Development of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images

Abstract

The new sea surface wind direction from the X-band marine radar image is proposed in this study using a fast convergent gray-level co-occurrence matrix (FC-GLCM) algorithm. First, the radar image is sampled directly without the need for interpolation due to the algorithm’s application of the GLCM to the polar co-ordinate system, which reduces the inaccuracy caused by image transformation. An additional process is then to merge the fast convergence method with the optimized GLCM so that the circular transition between rough and fine estimates is acquired, resulting in the fast convergence and accuracy improvement of the GLCM. Furthermore, the algorithm will affect the GLCM spatial distribution while calculating it, and it can automatically resolve the 180° ambiguity problem of sea surface wind direction retrieved from radar images. Finally, the proposed method is applied to 1436 X-band marine radar sequences collected from the coast of the East China Sea. Compared with in situ anemometer data, the correlation coefficient is as high as 0.9268, and the RMSE is 4.9867°. The new method was also tested under diverse sea conditions. The FC-GLCM wind direction results against the adaptive reduced method (ARM), energy spectrum method (ESM), and the traditional GLCM (T-GLCM) method produced the best stability and accuracy, in which the RMSE decreased by 91.6%, 67.7%, and 18.1%, respectively.

Enhancing GLCM-Based Feature Extraction for THT Solder Joint Inspection through Log Polar Transformation

“…The research findings supported the effectiveness of the proposed approach in detecting contrast enhancement when forensic fingerprints are tampered with anti-forensic attacks, achieving a true positive rate (TPR) of 92.0%. Another study [34] utilized an approach that combined the rectangularto-polar coordinate transformation, previously introduced by [35], with GLCM. The proposed model in their study demonstrated improved accuracy and computational speed compared to the classical GLCM method.…”

“…Referring to [32] and the successful research [34], the combination of polar transformation and GLCM feature extraction shows promising potential when applied to circular-shaped objects. This is because the integration of polar transformation with GLCM allows for the modeling of texture that not only considers the spatial relationships between neighboring pixels in the image but also considers the radial representation of the texture.…”