Structure tensor‐based SIFT algorithm for SAR image registration. Issue 5 (9th March 2020)

• Record Type: Journal Article
• Authors: S, Divya; Paul, Sourabh; Pati, Umesh Chandra
• Is Part Of: IET image processing. Volume 14:Issue 5(2020)
• Page Start: 929
• Page End: 938
• Publication Date: 2020-03-09
• Subjects: transforms -- image matching -- image registration -- geophysical image processing -- remote sensing -- radar imaging -- feature extraction -- synthetic aperture radar; SAR image registration -- structure tensor‐based SIFT algorithm -- scale layers -- input SAR images -- correct matches -- SAR image pairs -- widely used feature extraction -- feature matching method -- remote sensing image registration -- synthetic aperture radar images -- correct matching rate; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0568
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The scale‐invariant feature transform (SIFT) algorithm is the most widely used feature extraction as well as a feature matching method in remote sensing image registration. However, the performance of this algorithm is affected by the influence of speckle noise in synthetic aperture radar (SAR) images. It reduces the number of correct matches as well as the correct matching rate in SAR image registration. Moreover, SAR image registration is considered to be a challenging task as the images generally have significant geometric as well as intensity variations. To address these problems, a structure tensor‐based SIFT algorithm is proposed to register the SAR images. At first, the tensor diffusion technique is used to construct the scale layers. Then, the features are extracted in the scale layers. Finally, feature matching is performed between the input SAR images and correct matches are identified. The proposed method can increase the number of correct matches as well as position accuracy in registration. Experiments have been conducted on five SAR image pairs to verify the effectiveness of the method.