Open AccessStructure tensor‐based SIFT algorithm for SAR image registration
Author(s) -
S Divya,
Paul Sourabh,
Pati Umesh Chandra
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0568
Subject(s) - scale invariant feature transform , artificial intelligence , synthetic aperture radar , computer science , image registration , computer vision , structure tensor , feature (linguistics) , pattern recognition (psychology) , speckle pattern , feature extraction , matching (statistics) , inverse synthetic aperture radar , speckle noise , algorithm , image (mathematics) , radar imaging , mathematics , radar , telecommunications , linguistics , philosophy , statistics
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.