SET Corporation, under contract to the Air Force Research Laboratory, Sensors Directorate, is building a
Real-time Aerial Video Exploitation (RAVE) Station for Small Unmanned Aerial Vehicles (SUAVs). Users of
SUAVs have in general been underserved by the exploitation community because of the unique challenges of
operating in the SUAV environment. SUAVs are often used by small teams without the benefits of dedicated
personnel, equipment, and time for exploitation. Thus, effective exploitation tools for these users must have
sufficiently automated capabilities to keep demands on the team's labor low, with the ability to process video
and display results in real-time on commonly-found ruggedized laptops. The RAVE Station provides video
stabilization, mosaicking, moving target indicators (MTI), tracking, and target classification, and displays the
results in several different display modes. This paper focuses on features of the RAVE Station implementation
that make it efficient, low-cost, and easy to use. The software architecture is a pipeline model, allowing each
processing module to tap off the pipe, and to add new information back into the stream, keeping redundancy to
a minimum. The software architecture is also open, allowing new algorithms to be developed and plugged in.
Frame-to-frame registration is performed by a feature-tracking algorithm which employs RANSAC to discard
outlying matches. MTI is performed via a fast and robust three frame differencing algorithm. The user interface
and exploitation functions are simple, easy to learn and use. RAVE is a capable exploitation tool that meets the
needs of SUAV users despite their challenging environment.
Image segmentation is a process to extract and organize information energy in the image pixel space according to a prescribed feature set. It is often a key preprocess in automatic target recognition (ATR) algorithms. In many cases, the performance of image segmentation algorithms will have significant impact on the performance of ATR algorithms. Due to the variations in feature set definitions and the innovations in the segmentation processes, there is large number of image segmentation algorithms existing in ATR world. Recently, the authors have investigated a number of measures to evaluate the performance of segmentation algorithms, such as Percentage Pixels Same (pps), Partial Directed Hausdorff (pdh) and Complex Inner Product (cip). In the research, we found that the combination of the three measures shows effectiveness in the evaluation of segmentation algorithms against truth data (human master segmentation). However, we still don't know what are the impact of those measures in the performance of ATR algorithms that are commonly measured by Probability of detection (PDet), Probability of false alarm (PFA), Probability of identification (PID), etc. In all practical situations, ATR boxes are implemented without human observer in the loop. The performance of synthetic aperture radar (SAR) image segmentation should be evaluated in the context of ATR rather than human observers.
This research establishes a segmentation algorithm evaluation suite involving segmentation algorithm performance measures as well as the ATR algorithm performance measures. It provides a practical quantitative evaluation method to judge which SAR image segmentation algorithm is the best for a particular ATR application. The results are tabulated based on some baseline ATR algorithms and a typical image segmentation algorithm used in ATR applications.
This paper presents a new paradigm for feature extraction and segmentation of SAR imagery. Most of the existing segmentation algorithms explore the features based on the variations in image intensity, contrast and texture, mimicking human SAR scene analysts. Like medical ultrasound imaging, CT imaging and magnetic resonance imaging, the imaging modality of SAR is not consistent with the natural ability of human vision. That is why we need trained experts to analyze those medical images as well as SAR images. In the ATR application, SAR imagery will be processed and segmented by automatic computer algorithms without human analysts in the loop. Therefore, in order to fully utilize the capability of SAR as an advanced surveillance instrument, we need to develop a feature space that is based on the physics of SAR imaging modality, not the human visual perception. After the definition of feature space, we can process the SAR sensor data in the image domain or even before image formation. In this research, we try to focus on establishing a new SAR image segmentation processing paradigm based on the discrete frame theory. We will show the framework of the paradigm on a limited feature space covering some SAR attributes like targets and shadows. After setting up the feature space, we will develop a discrete frame to transform SAR sensor data into a feature space representation. The feature space representation consists of transform coefficients that indicate the location and strength of the features. Those transform coefficients can be further manipulated by some classification algorithms for ATR exploitation.
Correlators have been used for detecting shapes but not as often for measuring shape similarity. The complex inner product (CIP) has been used in various formulations as a shape similarity measure. The CIP is essentially a one-dimensional correlation approach to measuring similarity. One-dimensional variants of the correlation techniques including the matched filter (MF), phase-only filter (POF), and amplitude-modulated phase only filter (AMPOF) are shown to measure shape similarity in a trend that approaches human perception, however, clear performance differences are noted. The results show that the best correlator for measuring shape similarity is not the best correlator for detecting a shape. It is suggested that detection and shape similarity are fundamentally different functions that are in opposition to some degree. Ideal detection and ideal similarity measurement functions are explored. The degree to which various formulations of correlators approach the ideal functions of detection and similarity measurement are shown as well as results from human psychophysical experiments.
Measuring a system's capability to acquire accurate three- dimensional shape is important for validating the system for a particular application. Various system factors are reviewed that contribute to inaccurate shape. As shown in this paper, different shape measures do not do a complete evaluation but provide different information depending on the type of error. A partial-directed hausdorf (PDH) and complex inner product (CIP) measure that were previously introduced to measure two-dimensional shapes are now extended to measure three-dimensional shapes. PDH measures how close the 3D surface is to the ideal 3D surface within a predefined acceptable error margin, while the CIP measures how well the 3D surface correlates to the ideal 3D surface. Two variants of the CIP measure are used in this paper including a pure phase only filter and a normalized matched filter. The CIP measure is compared to the Procrustes metric for comparing shapes. Using a test case shape, the measures are compared and shown to provide varying information. Alone, any one measure cannot provide complete shape information. Combining measures provides a more robust three-dimensional shape measurement system. The shape measures are demonstrated first on three-dimensional data with controlled variation and then on laser ranging data.
Because of the large number of SAR images the Air Force generates and the dwindling number of available human analysts, automated methods must be developed. A key step towards automated SAR image analysis is image segmentation. There are many segmentation algorithms, but they have not been tested on a common set of images, and there are no standard test methods. This paper evaluates four SAR image segmentation algorithms by running them on a common set of data and objectively comparing them to each other and to human segmentations. This objective comparison uses a multi-measure approach with a set of master segmentations as ground truth. The measure results are compared to a Human Threshold, which defines the performance of human segmentors compared to the master segmentations. Also, methods that use the multi-measures to determine the best algorithm are developed. These methods show that of the four algorithms, Statistical Curve Evolution produces the best segmentations; however, none of the algorithms are superior to human segmentations. Thus, with the Human Threshold and Statistical Curve Evolution as benchmarks, this paper establishes a new and practical framework for testing SAR image segmentation algorithms.