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 was applied to Gaofen-3 (GF-3) imagery, filling the gap of SIM extraction using GF-3 imagery. The locally consistent (LC) 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.

Object Tracking Based on Satellite Videos: A Literature Review

“…Similarly, Ref. [133] designed a locally consistent flow field filtering algorithm with a correlation coefficient threshold and achieved better performance in sea ice motion estimation using GF-3 imagery.…”

“…Traditional [4] 2017 MCC tracker with hybrid example-based super-resolution model [128] 2017 A faster cross-correlation based tracking with several updates [129] 2018 A optical-flow based tracking with super-resolution enhancement [130] 2019 A multi-step tracker for ice motion tracking [131] 2020 Rotation-invariant ice floe tracking [132] 2021 Integrating the cross-correlation with feature tracking [133] 2022 Integrating locally consistent flow field filtering with cross-correlation DL-based [134] 2019 An encoder-decoder network with LSTM to predict ice motion trajectory [135] 2021 A CNN model to predict the arctic sea ice motions [136] 2021 A multi-step machine learning approach to track icebergs…”

Sea ice drift vector extraction based on feature matching using CS-1 Images

“…Additionally, the constellation will enable the utilization of interferometry techniques in different orbits and provide crucial support for the operational use of polarimetric SAR satellites in China. Although Gaofen-3 constellation SAR imagery has high resolution, only a few research works have applied its data [17][18] .…”

Improvement of Sea Ice Drift Extraction Based on Feature Tracking from C-SAR/01 Imagery

“…China's first civilian C-band highresolution SAR satellite, GF-3 (Gaofen-3), is equipped with 12 imaging modes [30], [31]. The sea ice drift vectors derived from GF-3 imagery demonstrated a high level of accuracy, with uncertainties in speed ranging from 0.119 cm/s to 0.287 cm/s, and in direction ranging from 4.119° to 5.930° [32]. The launch of C-SAR/01, a pivotal component of China's SAR satellite network, Launched on 23rd November 2021.…”

“…The author of [27] applied the ORB operator to extract sea ice drift vectors from Sentinel-1 SAR imagery and improved computational efficiency. The author of [32] improved the uniformity of feature points extracted by ORB compared to ORB by adding a Quadtree retrieval method; They eliminated the concentration of feature points extracted by ORB on ice ridges, leads, and coastlines, providing favorable conditions for the subsequent feature point matching calculation. Furthermore, some studies have focused on improving error filtering methods after calculating sea ice drift vectors using the ORB operator [40], [41].…”