Block-based detection methods for underwater target detection and classification from electro-optical imagery - Kabatek, Michael Jonathan, author; Azimi-Sadjadi, Mahmood R., advisor; Pezeshki, Ali, committee member; Wu, Mingzhong, committee member (Colorado State University. Libraries, 2010)
Detection and classification of underwater mine-like objects is a complicated problem due to various factors such as variations in the operating and environmental conditions, presence of spatially varying clutter, target obstruction and occlusion variations in target shapes, compositions, and orientation. Also contributing to the difficulty of the problem is the lack of a priori knowledge about the shape and geometry of new non-mine-like objects that may be encountered, as well as changes in the environmental or operating conditions encountered during data collection. Two different block-based methods are proposed for detecting frames and localization of mine-like objects from a new CCD-based Electro-optical (EO) imaging system. The block-based methods proposed in this study serve as an excellent tool for detection in low contrast frame sequences, as well as providing means for classifying detected objects as target or non-target objects. The detection methods employed provide frame location, automatic object segmentation, and accurate spatial locations of detected objects. The problem studied in this work is the detection of mine-like objects from a new CCD imagery data set which consists of runs containing tens to hundreds of frames (taken by the CCD camera). The goal is to detect frames containing mine-like objects, as well as locating detected objects and segmenting them from the frame to be subsequently classified as mine-like objects or background clutter. While object segmentation and classification of detected objects are also required as with the previous EO systems, the main challenge is successful frame detection with low false alarm rate. This has prompted research on new detection methods which utilize block- based snapshot information in order to identify potential frames containing targets, and spatially localize detected objects within those detected frames. More specifically, we have addressed CCD object detection problem by developing block-based Gauss-Gauss and matched subspace formulations. The block-based detection framework is applied to raw CCD data directly from the sensor without the need for computationally expensive filtering or pre-processing as with the previous methods. The detector operates by measuring the log-likelihood ratio in each block of a given frame and provides a spatial 'likelihood map'. This detection process pro- vides log-likelihood measurements of blocks in a given EO image which can then be thresholded to generate regions of interest within frame to be subsequently classified. This two-step process in both the Gauss-Gauss and matched subspace detectors consists of first measuring the log-likelihood, and determining frame of interest and then the regions of interest (ROI), and finally classifying the detected object ROIs, based upon shape-dependent features. Complex Zernike moments are extracted from each region of interest which are subsequently used to classify detected objects. The shape-based Zernike moments provide rotational invariance, and robustness to noise which are desirable characteristics for classification. This block-based framework provides flexibility in the detection methods used prior to object classification, and solves the problem of having to invoke a classification system on every CCD frame by determining frames containing only potential targets. A comprehensive study of the block-based detection and classification methods is carried out on a CCD imagery data set. A comparison is made on the detection and false alarm rate performance for the Gauss-Gauss and matched subspace detectors on the CCD data sets acquired from the Applied Signal Technologies in Sunnyvale, CA. In addition a neural-network based classification system is employed to perform object classification based upon the extracted Zernike moments. The tested data set from AST consist of ten runs over the mine field each run containing up to several hundred frames. The total number of frames tested totals 1317, with 16 frames containing a single or partial targets in five of the data runs. Results illustrating the effectiveness of the proposed detection methods are presented in terms of correct detection and false alarm rates. It is observed that the low-rank Gauss-Gauss detector provides an overall frame detection rate of 100% at the cost of a false alarm rate of 36.9%. The matched subspace detector outperforms the Gauss-Gauss method and reduces the false frame detection rate by 16.9%. Using the Zernike features extracted from the matched subspace detector's output and an artificial neural network classifier yields a true frame detection rate of Pd = 100% at the cost of Pfd = 16:8% reducing the detected false frames detected by 3.3%. The reduced-rank Gauss-Gauss detector has a detection rate of Pd = 100% at the cost of probability of false detection Pfd = 36:9%, using features extracted from the reduced-rank Gauss-Gauss detector's output passed to the neural network classifier yields a true detection rate of Pd = 100% at the cost of Pfd = 21:7% which significantly reduces the detected false frames by 15.1%.
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