithm 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | 2020 IEEE Radar Conference (RadarConf20). |
| Popis: | Ground-based multi-static synthetic aperture radar is able to produce high-resolution images with sufficient clutter suppression. It is therefore capable of detecting even small and weakly reflecting objects. The automatic extraction of these targets is of great interest for many applications like efficient landmine detection, for instance. Furthermore the automatic extraction of targets is the first useful stage for automatic target recognition. A Constant False Alarm Rate (CFAR) algorithm is a suitable approach for this task by analyzing the distribution of the surrounding clutter and calculating a reasonable threshold for each pixel. The Generalized Gamma distribution forms a large class of distributions and is therefore suitable to model different kind of clutter. In this paper the selected and implemented algorithm is validated using near-range high-resolution data from different kind of objects. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|