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.
Tesfaye G-Michael and Rodney G. Roberts, "Change detection in sonar images using independent component analysis," Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 101820K (Presented at SPIE Defense + Security: April 10, 2017; Published: 4 May 2017); https://doi.org/10.1117/12.2262953.
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