SAR-SIFT: A SIFT-Like Algorithm for SAR Images

• Published in: IEEE transactions on geoscience and remote sensing Vol. 53; no. 1; pp. 453 - 466
• Main Authors: Dellinger, Flora, Delon, Julie, Gousseau, Yann, Michel, Julien, Tupin, Florence
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Computer Science; Computer vision; Detectors; Feature extraction; Histograms; Image edge detection; Image Processing; Noise; Object recognition; Recognition; Remote sensing; SAR image registration; scale-invariant feature transform (SIFT); Speckle; Synthetic aperture radar; synthetic aperture radar (SAR); scale-invariant feature transform (SIFT); SAR image registration; synthetic aperture radar (SAR); Remote sensing; remote sensing
• ISSN: 0196-2892; 1558-0644; 1558-0644
• DOI: 10.1109/TGRS.2014.2323552

The scale-invariant feature transform (SIFT) algorithm and its many variants are widely used in computer vision and in remote sensing to match features between images or to localize and recognize objects. However, mostly because of speckle noise, it does not perform well on synthetic aperture radar (SAR) images. In this paper, we introduce a SIFT-like algorithm specifically dedicated to SAR imaging, which is named SAR-SIFT. The algorithm includes both the detection of keypoints and the computation of local descriptors. A new gradient definition, yielding an orientation and a magnitude that are robust to speckle noise, is first introduced. It is then used to adapt several steps of the SIFT algorithm to SAR images. We study the improvement brought by this new algorithm, as compared with existing approaches. We present an application of SAR-SIFT to the registration of SAR images in different configurations, particularly with different incidence angles.