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The capability to detect changes in an underwater scene has many applications, including environmental monitoring, surveillance of strategic maritime waterways, and naval mine countermeasures. This paper examines the automated detection of changes in repeat-pass synthetic aperture sonar (SAS) imagery by exploiting the loss of phase coherence caused by differences in the scene. A renavigation method based on track registration is developed for performing the coregistration of images. The method first determines the corrections to be applied to the repeat-pass navigation data by optimizing a linear track model using the estimated across-track pixel displacements. A second optimization is then performed using the along-track pixel displacements to determine a velocity correction for each ping location of the repeat-pass image to compensate for residual motion errors. The corrected navigation solution is then used to rebeamform the repeat-pass image onto the same focal points as the reference image. The method is demonstrated on a pair of repeat-pass SAS images acquired using the AquaPix INSAS2 sonar, where it is shown that it is not necessary to have access to raw element data to coregister images accurately enough for coherent change detection requirements. This technique is then applied to the second data set obtained using the HISAS 1030 sonar for the purpose of comparing both coherent and noncoherent (amplitude-only) approaches to change detection in scenes with low and high clutters. False alarms caused by the acoustic shadows of objects in high-clutter areas are mitigated using the single-pass interferometric coherence as a reference mask. The results show the successful use of the repeat-pass coherence to detect changes between SAS images with temporal baselines of several days on scenes consisting of mud, gravel, and sand, and demonstrate that a coherent approach is capable of detecting large-scale differences (caused by the introduction or removal of targets) as well as subtle scene changes that are not detectable using noncoherent methods.

International audience; Building an accurate and large digital terrain model (DTM) of the seabed is a key issue in various applications, especially for covert rapid environment assessment using autonomous underwater vehicles (AUVs). New AUV generations are capable of acquiring bathymetry with multiple acoustic sensors: single-beam echosounder, Doppler velocity log, multibeam echosounder (MBES), interferometric sidescan sonar (ISSS), etc. As these sensors acquire the seabed with different geometries, they can be combined to produce a DTM in shorter time. For example, ISSS can reach a wide swath in shallow water but it shows an information gap at nadir, which is usually covered by completing an additional track. Simultaneously using the MBES to acquire the nadir removes the need for this additional track and reduces the energy consumption of the whole survey. This paper focuses on fusion algorithms to extract best information of the two sensors (MBES and ISSS) to feed DTM production software with optimal bathymetric information. This problem may be solved by taking into account the average information of the two sensors. However, the sensors do not always give accurate information and the average information therefore becomes biased. Another way to tackle the problem is to select a priori information given by the better of the two sensors based on a given geometric parameter (e.g., grazing angle). In this case, when the assumed best sensor fails, the information produced by the second sensor cannot be used to compensate for the erroneous information. Our approach consists in using all the available information and fuses it ahead of producing the DTM. Based on the theory of belief functions, this paper presents a framework to fuse the information coming from the two swath bathymetric sensors (MBES and ISSS). The belief theory, applied successfully to other fields, has been extended to handle the bathymetric information. The reliability and the uncertainty of each sonar are introduced in the fusion process to improve the estimation and the accuracy of the final terrain model. First, simulated sonar data, with perfectly known ground truth, are used to quantitatively assess the performance of the fusion process by comparing DTM obtained with and without fusion. Then, the experimental validation is conducted on actual data, acquired simultaneously by the two sonars systems (Klein K5000 ISSS, Reson 8125 MBES) mounted on the DAURADE AUV. Our evaluation of the fusion method shows significant quantitative and qualitative improvement in the production of the final DTM.