A New Sample Consensus Based on Sparse Coding for Improved Matching of SIFT Features on Remote Sensing Images

• Published in: IEEE transactions on geoscience and remote sensing Vol. 58; no. 8; pp. 5254 - 5263
• Main Authors: Etezadifar, Pouriya, Farsi, Hassan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Azimuth; Coding; Dictionaries; Encoding; Feature extraction; Image coding; Image matching; Image registration; Matching; Mathematical model; Methods; random sample consensus (RANSAC); Remote sensing; scale-invariant feature transform (SIFT); sparse coding
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2019.2959606

In this article, a new method is proposed for feature matching of remote sensing images using sample consensus based on sparse coding (SCSC) to improve the image registration technique. To this end, scale-invariant feature transform (SIFT) features are used to select interesting points for image matching. The extracted points contain some differences and similarities in two images captured from the same area (but different in sensor resolution, azimuth, elevation, contrast, illumination, etc.); in such a case, similar points should be extracted and other dissimilar should be eliminated. In this article, we greatly improve the matching between two images using the SCSC through checking points altogether. Moreover, the proposed method is shown to have better results than standard alternative methods such as random sample consensus (RANSAC) when the number of feature points is too much or have noise. However, it should be noted that for a low-noise and distortion rate, the proposed method and the RANSAC yield similar results. In general, the proposed method using sparse coding achieves a higher correct match rate than the SIFT algorithm. In order to illustrate this issue, the proposed method is compared to other updated matching and registration methods based on the SIFT algorithm. The obtained results confirm the accuracy of this claim and show that the proposed algorithm is accurate between 0.48% and 7.68% rather than SVD-RANSAC, Hoge, Stone, Foroosh, Leprince, Nagashima, Guizar, Youkyung, Lowe, Preregistration, IS-SIFT, SPSA, Gong, Standard SIFT, IS-SIFT, UR-SIFT, Sourabh, and Han methods.