An Improved Image Matching Method Based on Surf Algorithm

Chen, S. J.; Zheng, Shouzhu; Xu, Zhenhui; Guo, Chengcheng; L., X.

doi:10.5194/isprs-archives-xlii-3-179-2018

Cited by 9 publications

(8 citation statements)

References 10 publications

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“…This database contains five sets of remote sensing images with different texture conditions, such as urban areas and natural landscapes (see Table 1). To perform a fair evaluation, a comparison is made with the classical SIFT-NNDR matching [16], SURF-Delaunay triangulation matching(SURF-DTM) [30] , SURF-NNDR matching [17], SIFT-Sparse Coding(SIFT-SC) [31] and improved SUSAN [32]. The tests were executed on a PC with Intel® Core TM i7 processor and 16 GB RAM memory, implemented in MATLAB®2018A software.…”

Section: -Resultsmentioning

confidence: 99%

Remote Sensing Image Registration based on a Geometrical Model Matching

Hossein-Nejad¹,

Agahi²,

Mahmoodzadeh³

2021

jist

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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.

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Section: -Resultsmentioning

confidence: 99%

Remote Sensing Image Registration based on a Geometrical Model Matching

Hossein-Nejad¹,

Agahi²,

Mahmoodzadeh³

2021

jist

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“…Many researchers have done studies to solve this problem of SIFT. [17][18][19] With the study of Bay and Van in 2006, they published the SURF, a developed version of SIFT. 20 It enables the detection and determination of SURF feature points.…”

Section: Related Workmentioning

confidence: 99%

“…20 However, it has a lower accuracy disadvantage than SIFT. 17 When SIFT and other algorithms that perform similar works are compared in more detail, it is clearly seen that the SIFT algorithm works best in many scenarios. 21 These scenarios are composed of various comparisons with respect to rotary angles and noisy images.…”

Section: Related Workmentioning

confidence: 99%

Image‐based locating and guiding for unmanned aerial vehicles using scale invariant feature transform, speeded‐up robust features, and oriented fast and rotated brief algorithms

Bal

Erdem

Kul

et al. 2021

Concurrency and Computation

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This study provides a method for determining the location information from a photograph taken by an Unmanned Aerial Vehicle (UAV). In today's defense industry, the goal is to design a reliable navigation system that can operate even when the Internet and GPS systems are unavailable. In the proposed system, it is expected that there is an image database in which each image has its own metadata in JavaScript Object Notation format that defines the geolocation information for the corresponding image. In real-time, UAV flies captures an image and uses a matching algorithm to compare it with each and every photo in the database. The image with the highest match rate in the database and its metadata is returned. The UAV extracts its own location information from the metadata. For image matching, Scale Invariant Feature Transform, Speeded-Up Robust Features, and Oriented FAST and Rotated BRIEF algorithms are used. The proposed system is tested and evaluated by using a real-world dataset obtained from Flickr's city images. The proposed approach is proved to work with a success rate of 97.14%.

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“…Hence, the application of special methods for enhancement and detection of edges of these types of images is always required. 29,30 One of the best groups of the methods introduced for image enhancement and edge detection is based on the fuzzy logic theory. 11,28,[31][32][33][34][35] Fuzzy enhancement and edge detection on satellite remote sensing images were first introduced in Refs.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, edge detection in coastal regions is not adequately possible due to some reasons such as sea waves or shallow water in some places, which may confuse edge detection algorithms. Hence, the application of special methods for enhancement and detection of edges of these types of images is always required 29 , 30…”

Section: Introductionmentioning

confidence: 99%

Development of the iterative edge detection method applied on blurred satellite images: state of the art

Rahimzadegan

Sadeghi

2016

J. Appl. Remote Sens

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.This paper aims to implement an iterative fuzzy edge detection (IFED) method on blurred satellite images. Some degradation effects such as atmospheric effects, clouds and their shadows, atmospheric aerosols, and fog remarkably decline the quality satellite images. Hence, some processes such as enhancement and edge detection in satellite images are challenging. One group of methods that can deal with these effects is fuzzy logic methods. Therefore, IFED method was applied in this work on the subimages of the Ikonos, Landsat 7, and SPOT 5 satellite images, contaminated by aforementioned effects. Such as most FED methods, IFED has two components: enhancement and edge detection. In this context, a six-step iterative method, using the if-then-else mechanism, was implemented on the images to perform fuzzy enhancement, and subsequently, edge detection was done. To evaluate the merit of the enhancement and select the best number of iterations, edge gray-value rate criterion was applied. The peak signal-to-noise ratio (PSNR) is applied for the quantitative evaluation of the IFED method. The results of IFED, in comparison with some prior edge detection methods, showed higher PSNR values and a high performance in the edge detection of the earth features in the blurred satellite images.

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An Improved Image Matching Method Based on Surf Algorithm

Cited by 9 publications

References 10 publications

Remote Sensing Image Registration based on a Geometrical Model Matching

Remote Sensing Image Registration based on a Geometrical Model Matching

Image‐based locating and guiding for unmanned aerial vehicles using scale invariant feature transform, speeded‐up robust features, and oriented fast and rotated brief algorithms

Development of the iterative edge detection method applied on blurred satellite images: state of the art

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