Many image recognition algorithms based on data-learning perform dimensionality reduction before the actual learning and classification because the high dimensionality of raw imagery would require enormous training sets to achieve satisfactory performance. A potential problem with this approach is that most dimensionality reduction techniques, such as principal component analysis (PCA), seek to maximize the representation of data variation into a small number of PCA components, without considering interclass discriminability. This paper presents a neural-network-based transformation that simultaneously seeks to provide dimensionality reduction and a high degree of discriminability by combining together the learning mechanism of a neural-network-based PCA and a backpropagation learning algorithm. The joint discrimination-compression algorithm is applied to infrared imagery to detect military vehicles.

Algorithms are considered for searching wide area forward-looking infrared imagery for military vehicles. Wide area search has typically been handled by using a simple detection algorithm with low computational cost to search the entire image or set of images, followed by a clutter rejection algorithm that analyzes only those portions of the image that are marked by the detection algorithm. We start with a feature based detector and a eigen-neural based clutter rejecter, and examine a number of architectures for combining these modules to maximize joint performance. The architectures considered include a clutter rejection threshold method and a nonlinear learning-based combination. The performance of the architectures are compared using a set of several thousand real images.

Adaptive anomaly detectors that find any materials whose spectral characteristics are out of context with those of the neighboring materials are proposed. We use a dual rectangular window that separates the local area into two regions- the inner window region (IWR) and outer window region (OWR). The statistical differences between the IWR and OWR is exploited by generating projection vectors onto which the IWR and OWR vectors are projected. Anomalies are detected if the projection separation between the IWR and OWR vectors is greater than a predefined threshold. Four different methods are used to produce the projection vectors. The proposed anomaly detectors have been applied to HYDICE (HYper-spectral Digital Imagery Collection Experiment) images and detection performance for each method has been measured.

Many pattern recognition (ATR) systems perform dimensionality reduction on the input imagery to reduce the requirement for a large number of training samples and high computational cost. Most of the dimensionality reduction techniques seek to optimize only the overall data compression, but not the interclass discriminability. We present a neural-network-based algorithm that simultaneously achieves data compression and target discriminability by adjusting the pre-trained base components to maximize separability between classes. This allows the classifiers to operate at a higher level of efficiency and generalization capability on low-dimensional data. We have applied this technique to the problem of automatic detection of military vehicles in infrared imagery.

An approach to model-based dynamic object verification and identification using video is proposed. From image sequences containing the moving object, we compute its motion trajectory. Then we estimate its three-dimensional (3-D) pose at each time step. Pose estimation is formulated as a search problem, with the search space constrained by the motion trajectory information of the moving object and assumptions about the scene structure. A generalized Hausdorff (1962) metric, which is more robust to noise and allows a confidence interpretation, is suggested for the matching procedure used for pose estimation as well as the identification and verification problem. The pose evolution curves are used to assist in the acceptance or rejection of an object hypothesis. The models are acquired from real image sequences of the objects. Edge maps are extracted and used for matching. Results are presented for both infrared and optical sequences containing moving objects involved in complex motions.

A pulsed ladar based object-recognition system with applications to automatic target recognition (ATR) is presented. The approach used is to fit the sensed range images to range templates extracted through a laser physics based simulation applied to geometric target models. A projection-based prescreener filters out more than 80% of candidate templates. For recognition, an M of N pixel matching scheme for internal shape matching is combined with a silhouette matching scheme. The system was trained on synthetic data obtained from the simulation, and has been blind tested on a data set containing real ladar images of military vehicles at various orientations and ranges. Successful blind testing on real imagery demonstrates the utility of synthetic imagery for training of recognizers operating on ladar imagery.

An automatic target detector often produces too many false alarms that could bog down the performance of a subsequent target classifier. Therefore, we need a good clutter rejector to remove as many clutterers as possible, before feeding the most likely target detections to the classifier. We investigate the benefits of using dual-band forward-looking infrared images to improve the performance of an eigen-neural based clutter rejector. With individual or combined bands as input, we use either principal component analysis or the eigenspace separation transform to perform feature extraction and dimensionality reduction. The transformed data is then fed to a properly trained multilayer perceptron that predicts the identity of the input, which is either a target or clutter. Experimental results are presented on a dataset of real dualband images.

In a typical automatic target recognition (ATR) system, the target detection module often produces too many false alarms, which may severely inhibit the effectiveness of the subsequent target classifier module. An effective clutter rejector is therefore needed between these two modules to reduce these false alarms. We explore the potential benefits of using dual-band infrared imagery to improve the performance of an eigenneural-based clutter rejector. Individual or combined bands of images are first compressed through eigenspace transformations, such as principal component analysis. The transformed data are then fed to a neural network that decides whether the input is a target or clutter. A huge and realistic set of dual-band passive infrared images was used in a series of experiments.

