| Autor: |
Songtao Shangguan, Xiaolan Qiu, Kun Fu, Bin Lei, Wen Hong |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 4282-4294 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2151-1535 |
| DOI: |
10.1109/JSTARS.2020.3012151 |
| Popis: |
With the needs of continuous data quality assessment for massive Gaofen-3 (GF-3) polarimetric data, an automatic and efficient quality evaluation method is urgently needed. In this article, an automated polarimetric SAR data quality assessment method is conducted using a classic convolution neural network (VGG-16). The method is first pretrained, performance-tested, and robustness-tested on Radarsat-2 fully polarimetric data, then trained by selected SAR scenes of GF-3 for being applied on GF-3 data. The network is supposed to fulfill the work of automatically and accurately selecting those distributed targets satisfying quality evaluation under various scenes. A PolSAR data assessment method based on these distributed targets proposed by the authors in previous work is then applied to give the evaluation results. Experiments on GF-3 data and the comparison to prior works and corner reflectors on polarimetric distortion assessment results verify the effectiveness and advantages of the proposed method. The polarization data quality of GF-3 at different beams is also obtained. The technique and strategy in this article are practical and contributing to the long-term quality assessment of PolSAR data. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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