GF-3 Polarimetric Data Quality Assessment Based on Automatic Extraction of Distributed Targets
Songtao Shangguan, Xiaolan Qiu, Kun Fu, Bin Lei, Wen Hong
Abstract
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.