Sonar image mosaic based on a new feature matching method

IET Image ProcessingVolume 14, Issue 10 p. 2149-2155 Research ArticleFree Access Sonar image mosaic based on a new feature matching method Zhijie Tang, Zhijie Tang School of Mechatronic Engineering and Automation, No. 99 Shangda Road, Shanghai University, Shanghai, People's Republic of ChinaSearch for more papers by this authorGaoqian Ma, Corresponding Author Gaoqian Ma gq_ma@shu.edu.cn orcid.org/0000-0003-2779-6099 School of Mechatronic Engineering and Automation, No. 99 Shangda Road, Shanghai University, Shanghai, People's Republic of ChinaSearch for more papers by this authorJiaqi Lu, Jiaqi Lu orcid.org/0000-0002-0228-3281 School of Mechatronic Engineering and Automation, No. 99 Shangda Road, Shanghai University, Shanghai, People's Republic of ChinaSearch for more papers by this authorZhen Wang, Zhen Wang School of Mechatronic Engineering and Automation, No. 99 Shangda Road, Shanghai University, Shanghai, People's Republic of ChinaSearch for more papers by this authorBin Fu, Bin Fu Shanghai Charpie Intelligent Technology Co., Ltd, Shanghai, People's Republic of ChinaSearch for more papers by this authorYijie Wang, Yijie Wang The Pearl River Hydraulic Research Institute, Guangzhou, People's Republic of ChinaSearch for more papers by this author Zhijie Tang, Zhijie Tang School of Mechatronic Engineering and Automation, No. 99 Shangda Road, Shanghai University, Shanghai, People's Republic of ChinaSearch for more papers by this authorGaoqian Ma, Corresponding Author Gaoqian Ma gq_ma@shu.edu.cn orcid.org/0000-0003-2779-6099 School of Mechatronic Engineering and Automation, No. 99 Shangda Road, Shanghai University, Shanghai, People's Republic of ChinaSearch for more papers by this authorJiaqi Lu, Jiaqi Lu orcid.org/0000-0002-0228-3281 School of Mechatronic Engineering and Automation, No. 99 Shangda Road, Shanghai University, Shanghai, People's Republic of ChinaSearch for more papers by this authorZhen Wang, Zhen Wang School of Mechatronic Engineering and Automation, No. 99 Shangda Road, Shanghai University, Shanghai, People's Republic of ChinaSearch for more papers by this authorBin Fu, Bin Fu Shanghai Charpie Intelligent Technology Co., Ltd, Shanghai, People's Republic of ChinaSearch for more papers by this authorYijie Wang, Yijie Wang The Pearl River Hydraulic Research Institute, Guangzhou, People's Republic of ChinaSearch for more papers by this author First published: 18 June 2020 https://doi.org/10.1049/iet-ipr.2019.0695Citations: 4AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Sonar images are valuable in exploring underwater environmental information. As these images are generally limited by the viewing angle, sonar image mosaicking becomes an important research topic. By combining several frames consecutively acquired while the underwater vehicle is manoeuvring, an image with a wider view can be obtained. This study presents a fast sonar image mosaicking approach consisting of denoising, feature extraction, initial matching, splicing, and optimisation. Based on the Euclidean distance between initially matched points and dip angle of connection line, poorly matched feature point pairs are removed to avoid false matching. This way, the success rate of image mosaicking and quality of the resulting mosaicking is effectively improved. 1 Introduction In recent years, with the gradual deepening of seabed exploration, sonar has been applied in more scenarios, such as submarine target detection [1], underwater environment mapping [2], hull inspection [3], fish species detection [4], and so on. However, the visual range of traditional sonar equipment is small, the amount of single sonar image's information is limited, and limited or incomplete information limits the application of sonar. To obtain a larger range of underwater environment information, it is necessary to splice sonar images. Underwater robot or unmanned boat is needed to collect sonar data, but it is not stable in the process of movement. The flow of water will drive it to move sideways, and the water wave will shake it up and down [5]. The spherical spreading and absorption by water molecules will both cause the attenuation of sound signal [6]. All these factors affect the quality of the sonar image. In addition, the sonar image is different from the traditional optical image. Firstly, the sonar image is low frequency [7]. The main part of the sonar image is a flat area [8]. Secondly, there is a large amount of random noise in the sonar image [9], which reduces the image quality and increases the difficulty of the sonar image mosaic. Finally, the sonar image has a relatively narrow greyscale, and the features corresponding to different sonar perspectives are different [10]. The image levels are not clear enough and the details are fuzzy. At present, there is not a complete system for sonar image splicing. Existing research studies mainly focus on the character of a sonar image or a specific step of sonar image splicing