An Image Fusion Method of SAR and Multispectral Images Based on Non-Subsampled Shearlet Transform and Activity Measure

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 18; p. 7055
• Main Authors: Huang, Dengshan, Tang, Yulin, Wang, Qisheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2022; MDPI
• Subjects: activity measure; Artificial satellites in remote sensing; Comparative analysis; Computer vision; Decomposition; Deep learning; Dictionaries; Evaluation; image fusion; Image processing; Methods; multispectral images; Optical measuring instruments; Remote sensing; Remote sensors; SAR; Satellites; Sensors; shearlet transform; Spatial resolution; Statistical analysis; Synthetic aperture radar; Wavelet transforms; China
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22187055

Synthetic aperture radar (SAR) is an important remote sensing sensor whose application is becoming more and more extensive. Compared with traditional optical sensors, it is not easy to be disturbed by the external environment and has a strong penetration. Limited by its working principles, SAR images are not easily interpreted, and fusing SAR images with optical multispectral images is a good solution to improve the interpretability of SAR images. This paper presents a novel image fusion method based on non-subsampled shearlet transform and activity measure to fuse SAR images with multispectral images, whose aim is to improve the interpretation ability of SAR images easily obtained at any time, rather than producing a fused image containing more information, which is the pursuit of previous fusion methods. Three different sensors, together with different working frequencies, polarization modes and spatial resolution SAR datasets, are used to evaluate the proposed method. Both visual evaluation and statistical analysis are performed, the results show that satisfactory fusion results are achieved through the proposed method and the interpretation ability of SAR images is effectively improved compared with the previous methods.