We present an adaptive hierarchical segmentation algorithm based on quadtree decomposition and a modified minimum-distance classifier. The proposed algorithm uses quadtree decomposition because this technique can adapt to the local characteristics of the hyperspectral data. A feature vector (i.e., a class centroid) for each material type is recursively estimated and updated, so that increasingly accurate segmentation results are achieved as the decomposition proceeds. The proposed method provides improved segmentation performance over standard template-matching segmentation techniques because it adapts to the local context. It also imposes a spatial smoothness constraint on the pixel classification that provides spatial continuity during the segmentation process. Both the proposed algorithm and a standard template-matching technique were applied to a set of visible to near-infrared hyperspectral images results are presented.

We introduce the concepts of dynamic object identification and verification using video. A generalized Hausdorff metric, which is more robust to noise and allows a confidence interpretation, is suggested for the identification and verification problem. Parameters from sensor motion compensation procedure are incorporated into the search step such that the Hausdorff metric based matching can be achieved efficiently under more complex transformation groups. An algorithm is proposed for identification/verification based on edge map matching using the generalized Hausdorff metric. Experiments on infrared video sequences are provided.

The preprocessing stage of an automatic target recognition system extracts areas containing potential targets from a battlefield scene. These potential target images are then sent to the classification stage to identify the targets. It is highly desirable at the preprocessing stage to minimize the incorrect rejection rate. This, however, results in a high false alarm rate. The high false alarm rate, in turn, makes subsequent target classification decisions unreliable. We present a new technique to reject false alarms (clutter images) produced by the preprocessing stage. Our technique, which we call region-based principal component analysis (PCA), uses topological features of the targets to reject false alarms. In this technique a potential target is divided into several regions and a PCA is performed on each region to extract regional feature vectors. We propose to use regional feature vectors of arbitrary shapes and dimensions that are optimized for the topology of a target in a particular region. These regional feature vectors are then used by a two-class classifier based on the learning vector quantization to decide whether a potential target is a false alarm or a real target.

A pulsed laser radar (ladar) based object recognition system with applications to automatic target recognition is reported. The approach used is to fit the sensed range images to the range templates extracted using laser physics based simulation of Computer Aided Design target models. A projection based pre-screener filters out more than 80 percent of candidate templates. An M of N pixel matching scheme for internal shape matching combined with a silhouette matching scheme is used for recognition. The system has been blind tested on a data set containing 276 real ladar images of military vehicles at various orientations and different ranges. The system achieves above 90 percent accuracy in recognition of 0.4 meters resolution ladar images.

A modular neural network classifier has been applied to the problem of automatic target recognition using forward-looking infrared (FLIR) imagery. The classifier consists of several independently trained neural networks. Each neural network makes a decision based on local features extracted from a specific portion of a target image. The classification decisions of the individual networks are combined to determine the final classification. Experiments show that decomposition of the input features results in performance superior to a fully connected network in terms of both network complexity and probability of classification. Performance of the classifier is further improved by the use of multiresolution features and by the introduction of a higher level neural network on the top of the individual networks, a method known as stacked generalization. In addition to feature decomposition, we implemented a data-decomposition classifier network and demonstrated improved performance. Experimental results are reported on a large set of real FLIR images.

Composite classifiers consisting of a number of component classifiers have been designed and evaluated on the problem of automatic target recognition (ATR) using a large set of real forward-looking infrared (FLIR) imagery. Two existing classifiers are used as the building blocks for our composite classifiers. The performance of the proposed composite classifiers are compared based on their classification ability and computational complexity. It is demonstrated that the composite classifier based on a cascade architecture greatly reduces the computational complexity with a statistically insignificant decrease in performance in comparison to standard classifier fusion algorithms.

A probe-based approach combined with image modeling is used to recognize targets in spatially resolved, single frame, forward looking infrared (FLIR) imagery. A probe is a simple mathematical function that operates locally on pixel values and produces an output that is directly usable by an algorithm. An empirical probability density function of the probe values is obtained from a local region of the image and used to estimate the probability that a target of known shape is present. Target shape information is obtained from three-dimensional (3-D) computer-aided design (CAD) models. Knowledge of the probe values along with probe probability density functions and target shape information allows the likelihood ratio between a target hypothesis and background hypothesis to be written. The generalized likelihood ratio test is then used to accept one of the target poses or to choose the background hypothesis. We present an image model for infrared images, the resulting recognition algorithm, and experimental results on three sets of real and synthetic FLIR imagery.

A probe based approach is presented for the recognition of targets in a cluttered background using an infrared imager. A probe is a simple mathematical function which operates locally on image grey levels and produces an output that is more directly usable by an algorithm. A directional probe image is calculated by taking the difference in grey levels between pixels a set distance apart in a given direction, centered on the probe image pixel. A parametric statistical image background model which describes the probe images is introduced. The parameters of the probe image model can be readily estimated from the image. Knowledge of these parameters, together with target signatures obtained from Computer Aided Design (CAD) models, allows the likelihood ratio for a given object pose hypothesis versus the background null hypothesis to be written. The generalized likelihood ratio test is used to accept one of the object poses or to choose the null hypothesis. Results of the method applied to a large set of terrain model board images are presented.>