separately. Reed et al. [11] proposed a framework for large-scale side-scan sonar image mosaic. Image fusion was achieved by machine learning, but the training set was very limited and could not produce an ideal mosaics effect. Zhang et al. [12] proposed a sonar image mosaic method based on feature matching and wavelet transform. Small univalue segment assimilating nucleus (SUSAN) algorithm was used in the process of feature recognition. The detection accuracy of feature points is in general, and the error rate is relatively high. It directly affects the quality of the mosaic image, and there is an obvious crack in the transition region of the mosaic image. Zhang et al. [13] proposed a region-based registration method for the step of image registration in sonar image mosaic, but the application conditions are rather harsh, such as the need to acquire the speed of sonar acquisition equipment or calibrate the distance of specific objects etc., so the application scope is narrow. Tao et al. [14] proposed a sonar image matching method based on prior position knowledge, which used the feature-based matching method. However, in the process of feature matching, the matching information is too dependent on a single parameter, which is prone to bias. Henson et al. [15] realised sonar image matching by calculating the pixel displacement between continuous sonar images. This method has high requirements for the motion control and quality sonar data; otherwise, it is difficult to obtain accurate results. Wang et al. [16] proposed a sonar image registration method based on scale-invariant feature transform (SIFT) algorithm, which has good accuracy but low efficiency. Guo et al. [17] studied the theory of SIFT matching, use Euclid distance as a similarity measurement of key points and use random sample consensus (RANSAC) to eliminate mismatches. Compared with SIFT, the speeded-up robust features (SURF) algorithm in this study inherits useful characteristics of SIFT, such as rotation invariance and scale invariance, and has higher time efficiency. Li et al. [18] proposed a self-selected regularised Gaussian field criterion for non-rigid point matching. In the process of selecting the best sample points by using a self-selected mechanism, there is good registration accuracy in regular images. However, this method is not suitable for sonar image processing, because there are many noise points and the feature points are not obvious, and the self-selection mechanism cannot distinguish them. Feature-based sonar image mosaic is studied in this paper. SURF algorithm is used in the process of feature recognition, the detection accuracy is much better than the SUSAN algorithm, the detection efficiency is higher than SIFT, and the comprehensive performance of SURF is better. In the process of feature matching, a method of screening matching information is proposed. Compared with the method of screening matching information relying on a single parameter, the deviation of this algorithm is smaller and more accurate, and no prior knowledge is needed. Then homography matrix is obtained to acquire the mapping relationship between two images and finish the sonar image splicing process. Finally, a weighted fusion algorithm is proposed to optimise the image and remove the crack in the mosaic image. 2 Principle In this study, sonar image splicing is mainly divided into three steps. Firstly, the sonar images are preprocessed to improve the images' signal-to-noise ratio. Secondly, the process of sonar image splicing. Also, a new algorithm was proposed, which can effectively improve the quality of sonar image splicing. Finally, optimising the mosaic image to improve the quality of the mosaic image. The whole process is shown in Fig. 1. Fig. 1Open in figure viewerPowerPoint Flow chart of sonar image mosaic 2.1 Sonar image preprocessing It is necessary to pre-process the sonar images to improve the quality before splicing. Since the low frequency and narrow greyscale are the inherent characteristics of sonar images, which cannot be effectively optimised, the preprocessing mainly deals with the noise in sonar images. The main purpose of sonar image preprocessing is to remove as much noise as possible. The most common noise in sonar images is speckle noise [19]. Presently, median filtering is the most commonly used to pre-process sonar images, which is a relatively effective method. Therefore, the median filtering was adopted to pre-process sonar images. 2.2 Sonar image mosaic The feature-based method is adopted for sonar image splicing. The process of image splicing is the same as that of common image splicing which consisted of feature recognition, feature matching, and image splicing. Feature recognition process extracts and describes feature points in sonar images. The feature matching process matches and filters the feature points of the sonar images. Image splicing calculates the mapping relationship between sonar images to be spliced and then splices the two images into one image. 