An NSST-Based Fusion Method for Airborne Dual-Frequency, High-Spatial-Resolution SAR Images

Abstract

With the continuous development of synthetic aperture radar (SAR) technology, SAR image data are becoming increasingly abundant. For the same scene, dual-frequency (high-frequency and low-frequency) SAR images can present different details and feature information. SAR image fusion of the two frequencies can combine the advantages of both, thus describing targets more comprehensively. Because high-resolution SAR images contain a large amount of detailed information, such as edges and textures, the traditional fusion methods cannot fuse this information better, resulting in a loss of information. To solve the problem, this article proposes a fusion method suitable for airborne dual-frequency, high-resolution SAR images. First, the source SAR images are decomposed to obtain their low-pass bands and high-pass bands by using the nonsubsampled Shearlet transform (NSST). Then, we apply the improved nonnegative matrix factorization to merge the low-pass bands and use the new sum of modified Laplacian to merge the high-pass bands. After that, the fused low-pass bands and high-pass bands are reconstructed by the inverse NSST, to obtain the final fused image. Finally, by processing the airborne SAR data, the effectiveness of the proposed method is verified.

Keywords

• Dual-frequency SAR images
• image fusion
• new sum of modified Laplacian (NSML)
• nonnegative matrix factorization (NMF)
• nonsubsampled Shearlet transform (NSST)