A Space-borne GNSS-R DDM waveform classification method for land surface

doi:10.13474/j.cnki.11-2246.2019.0249

Abstract

Abstract: In the GNSS-R based land surface remote sensing application for the complex scenarios, there are limitations, such as the low signal-to-noise ratio (SNR) and the difficulty for effective information identification. This problem seriously restricts the GNSS-R application in land surface remote sensing. In order to quickly distinguish clutter signals and effective information from massive space-borne GNSS-R land surface data of low SNR, a new method is proposed for the DDM waveform classification based on statistical inductive analysis and the significant level of the space-borne Delay Doppler Map (DDM) correlation peak. Subsequently, this method is utilized to waveform classification the land surface observation data of UK TechDemoSat-1 (TDS-1) satellite. Finally, related comparative analysis for the SNR of the waveform after classification, and the correlation analysis between the classification results and various typical land surface types was accomplished, which demonstrated the feasibility and effectiveness of the proposed waveform classification method.

Key words: space-borne GNSS-R, delay Doppler map, DDM waveform classification, UK TechDemoSat-1 (TDS-1), typical land surface types

CLC Number:

P237

TU Jinsheng, ZHANG Rui, HONG Xuebao, HAN Mutian. A Space-borne GNSS-R DDM waveform classification method for land surface[J]. 测绘通报, 2019, 0(8): 44-47.

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