2.2.1 Feature recognition Commonly used feature recognition algorithms include SURF, SIFT, Oriented FAST and Rotated BRIEF (ORB), Brief etc. [20]. Brief and ORB run faster than SURF and SIFT, but Brief cannot recognise the scale and direction of features in the graph [21]. ORB has the feature of rotation invariance, but the scale invariance is very poor, while SURF and SIFT have the good property of scale invariance and rotation invariant. In this study, sonar images are acquired in the underwater vehicle's moving process, so the scale and direction of the same object in different sonar images will inevitably change. So SURF and SIFT can meet the requirements. The main differences between SURF and SIFT are the aspects of scale-space extremum detection, feature point orientation, and feature descriptor. In the aspect of scale-space extremum detection, SURF uses a square filter and Hessian matrix determinant to detect extremum, and integral graphs to accelerate operation, while SIFT uses a Gauss filter to find extremum. In the aspect of feature point orientation, SURF calculates the Haar wavelet transform in the X and Y directions of the pixels around the feature points, takes the maximum value of the sum of the wavelet transform in the X and Y directions as the direction of the feature points, and uses the Haar wavelet to increase the speed, while SIFT calculates the direction histogram of the local neighbourhood of the key points to find the biggest value in the histogram and the direction of the large value is the main direction of the key points. In the aspects of feature descriptor, SURF uses 64-dimensional vectors to describe features, while SIFT uses 128-dimensional vectors to describe features. These differences make SURF far more efficient than SIFT. To reduce the time-consuming sonar image mosaic and improve the efficiency of the mosaic, the SURF algorithm is used for sonar images' feature recognition in this study. Hessian matrix, which is the core of the SURF algorithm, is a matrix formed by the second partial derivatives of a multivariate function. It describes the local curvature of the function. In image processing, the Hessian matrix can be used to find the extremum points in the image. Suppose the pixel coordinates of a point in the image are (x, y), its pixel value is f(x, y), and the Hessian matrix H is shown in formula (1) (1)Setting the appropriate threshold of the Hessian matrix (denoted as th) is an important step in the process of feature point detection. In the SURF algorithm, the extremum points in the image are determined by comparing the threshold value and the discriminant of the Hessian matrix [22]. Among them, the threshold value of the Hessian matrix has a default value of 200 [23], while the discriminant is the eigenvalue of the Hessian matrix, denoted as det(H), which is calculated in formula (2) (2)After feature detection is completed, feature points are numbered, and their information such as pixel coordinates and directions of features are recorded. 2.2.2 Feature matching Image registration is the process of finding the transformation that aligns two or more images of the same scene taken at different times from different poses (viewpoints and (or) orientations) and/or by different sensors [24, 25]. The methods of image feature matching mainly include Brute-force matching and fast library for approximate nearest neighbours (FLANN)-based matching. The difference between them is that Brute-force matching always trying all possible matching to find the best matching while FLANN-based matching only finds an approximate nearest neighbour, which is a good matching but not necessarily the best. Since the features of the sonar images are different in scale and direction, to get the best matching, Brute-force matching is chosen in this study. After Brute-force matching, feature matching data, including feature points index information and distance information, is obtained, which is collectively referred to as feature descriptors. The feature point's index information indicates the one-to-one correspondence between the feature points of two images. Distance information is the Euclidean distance, which is used to calculate the actual distance between two points in the n-dimensional space (as shown in Fig. 2), belonging to the L2 norm. Fig. 2Open in figure viewerPowerPoint Euclidean distance between two points in an n-dimensional space For two points and in the n-dimensional space, the Euclidean distance is defined as (3)In the feature matching process, Euclidean distance can be used to characterise a similar degree of feature points. The smaller the Euclidean distance, the degree of similarity is higher. To obtain better-matched feature point pairs and remove the unqualified pairs, the distance information is used for preliminary screening of matched feature point pairs. A matched feature pair with the distance greater than a certain multiple (denoted as n) of the minimum distance of all pairs will be discarded. After filtering