Coherent Change Detection (CCD) is a process of highlighting an area of activity in scenes (seafloor) under survey and generated from pairs of synthetic aperture sonar (SAS) images of approximately the same location observed at two different time instances. The problem of CCD and subsequent anomaly feature extraction/detection is complicated due to several factors such as the presence of random speckle pattern in the images, changing environmental conditions, and platform instabilities. These complications make the detection of weak target activities even more difficult. Typically, the degree of similarity between two images measured at each pixel locations is the coherence between the complex pixel values in the two images. Higher coherence indicates little change in the scene represented by the pixel and lower coherence indicates change activity in the scene. Such coherence estimation scheme based on the pixel intensity correlation is an ad-hoc procedure where the effectiveness of the change detection is determined by the choice of threshold which can lead to high false alarm rates. In this paper, we propose a novel approach for anomalous change pattern detection using the statistical normalized coherence and multi-pass coherent processing. This method may be used to mitigate shadows by reducing the false alarms resulting in the coherent map due to speckles and shadows. Test results of the proposed methods on a data set of SAS images will be presented, illustrating the effectiveness of the normalized coherence in terms statistics from multi-pass survey of the same scene.
In this paper, an automated change detection technique is presented that compares new and historical seafloor images created with sidescan synthetic aperture sonar (SAS) for changes occurring over time. The method consists of a four stage process: a coarse navigational alignment; fine-scale co-registration using the scale invariant feature transform (SIFT) algorithm to match features between overlapping images; sub-pixel co-registration to improves phase coherence; and finally, change detection utilizing canonical correlation analysis (CCA). The method was tested using data collected with a high-frequency SAS in a sandy shallow-water environment. By using precise co-registration tools and change detection algorithms, it is shown that the coherent nature of the SAS data can be exploited and utilized in this environment over time scales ranging from hours through several days.
In this paper we present a method for clustering and classification of acoustic color data based on statistical
analysis of functions using square-root velocity functions (SVRF). The convenience of the SVRF is that it transforms
the Fisher-Rao metric into the standard L2 metric. As a result, a formal distance can be calculated using
geodesic paths. Moreover, this method allows optimal deformations between acoustic color data to be computed
for any two targets allowing for robustness to measurement error. Using the SVRF formulation statistical models
can then be constructed using principal component analysis to model the functional variation of acoustic color
data. Empirical results demonstrate the utility of functional data analysis for improving performance results in
pattern recognition using acoustic color data.
In this paper a new detection method for sonar imagery is developed in K-distributed background clutter.
The equation for the log-likelihood is derived and compared to the corresponding counterparts derived for the
Gaussian and Rayleigh assumptions. Test results of the proposed method on a data set of synthetic underwater
sonar images is also presented. This database contains images with targets of different shapes inserted into
backgrounds generated using a correlated K-distributed model. Results illustrating the effectiveness of the K-distributed
detector are presented in terms of probability of detection, false alarm, and correct classification rates
for various bottom clutter scenarios.
Proc. SPIE. 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII
KEYWORDS: Target detection, Radon, Sensors, Image resolution, Interference (communication), Feature extraction, Image sensors, Image classification, Simulation of CCA and DLA aggregates, Classification systems
In this paper a new coherence-based feature extraction method for sonar imagery generated from two disparate
sonar systems is developed. Canonical correlation analysis (CCA) is employed to identify coherent information
from co-registered regions of interest (ROI's) that contain target activities, while at the same time extract
useful coherent features from both images. The extracted features can be used for simultaneous detection
and classification of target and non-target objects in the sonar images. In this study, a side-scan sonar that
provides high resolution images with good target definition and a broadband sonar that generates low resolution
images, but with reduced background clutter. The optimum
Neyman-Pearson detector will be presented and
then extended to the dual sensor platform scenarios. Test results of the proposed methods on a dual sonar
imagery data set provided by the Naval Surface Warfare Center (NSWC) Panama City, FL will be presented.
This database contains co-registered pair of images over the same target field with varying degree of detection
difficulty and bottom clutter. The effectiveness of CCA as the optimum detection tool is demonstrated in terms
of probability of detection and false alarm rate.
Detection and classification of underwater objects in sonar imagery are challenging problems. In this paper, a new
coherent-based method for detecting potential targets in high-resolution sonar imagery is developed using canonical
correlation analysis (CCA). Canonical coordinate decomposition allows us to quantify the changes between
the returns from the bottom and any target activity in sonar images and at the same time extract useful features
for subsequent classification without the need to perform separate detection and feature extraction. Moreover,
in situations where any visual analysis or verification by human operators is required, the detected/classified
objects can be reconstructed from the coherent features. In this paper, underwater target detection using the
canonical correlations extracted from regions of interest within the sonar image is considered. Test results of the
proposed method on underwater side-scan sonar images provided by the Naval Surface Warfare Center (NSWC)
in Panama City, FL is presented. This database contains synthesized targets in real background varying in degree
of difficulty and bottom clutter. Results illustrating the effectiveness of the CCA based detection method are
presented in terms of probability of detection, and false alarm rates for various densities of background clutter.