The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.

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Object tracking: A survey

Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.

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Hyperspectral Remote Sensing Data Analysis and Future Challenges

Image processing with neural networks–a review

Airborne laser scanning (ALS) is an active remote sensing technique providing range data as 3D point clouds. This paper aims at presenting a survey of the literature related to such techniques, with emphasis on the new sensors called full-waveform lidar systems. Indeed, an emitted laser pulse interacts with complex natural and man-made objects leading to a temporal distortion of the returned energy profile. The new technology of full-waveform laser scanning systems permits one to digitize the complete waveform of each backscattered pulse. Full-waveform lidar data give more control to an end user in the interpretation process of the physical measurement and provide additional information about the structure and the physical backscattering characteristics of the illuminated surfaces. In this paper, the theoretical principles of full-waveform airborne laser scanning are first described. Afterwards, a review of the main sensors as well as signal processing techniques are presented. We then discuss the interpretation of full-waveform measures with special interest on vegetated and urban areas.

Full-waveform topographic lidar : State-of-the-art

We present a nonlinear version of the well-known anomaly detection method referred to as the RX-algorithm. Extending this algorithm to a feature space associated with the original input space via a certain nonlinear mapping function can provide a nonlinear version of the RX-algorithm. This nonlinear RX-algorithm, referred to as the kernel RX-algorithm, is basically intractable mainly due to the high dimensionality of the feature space produced by the nonlinear mapping function. However, in this paper it is shown that the kernel RX-algorithm can easily be implemented by kernelizing the RX-algorithm in the feature space in terms of kernels that implicitly compute dot products in the feature space. Improved performance of the kernel RX-algorithm over the conventional RX-algorithm is shown by testing several hyperspectral imagery for military target and mine detection.

Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery

We propose adaptive anomaly detectors that find materials whose spectral characteristics are substantially different from those of the neighboring materials. Target spectral vectors are assumed to have different statistical characteristics from the background vectors. 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 spectral differences between the IWR and OWR are exploited by generating subspace projection vectors onto which the IWR and OWR vectors are projected. Anomalies are detected if the pro- jection separation between the IWR and OWR vectors is greater than a predefined threshold. Four different methods are used to produce the subspace projection vectors. The four proposed anomaly detectors are applied to Hyperspectral Digital Imagery Collection Experiment (HY- DICE) images and the detection performance for each method is evaluated. © 2003 Society of Photo-Optical Instrumentation Engineers.

Adaptive anomaly detection using subspace separation for hyperspectral imagery

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.

Automatic target recognition using a feature-decomposition and data-decomposition modular neural network

We describe a computer simulation of atmospheric and target effects on the accuracy of range measurements using pulsed laser radars with p-i-n or avalanche photodiodes for direct detection. The computer simulation produces simulated images as a function of a wide variety of atmospheric, target, and sensor parameters for laser radars with range accuracies smaller than the pulse width. The simulation allows arbitrary target geometries and simulates speckle, turbulence, and near-field and far-field effects. We compare simulation results with actual range error data collected in field tests.

Simulation of error in optical radar range measurements

Experimental Evaluation of FLIR ATR Approaches–A Comparative Study

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.

Sandor Z. Der

Papers

Adaptive anomaly detection using subspace separation for hyperspectral imagery

Automatic target recognition using a feature-decomposition and data-decomposition modular neural network

Simulation of error in optical radar range measurements

Experimental Evaluation of FLIR ATR Approaches–A Comparative Study

Model-based temporal object verification using video