with distance information, most of the wrong matching information will be filtered out, but a small part will be retained. This is because difference between matched feature point pairs described by Euclidean distance is not the difference between real objects, but rather the difference of matched feature point pairs described by feature descriptors. In other words, Euclidean distance indicates the difference of feature descriptors. Therefore, when the feature descriptors of two different objects in the two sonar images are very similar, it is possible to be retained in the step of preliminary screening. Although the number of such wrong matches is far less than the number of correct matches, it will still have an impact on subsequent image splicing and even lead to unsuccessful mosaic. To get rid of these matching information, this study uses the dip angle of matched feature point pairs as a constraint to further screening matching information, which is because sonar images to be spliced are continuous and the dip angle of same object's locations in two images, which is determined by the motion state of sonar equipment and image acquisition interval, cannot be more than a threshold (denoted as θ). For the two sonar images, suppose the resolution of the left image is , and the right is . The pixel coordinates of the two feature points to be matched in the images are (in the left image) and (in the right image), so the dip angle between the two points is calculated in formula (4) (4)Constructing constraint by setting threshold θ as follows: (5)When screening matched feature point pairs through the above two methods, the selection of multiple n and angle threshold θ is crucial. The value of n determines how similar point pairs are, and the value of θ determines the angle range of point's pairs. This study filters matched feature point pairs by constraining both n and θ at the same time. 2.2.3 Sonar image mosaic In the process of sonar image splicing, image registration should be carried out first to obtain the mapping relationship between the images to be spliced. Image registration is to transform different independent images into the same coordinate system [26]. In Fig. 3, a point X is projected onto the imaging plane corresponding for , respectively, at the corresponding coordinates and . Fig. 3Open in figure viewerPowerPoint Projection of a point on to two planes The mapping of points on plane to plane can be realised by the homography matrix. In this study, the homography matrix is obtained by using the RANSAC method, which can optimise the results in the iteration. The obtained matrix is a 3 × 3 non-singular matrix, denoted as H and can be expressed as formula (6) (6) are eight unknown elements in the homography matrix. The mapping process from point m on to point on can be expressed in formula (7) (7)When there are at least four matched feature point pairs that are not collinear, the homography matrix H can be solved. According to the homography matrix, the right image can be registered to the left image, and the pixel coordinates of the four vertices of the right image in the left image can be obtained. Then create a new image to output the result of image mosaic, and copy the left image and the registered right image to the new image to complete the splicing. 2.3 Mosaic image optimisation The main problem of the preliminary splicing sonar image is that there is obvious crack in the optimise region between two images. This is because the direct copy method is adopted in the final image mosaic process, and crack will inevitably appear at the intersection of the two images. To optimise the splicing image, the weighted fusion method is adopted in this study. In the transition region (the default is a rectangular region, if the overlapping part is irregular region, the minimum rectangular region covering the entire overlapping region will be taken), the left image will be gradually transferred to the right image, i.e. the pixel values in the transition region of the image to be spliced will be added with certain weights and assigned to the final splicing image. In the final splicing image, , the starting line of the transition region, is obtained from the registered right image, and , the ending line of the transition region, is the right line of the left image ( is equals to number of left image's columns). The value of the weight coefficient is defined in formula (8) (8)It can be known from formula (8) that on the left side of the transition region, the weight of the left image is larger, while on the right side of the transition region, the weight of the right image is larger. The pixel value of the point's v in the transition zone is calculated in formula (9) (9)where is the pixel value of the corresponding point in the left image and is the pixel value of the corresponding point in the registered right image. A combination of subjective evaluation and objective evaluation is used to evaluate the quality of the image. The result proves that