The automated detection of changes occurring between multi-temporal images is of significant importance in a wide range of medical, environmental, safety, as well as many other settings. The usage of k-means clustering is explored as a means for detecting objects added to a scene. The silhouette score for the clustering is used to define the optimal number of clusters that should be used. For simple images having a limited number of colors, new objects can be detected by examining the change between the optimal number of clusters for the original and modified images. For more complex images, new objects may need to be identified by examining the relative areas covered by corresponding clusters in the original and modified images. Which method is preferable depends on the composition and range of colors present in the images. In addition to describing the clustering and change detection methodology of our proposed approach, we provide some simple illustrations of its application.
Change Detection is the process of detecting changes from pairs of multi-temporal sonar images that are surveyed approximately from the same location. The problem of change detection and subsequent anomaly feature extraction is complicated due to several factors such as the presence of random speckle pattern in the images, variability in the seafloor environmental conditions, and platform instabilities. These complications make the detection and classification of targets difficult. In this paper, we propose a change detection technique for multi-temporal synthetic aperture sonar (SAS) images, based on independent component analysis (ICA). ICA is a well-established statistical signal processing technique that aims at decomposing a set of multivariate signals (in our case SAS images) into a base of statistically independent data-vectors with minimal loss of information content. The goal of ICA is to linearly transform the data such that the transformed variables are as statistically independent from each other as possible. The changes in the imaging scene are detected in reduced second or higher order dependencies by ICA and the correlation among the multi-temporal images is removed. Thus removing dependencies will leave with the change features that will be further analyzed for detection and classification. Test results of the proposed method on SAS images (snippets) of declared changes from automated change detection (ACD) process will be presented. These results will illustrate the effectiveness of ICA for reduction of false alarms in the ACD process.
Some synthetic aperture imaging techniques have strict path requirements and need to be processed in overlapping chunks. Path adjustments applied for images can decorrelate and alter the appearance of overlapping regions, which can make mosaicing the images chunks for post mission analysis challenging. In this paper, we investigate the application of automatic change detection techniques to the problem of high accuracy, multi-band mosaicing. We will describe and demonstrate the aspects of synthetic aperture sonar (SAS) change detection, in particular automated change detection (ACD) co-registration processing that enables the production of good quality of seabed maps known as mosaicing where individual SAS runs from different traverses are pieced together in adjacent positions to form a complete image.
Automated change detection (ACD) is a technique that automatically discerns any area of change when comparing two images of the same geographic location over different moments in time. Within the ACD processing stream, co-registration ensures the areas depicted in two images coincide. The difficulty in co-registering sonar images of the sea floor can arise from a difference in vehicle trajectories, low resolution, and the presence of noise. Moreover, the changing features of the sea floor can further add to the difficulty. The successful co-registration of sonar images is important when comparing images, and is thus required in areas such as change detection and mosaicing. In this effort, a three-step co-registration process is used: co-registration by navigational alignment, fine-scale co-registration using SIFT, and local co-registration that corrects navigational differences. In this paper, we focus on the final step where phase alignment occurs. To eliminate unreliable unwrapped phase data, we introduce a novel histogram based adaptive thresholding technique which rejects errors in phase alignment occurring in the across-track direction of the vehicle. Further, an adaptive thresholding technique is applied to the change-map generated following the co-registration stage. To isolate pixels of interest related to anomalies or targets, a thresholding method is applied in conjunction with principal and independent component analysis (PCA and ICA).
We will demonstrate the effectiveness of these adaptive thresholding techniques in sub-pixel co-registration and target detection.
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.
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