Oil exploration oriented multi-sensor image fusion algorithm

• Main Authors: Xiaobing Zhang, Wei Zhou, Mengfei Song
• Format: Article
• Language: English
• Published: De Gruyter 2017-04-01
• Series: Open Physics
• Subjects: oil exploration; multi-sensor image fusion; dual-tree complex wavelet transform; priority map; hilbert transform; 93.85.tf
• Online Access: https://doi.org/10.1515/phys-2017-0020

In order to accurately forecast the fracture and fracture dominance direction in oil exploration, in this paper, we propose a novel multi-sensor image fusion algorithm. The main innovations of this paper lie in that we introduce Dual-tree complex wavelet transform (DTCWT) in data fusion and divide an image to several regions before image fusion. DTCWT refers to a new type of wavelet transform, and it is designed to solve the problem of signal decomposition and reconstruction based on two parallel transforms of real wavelet. We utilize DTCWT to segment the features of the input images and generate a region map, and then exploit normalized Shannon entropy of a region to design the priority function. To test the effectiveness of our proposed multi-sensor image fusion algorithm, four standard pairs of images are used to construct the dataset. Experimental results demonstrate that the proposed algorithm can achieve high accuracy in multi-sensor image fusion, especially for images of oil exploration.