Spatial Feature-Based ISAR Image Registration for Space Targets

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 19; p. 3625
• Main Authors: Zhao, Lizhi, Wang, Junling, Su, Jiaoyang, Luo, Haoyue
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2024
• Subjects: Adaptability; Algorithms; Artificial satellites in remote sensing; Computer vision; Coordinate transformations; Deep learning; Efficiency; Fourier transforms; Glint; Image enhancement; Image registration; Information processing; Inverse synthetic aperture radar; inverse synthetic aperture radar (ISAR); Medical imaging equipment; Methods; Parameter estimation; Radar; Radar imaging; Registration; Signal to noise ratio; Spatial data; spatial feature; Synthetic aperture radar; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16193625

Image registration is essential for applications requiring the joint processing of inverse synthetic aperture radar (ISAR) images, such as interferometric ISAR, image enhancement, and image fusion. Traditional image registration methods, developed for optical images, often perform poorly with ISAR images due to their differing imaging mechanisms. This paper introduces a novel spatial feature-based ISAR image registration method. The method encodes spatial information by utilizing the distances and angles between dominant scatterers to construct translation and rotation-invariant feature descriptors. These feature descriptors are then used for scatterer matching, while the coordinate transformation of matched scatterers is employed to estimate image registration parameters. To mitigate the glint effects of scatterers, the random sample consensus (RANSAC) algorithm is applied for parameter estimation. By extracting global spatial information, the constructed feature curves exhibit greater stability and reliability. Additionally, using multiple dominant scatterers ensures adaptability to low signal-to-noise (SNR) ratio conditions. The effectiveness of the method is validated through both simulated and natural ISAR image sequences. Comparative performance results with traditional image registration methods, such as the SIFT, SURF and SIFT+SURF algorithms, are also included.