Application of the Combined Feature Tracking and Maximum Cross-Correlation Algorithm to the Extraction of Sea Ice Motion Data From GF-3 Imagery

Abstract

In this study, an algorithm combining feature tracking and maximum cross-correlation (FT-MCC) for the extraction of sea ice motion (SIM) vectors were applied to Gaofen-3 (GF-3) imagery, filling the gap of SIM extraction using GF-3 imagery. The locally consistent flow field filtering method is proposed to replace the filtering method based on the correlation coefficient threshold in FT-MCC to improve filtering effectiveness of SIM results extracted by FT-MCC. A comparison of the probability density distributions (PDDs) of the correlation coefficients of SIM vectors extracted by FT-MCC from images with different resolutions revealed high reliability for SIM vectors extracted for images with an 80 m spatial resolution. A comparison of the PDDs of the correlation coefficients of SIM vectors obtained from images with different polarization modes showed more reliable SIM vectors were extracted from vertical transmit horizontal receive (VH) polarization images than from corresponding vertical transmit vertical receive (VV) polarization images. The SIM vectors extracted from GF-3 images by two methods (FT(A-KAZE)-MCC and FT(ORB)-MCC) derived from the FT-MCC algorithm were highly consistent in terms of accuracy and reliability. SIM vectors extracted manually and from Sentinel-1 images were used as reference data to verify the SIM results extracted from GF-3 images, for which the uncertainties in the magnitude and direction of the extracted SIM vectors were found to be 0.119 cm/s–0.287 cm/s (103 m/d–248 m/d) and 4.119°–5.930°, respectively.

Keywords

• Correlation coefficient
• feature tracking (FT)
• Gaofen-3 (GF-3)
• image information entropy
• maximum cross-correlation (MCC)
• probability density distribution (PDD)