Side-Scan Sonar Image Mosaic Using Couple Feature Points with Constraint of Track Line Positions

Zhao, Jianhu; Shang, Xiaodong; Zhang, Hongmei

doi:10.3390/rs10060953

Cited by 11 publications

(5 citation statements)

References 33 publications

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“…In order to further evaluate the performance of the criterion, some objective indexes are analyzed quantitatively, as shown in Tables 3 and 4. 2 WFQI denotes as local regional significance. 3 EFQI is described as edge image similarity.…”

Section: High Frequency Fusionmentioning

confidence: 99%

“…Full sea area survey is general carried out by means of strip measurement, which requires the adjacent strip to have a certain width of overlapping area [1]. However, owing to the effect of imaging time, hull attitude, and ocean reverberation [2], underwater target and terrain texture in the overlapping area have a certain degree of distortion. The overlapping area of a single strip image has incomplete information detection, and the contours of underwater targets do not match.…”

Section: Introductionmentioning

confidence: 99%

“…IFQI denotes as the overall similarity with source image 2. WFQI denotes as local regional significance 3.…”

mentioning

confidence: 99%

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Side-Scan Sonar Image Fusion Based on Sum-Modified Laplacian Energy Filtering and Improved Dual-Channel Impulse Neural Network

et al. 2020

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The operation mode of a single strip provides incomplete side-scan sonar image in a specific environment and range, resulting in the overlapping area between adjacent strips often with imperfect detection information or inaccurate target contour. In this paper, a sum-modified Laplacian energy filtering (SMLF) and improved dual-channel pulse coupled neural network (IDPCNN) are proposed for image fusion of side-scan sonar in the domain of nonsubsampled contourlet transform (NSCT). Among them, SMLF energy is applied to extract the fusion coefficients of the low frequency sub-band, which combines the characteristics of energy information, human visual contrast, and guided filtering to eliminate the pseudo contour effect of block flow. In addition, the IDPCNN model, which utilizes the average gradient, soft limit function, and novel sum-modified Laplacian (NSML) to adaptively represent the corresponding excitation parameters, is applied to improve the depth and activity of pulse ignition, so as to quickly and accurately select the image coefficients of the high frequency sub-band. The experimental results show that the proposed method displays fine geomorphic information and clear target contour in the overlapping area of adjacent strips. The objective index values are generally optimal, which reflect the information of image edge, clarity, and overall similarity.

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Section: High Frequency Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Side-Scan Sonar Image Fusion Based on Sum-Modified Laplacian Energy Filtering and Improved Dual-Channel Impulse Neural Network

et al. 2020

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“…Zhao et al [16] extracted SURF features of the preprocessed strip images and then performed block registration, which achieved good mosaic results. To obtain sonar images of large-area seafloor surface, Zhao et al [17] also proposed a side-scan image mosaicking method based on the coupling feature points of position constraints. In addition, He et al [18] used the unsharp masking (USM) algorithm to enhance the side-scan images and the SURF algorithm for image mosaicking; experiments showed that their method effectively enhanced image features and increased the amount of image information, but the average gray values of the images were affected.…”

Section: Introductionmentioning

confidence: 99%

Underwater Terrain Image Stitching Based on Spatial Gradient Feature Block

Wang¹,

Li²,

Wang³

et al. 2022

Computers, Materials &Amp; Continua

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At present, underwater terrain images are all strip-shaped small fragment images preprocessed by the side-scan sonar imaging system. However, the processed underwater terrain images have inconspicuous and few feature points. In order to better realize the stitching of underwater terrain images and solve the problems of slow traditional image stitching speed, we proposed an improved algorithm for underwater terrain image stitching based on spatial gradient feature block. First, the spatial gradient fuzzy C-Means algorithm is used to divide the underwater terrain image into feature blocks with the fusion of spatial gradient information. The accelerated-KAZE (A-KAZE) algorithm is used to combine the feature block information to match the reference image and the target image. Then, the random sample consensus (RANSAC) is applied to optimize the matching results. Finally, image fusion is performed with the global homography and the optimal seam-line method to improve the accuracy of image overlay fusion. The experimental results show that the proposed method in this paper effectively divides images into feature blocks by combining spatial information and gradient information, which not only solves the problem of stitching failure of underwater terrain images due to unobvious features, and further reduces the sensitivity to noise, but also effectively reduces the iterative calculation in the feature point matching process of the traditional method, and improves the stitching speed. Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image.

show abstract

“…Zhao et al [ 15 ] extracted SURF features of the pre-processed strip images and then performed block registration, which achieved good mosaic results. To obtain sonar images of large-area seafloor surface, Zhao et al [ 16 ] also proposed a side-scan image mosaicking method based on the coupling feature points of position constraints. In addition, He et al [ 17 ] used the unsharp masking (USM) algorithm to enhance the side-scan images and the SURF algorithm for image mosaicking; experiments showed that their method effectively enhanced image features and increased the amount of image information, but the average gray values of the images were affected.…”

Section: Introductionmentioning

confidence: 99%

A Mosaic Method for Side-Scan Sonar Strip Images Based on Curvelet Transform and Resolution Constraints

Zhang

Jin

Bian

et al. 2021

Sensors

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Due to the complex marine environment, side-scan sonar signals are unstable, resulting in random non-rigid distortion in side-scan sonar strip images. To reduce the influence of resolution difference of common areas on strip image mosaicking, we proposed a mosaic method for side-scan sonar strip images based on curvelet transform and resolution constraints. First, image registration was carried out to eliminate dislocation and distortion of the strip images. Then, the resolution vector of the common area in two strip images were calculated, and a resolution model was created. Curvelet transform was then performed for the images, the resolution fusion rules were used for Coarse layer coefficients, and the maximum coefficient integration was applied to the Detail layer and Fine layer to calculate the fusion coefficients. Last, inverse Curvelet transform was carried out on the fusion coefficients to obtain images in the fusion area. The fusion images in multiple areas were then combined in the registered images to obtain the final image. The experiment results showed that the proposed method had better mosaicking performance than some conventional fusion algorithms.

show abstract

Side-Scan Sonar Image Mosaic Using Couple Feature Points with Constraint of Track Line Positions

Cited by 11 publications

References 33 publications

Side-Scan Sonar Image Fusion Based on Sum-Modified Laplacian Energy Filtering and Improved Dual-Channel Impulse Neural Network

Side-Scan Sonar Image Fusion Based on Sum-Modified Laplacian Energy Filtering and Improved Dual-Channel Impulse Neural Network

Underwater Terrain Image Stitching Based on Spatial Gradient Feature Block

A Mosaic Method for Side-Scan Sonar Strip Images Based on Curvelet Transform and Resolution Constraints

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