Estimation of cloud motion is a challenging task due to the non-linear phenomena of cloud formation and deformation. Satellite images processing is a popular tool used to study the characteristics of clouds which constitute major factors in forecasting the meteorological parameters. Due to the low resolution of satellite images, researchers have turned towards analyzing the high-resolution images captured by ground-based sky cameras. The first objective of this chapter is to demonstrate the different techniques used to estimate clouds motion and to compare them with respect to the accuracy and the computational time. The second aim is to propose a fast and efficient block matching technique based on combining the two types of images. The first idea of our approach is to analyze the low-resolution satellite images to detect the direction of motion. Then, the direction is used to orient the search process to estimate the optimal motion vectors from the high-resolution ground-based sky images. The second idea of our method is to use the entropy technique to find the optimal block sizes. The third idea is to imply an adaptive cost function to perform the matching process. The comparative study demonstrates the high performance of the proposed method with regards to the robustness, the accuracy and the computation time.
Part of the book: Colorimetry and Image Processing