A Comparative Study on Image Registration Techniques for SAR Images

Sreeja, G. Bharatha; Saraniya, O.

doi:10.1109/icaccs.2019.8728390

Cited by 4 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also in [8], a survey of various methods for detection and description of handcrafted to learning based features is provided. There are some papers which show a comparison of SIFT and SURF and advantages of SURF over SIFT [9,10]. So here, in this paper, SURF is used for the feature detection process.…”

mentioning

confidence: 99%

“…Hence, due to the advantages of SURF over SIFT [9,10] and the availability of different levels of features from CNN [18], the proposed approach in this paper includes SURF as a feature detector and CNN (visual geometry group (VGG16)) for descriptor generation, to address the issue of illumination level change in reference and sensed satellite images. The results of the proposed approach are compared with approach, where SURF is used for feature detection and feature descriptor generation.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Feature based image registration using CNN features for satellite images having varying illumination level

2023

IJATEE

View full text Add to dashboard Cite

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

Feature based image registration using CNN features for satellite images having varying illumination level

2023

IJATEE

View full text Add to dashboard Cite

show abstract

“…Accordingly, intensity-based techniques encounter various challenges [9,10]. One main challenge is that for images with large intensity differences, overlapping regions in the images become limited [11]. Additionally, the run-time of these approaches is high, since they make use of the whole content in the image [12].…”

mentioning

confidence: 99%

Remote Sensing Image Registration based on a Geometrical Model Matching

Hossein-Nejad¹,

Agahi²,

Mahmoodzadeh³

2021

jist

View full text Add to dashboard Cite

Remote sensing image registration is the method of aligning two images from the same scene taken under different imaging circumstances containing different times, angles, or sensors. Scale-invariant feature transform (SIFT) is one of the most common matching methods previously used in the remote sensing image registration. The defects of SIFT are the large number of mismatches and high execution time due to the high dimensions of classical SIFT descriptor. These drawbacks reduce the efficiency of the SIFT algorithm. To enhance the performance of the remote sensing image registration, this paper proposes an approach consisting of three different steps. At first, the keypoints of both reference and second images are extracted using SIFT algorithm. Then, to increase the speed of the algorithm and accuracy of the matching, the SIFT descriptor with the vector length of 64 is used for keypoints description. Finally, a new method has been proposed for the image matching. The proposed matching method is based on calculating the distances of keypoints and their transformed points. Simulation results of applying the proposed method to some standard databases demonstrated the superiority of this approach compared with some other existing methods, according to the root mean square error (RMSE), precision and running time criteria.

show abstract

Cross-View Images Matching and Registration Technology Based on Deep Learning

Zhou

Zhu

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A Comparative Study on Image Registration Techniques for SAR Images

Cited by 4 publications

References 33 publications

Feature based image registration using CNN features for satellite images having varying illumination level

Feature based image registration using CNN features for satellite images having varying illumination level

Remote Sensing Image Registration based on a Geometrical Model Matching

Cross-View Images Matching and Registration Technology Based on Deep Learning

Contact Info

Product

Resources

About