image optimisation improves the image quality and remove the crack (analysis process and experimental data are shown at the end of Section 3). The subjective evaluation mainly evaluates images' noise, smoothness, edge sharpness etc., while objective evaluation evaluates the spatial distribution and gradient distribution of the image. Three indexes are used in objective evaluation, which is variance, entropy, and Tenengrad gradient. Variance indicates the deviation of pixel values from its mean. Since the sonar image has a relatively narrow greyscale, the variance of a sonar image is smaller than that of a normal image. For a picture of size M × N, the variance is calculated by formula (10) (10)where is the pixel value at pixel coordinates , and is the average value of all pixels, which is obtained by formula (11) (11)Entropy is an indicator of the amount of information in an image. The larger the entropy value, the richer the amount of information [27]. The value of entropy is obtained from formula (12) (12)MAX is the maximum pixel value, which is 255 in this study. is the probability of the pixels whose grey value is i, i.e. the ratio of the number of pixels with value i to the total number of pixels of image. Tenengrad gradient is an index of the clarity of the image [28]. The cleaner the image, the larger the value of the Tenengrad gradient. It is defined as formula (13) (13) and are the convolutions of the horizontal and vertical edge detection operators and at pixel , respectively. The edge detection operators are as followed: (14) 3 Experiments and results The sonar acquisition equipment used in experiments is blueprint subsea M750d, mounted on an underwater robot, which can provide two working frequencies, namely 750 kHz and 1.2 MHz. The sonar image used in this study is acquired at the frequency of 1.2 MHz by M750. Compared with the other frequency, the object contour detected by the sonar at 1.2 MHz is clearer, which is suitable for the subsequent processing of sonar images. For the collected sonar files, the sonar images are obtained by intercepting frames and the interception time interval is 1 s. The sonar image used in this study is shown in Fig. 4. Fig. 4Open in figure viewerPowerPoint Sonar image to be spliced (a) Left sonar image, (b) Right sonar image When detecting image features with SURF, the lower the threshold value of the Hessian matrix is, the lower the accuracy of feature detection is, and the more feature points are detected. Conversely, the higher the threshold value of the Hessian matrix is, the higher the accuracy of feature detection is, and the fewer feature points are detected, as shown in Fig. 5. The higher the detection accuracy is, the more feature points there are, the better the final sonar splicing quality will be. Therefore, both too high and too low thresholds are not conducive to feature matching and image splicing. The threshold value of the Hessian matrix is set to 800, which can filter out the feature points that are not good enough and detect a sufficient number of feature points. In the process of feature matching, matched feature point pairs are filtered by two constraints. Most unqualified matching information can be eliminated by discarding point pairs whose distance is greater than a certain multiple of the minimum distance. The following is the feature matching map obtained by using different multiple n to filter sonar image in Fig. 4b. Fig. 5Open in figure viewerPowerPoint Feature image corresponding to different Hessian matrix threshold (a) Threshold value of the Hessian matrix is 400, and the number of detected feature points is 960, (b) Threshold value of the Hessian matrix is 800, and the number of detected feature points is 561, (c) Threshold value of The Hessian matrix is 1200, and the number of detected feature points is 397 After screening points pairs with Euclidean distance, there will still be wrong matching information, like the connection line with large dip angles in Fig. 6b. The inclination angle is taken as the second constraint condition for further screening, and Fig. 7 is the result of adding an angle constraint to Fig. 6b. Obviously that connection line with a large dip angle in Fig. 6b is removed. Fig. 6Open in figure viewerPowerPoint Feature matching maps corresponding to different multiples n (a) The value of n is 1.5 and the number of matched feature point pairs is 15, (b) The value of n is 2.0 and the number of matched feature point pairs is 50, (c) The value of n is 2.5 and the number of matched feature point pairs is 127 Fig. 7Open in figure viewerPowerPoint Add angle constraint to Fig. 6b The matched feature point pairs are screened by inclination angle and distance, and their number corresponding to different values of n and θ is shown in Table 1. As can be seen, the number of point pairs is more sensitive to parameter n, and the change in angle has less influence on it, which is because angle constraint is mainly used to filter out a few point pairs with a large dip