Extraction of Statistical Features for Improved Automatic Detection of Subglacial Lakes in Radar Sounder Data
| Autor: | Mahdi Khodadadzadeh, Ana-Maria Ilisei, Lorenzo Bruzzone |
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| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
Attenuation Interface (computing) Feature extraction 0211 other engineering and technologies 02 engineering and technology 01 natural sciences Signal law.invention Set (abstract data type) Support vector machine law Extraction (military) Radar Geology 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | IGARSS |
| DOI: | 10.1109/igarss.2018.8518294 |
| Popis: | Approximately 70% of the total number of inventoried subglacial lakes (SLs) in Antarctica have been detected by visual interpretation or semiautomatic techniques applied to data acquired by airborne radar sounder (RS) instruments. Recently, interest has been shown in using automatic classifiers fed with topographic and structural features of the basal interface for the discrimination between lake and non-lake interfaces in RS data. To enhance the performances of the automatic classifiers, in this paper, we propose an additional set of three discriminant features of the basal interface. The features model the statistical properties of the basal reflected radar signal in terms of central moments and are particularly suitable to the accurate description of subglacial lakes since they i) locally characterize the basal interface, ii) do not rely on subsurface attenuation models, and ii) are independent on depth. The effectiveness of the proposed statistical features has been proven experimentally using a large RS dataset acquired in East Antarctica. |
| Databáze: | OpenAIRE |
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