Fourier analysis based focusing of synthetic aperture radar (SAR) data collected during circular flight path
is a recent advancement in SAR signal processing. Fast CSAR algorithm uses the Householder transform to
obtain a ground plane circular SAR (CSAR) signal phase history from the slant plane CSAR phase history by
inverting the linear shift-varying system model, thereby circumventing the need for explicitly computing a pseudo-inverse.
The Householder transform has recently been shown to have improved error bounds and stability as an
underdetermined and ill-conditioned system solver, and the Householder transform is computationally efficient.
This paper utilizes the methodology of SAR2 algorithm, a two dimensional variant of the ω-k algorithm, to refocus
out-of-focus images. Refocusing of images may be necessary in machine vision as a preprocessing step before edge
detection or image segmentation in the imaging and manipulation of 3D objects. The SAR2 algorithm generates a
complex amplitude distribution and the corresponding point spread function in a manner similar to Fraunhofer
diffraction distribution model and its point spread function as seen in Fourier optics. The matched filter utilized in the
SAR2 algorithm has a focus-in-altitude interpretation and may be varied to increase or decrease the radius of out-of-focus
blur associated with a particular point spread function of scatterers of various heights. This paper demonstrates
focusing of a line object L={1 : x=y;-≤x≤63; -64≤y≤63}. Although a rectangular aperture is used in the refocusing
process, other apertures may also be used such as circular or Gaussian.
Synthetic Aperture Radar (SAR) is capable of producing high-resolution terrain images from data collected by a relatively small airborne or spaceborne antenna. This data collection is done in cross-range or slow-time along flight trajectory and range or fast-time along direction of electromagnetic wave propagation. The slow-time imaging is what distinguishes SAR from its predecessor imaging radars. The high resolution pulse compression based fast-time imaging in range introduces some visual artifacts into SAR imagery due to range skew and phase information anomaly due to residual video phase (RVP). In this paper, we introduce the concept of SAR 2D aperture synthesis that extends the slow-time imaging concept to range and relies on a single frequency instead of chirp. Moreover, our 2D aperture synthesis implementation does not need computationally expensive Stolt interpolation.
Nearest neighbor classifiers with direct sum successive approximation (DSSA) templates are shown to be effective for detecting and discriminating mines and mine-like objects in forward looking sonar data. DSSA results are demonstrated on data obtained form field measurements with actual mines and calibration targets. The DSSA templates are used in a nearest neighbor classifier that can be characterized as a new type of radial basis function neural network. This neural network is not designed with a preset complexity level as quantified by an a priori determined number of degrees-of-freedom. Rather, the system is constructed incrementally and adds additional degrees-of-freedom as required by the nature of the training data. The neural net system possesses stage structure that result in inherent computational and memory efficiency in searching and storing the DSSA-based radial basis functions.
Undetected sea mines in a littoral environment are dangerous threats that must be first detected and then avoided or neutralized in the conduct of strategic and tactical warfare. The U.S. Navy is seeking enabling sensor suites and associated algorithms that allow autonomous underwater vehicles to search, detect and destroy sea mines. Acoustic backscatter is a sensing mechanism that permits searches to be conducted at comparatively long ranges and thus would enable high area coverage rates. The research problem addressed in this paper is the development of an algorithm that allows acoustic backscatter to be used to detect and classify mines and mine like objects (MLOs). This paper presents a novel approach of fusing and classifying multiple acoustic backscatter signals for the purpose of identifying mines and mine-like objects at long ranges. The algorithm relies on an underlying database of measured target signatures for classification purposes and uses a set of quick search templates that encapsulate the target information contained in this 'knowledge-pool' database. The templates are mathematically structured to permit database searches to be performed in real time with low to moderate computational resources. The mathematical structure of the search templates is hierarchical in nature and allows the signal processing tasks of mine detection, discrimination, and identification to be performed by a single integrated system in a progressive manner. This classification system also knows when data of an unknown nature is encountered.
A mine detection algorithm based on the us of structured templates applied to acoustic backscatter data is proposed. The structured templates correspond to the codevectors of a type of cluster-based compression algorithm called residual vector quantization (RVQ). The RVQ clusters have a hierarchical structure that permits efficient searches for nearest neighbor templates, and efficient dictionary storage for memory cost reduction. The structured templates are generated by a multistage synthesis process that produces a sequence of finite precision representations of training data. This successive approximation process is combined with a sequential classification process to form a new type of classifier called a direct sum successive approximation classifier.
The use of residual (multiple stage) vector quantizer codevectors in a nearest neighbor classifier for direct classification of image pixel data is proposed. This approach combines the successive approximation process generated by the residual vector quantizer with sequential decision making. This approach potentially has the advantage of making large data base searches for small object or texture recognition in images both computation and memory efficient.
The feasibility of using residual (multiple stage) vector quantizer codevectors in a nearest neighbor classifier for direct classification of sonar pixel data is established. This approach combines the successive approximation process generated by the residual vector quantizer with successive decision making. Experimental results show that the probability of detection is about 80% and that the false alarm rate if about 5.6 false alarms per image. These initial performance benchmarks are encouraging considering the heuristic manner in which the residual vector quantizer codebooks were employed in the nearest neighbor classifier.
This paper reports on a current investigation of a new approach to vector quantization (VQ) for image coding. The approach consists of integrating finite state vector quantization (FSVQ) into a binary residual vector quantizer structure. Due to the inherently predictive nature of FSVQ, the quality of image representation is often greatly superior to that of memoryless VQ (for small vector sizes). However, the codebook storage requirements are typically very large by comparison. The objective of this work is to reduce the codebook storage requirement of FSVQ without significantly impairing the reproduction quality of the coded image. Experimental results indicate that low bit rate systems based on a combination of FSVQ and RVQ can be designed with performance approaching the quality of the FSVQ schemes but with only a very small fraction of the storage requirement.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.