angle. Verified by experiment, when the number of point pairs is between 50 and 80, the success rate of the sonar image mosaic is relatively high. Table 1. Number of matched feature point pairs for different value of n and θ n θ 5° 10° 15° 1.8 31 32 32 2.0 45 46 48 2.2 56 58 61 2.4 72 79 84 When . The homography obtained with RANSAC algorithm matrix is Based on the homography matrix, the right image is registered and its height is limited to be the same as the image on the left. The result is shown in Fig. 8. Fig. 8Open in figure viewerPowerPoint Feature matching maps corresponding to different multiples n (a) Left image, (b) Registered right image, (c) Image spliced The coordinates of the four vertices of the registered right image are Top left vertex: (52.079, 31.140); Top right vertex: (95.548, 609.378); Lower left vertex: (356.033, − 37.321); Lower right vertex: (475.209, 723.378). Then create a new image to output the result of image mosaic, and copy the left image and the registered left image to the new image to complete the splicing, and Fig. 8c is the result. It can be seen that the transition region is not natural enough, with obvious crack. Fusion has been used by implementing formulas (8) and (9) to obtain Fig. 9. The transition region of Fig. 9 is relatively smooth, with better integrity and no crack, but the transition region is a little fuzzy, and edges of objects are not obvious enough. The values of variance, entropy and Tenengrad gradient of the image before and after optimisation are shown in Table 2. In general, the values of variance and Tenengrad gradient of the optimised image are smaller than the untreated image, while the value of entropy of the optimised image is larger. The weighted fusion algorithm will remove most of the maximum and minimum values, making the optimised image's variance value smaller. The increase of entropy value of the optimised image indicates that the information amount is increased and the integrity of the image is better, and the decrease of the Tenengrad gradient value indicates that the optimised image is not clear enough, which is consistent with the previous subjective evaluation. Through subjective evaluation and objective evaluation, it can be concluded that the optimisation process successfully removed the crack, making the mosaic image better and smoother, but also sacrificed the clarity of the mosaic image. Fig. 9Open in figure viewerPowerPoint Optimised spliced sonar image Table 2. Evaluation index value of image before and after optimisation Image Index Variance Entropy Tenengrad gradient before optimisation 46.7784 6.6314 7.7181 × 107 after optimisation 45.1742 6.9328 7.3322 × 107 4 Conclusion In this study, sonar image mosaic is studied through steps of de-noising, feature detection and recognition, feature matching, sonar image splicing, and optimisation. The speckle noise in the sonar image is filtered by median filter. The SURF method was used for feature detection and recognition of sonar images in view of its translation, rotation, scale invariance, and good processing speed. In the process of feature matching, the Brute-force matching algorithm is used to ensure every feature point pair to find the best match. A method, which filters out unqualified point pairs by restraining dip angle and Euclidean distance between them, is proposed to ensure that the preserved matched feature point pairs are best for splicing. In previous research papers, only distance information was used as the screening condition, and the results were not ideal. In this study, the dip angle of matched feature point pairs was used as the second screening condition, which could further optimise the results of feature matching, and had a direct impact on the success rate of mosaic and the quality of the final sonar mosaic image. Then, the homography matrix is obtained to acquire the mapping relationship between two images and finish the sonar image splicing process. Finally, a weighted fusion algorithm is proposed to optimise the image and remove the crack in the mosaic. Through subjective evaluation and objective evaluation, it can be concluded that the method can effectively improve the success rate and stability of sonar image mosaic, and can be applied to the fast mosaic of mobile sonar detection. 5 Acknowledgments The authors would like to acknowledge the National Natural Science Foundation of China (no. 51005142), the Innovation Program of Shanghai Municipal Education Commission (no. 14YZ010), and the Natural Science Foundation of Shanghai (nos. 14ZR1414900 and 19ZR1419300) for providing financial support for this work. 6 References 1Klausner N.H., Azimi-Sadjadi M.R.: 'Performance prediction and estimation for underwater target detection using multichannel sonar', IEEE J. Ocean. Eng., 2019, 45, pp. 1– 13 2Mace L.C.: 'Mapping and classifying eastern oyster (Crassostrea virginica) Habitat in Copano Bay, Texas, by coupling acoustic technologies', J. Coast. 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