Automatic Target Detection Algorithm Research Articles

FPGA Implementation of a Hardware Optimized Automatic Target Detection and Classification Algorithm for Hyperspectral Image Analysis

In hyperspectral image analysis one of the most important tasks is target detection, requiring the execution of algorithms with high computational complexity. Recently, research efforts have focused on on-board real-time target detection to provide timely responses for swift decisions. Therefore, it is necessary to use a technology that provides the performance needed for real-time target detection, and at the same time meets the satellite payload requirements. Field-programmable gate arrays (FPGAs) have very interesting properties in terms of performance, size and power consumption, which have become the standard option for on-board processing. In this letter, we present a hardware optimized implementation for FPGAs of the automatic target detection and classification algorithm (ATDCA) using the Gram–Schmidt (GS) method for orthogonalization purposes. The ATDCA-GS algorithm is directly coded using VHDL and verified on a Virtex-7 XC7VX690T FPGA using real hyperspectral data (collected by Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor and by NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)) and a synthetic image. Experimental results demonstrate that our hardware version of the ATDCA-GS algorithm outperforms previous implementations (multicore processors, GPUs and accelerators) in both computation time (obtaining real-time performance) and power consumption, demonstrating the suitability of FPGAs for this purpose.

Foreign Object Debris Automatic Target Detection for Millimeter-Wave Surveillance Radar.

Foreign Object Debris (FOD) refers to any foreign material on the airfield that may injure and threaten the aircraft and airport system. Due to the complex background on the airfield pavement and weak target echoes in long-distance monitoring, it is not easy to detect objects of various types and sizes. The existing FOD radar system’s detection method has a short effective range, and the detectable objects’ radar cross-section intensity is no less than −20 dBsm. In this paper, we propose an integrated FOD automatic target detection algorithm for millimeter-wave (MMW) surveillance radar to improve small target detection under long-range conditions of over 660 m. The signal form of FOD and a clutter model of ground clutter received by millimeter-wave radar are primarily utilized and established theoretically. The runway edge detection means that it is employed based on the in-continuity features as the runway region of interest during the automatic extraction step. Following the clutter map constant false alarm detection algorithm, we utilize a time-domain algorithm that functions as the vital detection processor. Moreover, an explicit definition of the FOD detection performance is developed in a characteristic quantitative way. This criterion involves an absolute reference value for all FOD radar systems. The well-designed FOD frequency-modulated continuous-wave MMW surveillance radar is utilized, and actual experiments are carried out in a real airport in Beijing, China. The results validate the proposed method’s effectiveness and the superior performance of FOD target detection in long-range situations.

Portability Study of an OpenCL Algorithm for Automatic Target Detection in Hyperspectral Images

In the last decades, the problem of target detection has received considerable attention in remote sensing applications. When this problem is tackled using hyperspectral images with hundreds of bands, the use of high-performance computing (HPC) is essential. One of the most popular algorithms in the hyperspectral image analysis community for this purpose is the automatic target detection and classification algorithm (ATDCA). Previous research has already investigated the mapping of ATDCA on HPC platforms such as multicore processors, graphics processing units (GPUs), and field-programmable gate arrays (FPGAs), showing impressive speedup factors (after careful fine-tuning) that allow for its exploitation in time-critical scenarios. However, the lack of standardization resulted in most implementations being too specific to a given architecture, eliminating (or at least making extremely difficult) code reusability across different platforms. In order to address this issue, we present a portability study of an implementation of ATDCA developed using the open computing language (OpenCL). We focus on cross-platform parameters such as performance, energy consumption, and code design complexity, as compared to previously developed (hand-tuned) implementations. Our portability study analyzes different strategies to expose data parallelism as well as enable the efficient exploitation of complex memory hierarchies in heterogeneous devices. We also conduct an assessment of energy consumption and discuss metrics to analyze the quality of our code. The conducted experiments—using synthetic and real hyperspectral data sets collected by the Hyperspectral Digital Imagery Collection Experiment (HYDICE) and NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS)—demonstrate, for the first time in the literature, that portability across different HPC platforms can be achieved for real-time target detection in hyperspectral missions.

A Novel FPGA-Based Architecture for Fast Automatic Target Detection in Hyperspectral Images

Onboard target detection of hyperspectral imagery (HSI), considered as a significant remote sensing application, has gained increasing attention in the latest years. It usually requires processing huge volumes of HSI data in real-time under constraints of low computational complexity and high detection accuracy. Automatic target generation process based on an orthogonal subspace projector (ATGP-OSP) is a well-known automatic target detection algorithm, which is widely used owing to its competitive performance. However, ATGP-OSP has an issue to be deployed onboard in real-time target detection due to its iteratively calculating the inversion of growing matrices and increasing matrix multiplications. To resolve this dilemma, we propose a novel fast implementation of ATGP (Fast-ATGP) while maintaining target detection accuracy of ATGP-OSP. Fast-ATGP takes advantage of simple regular matrix add/multiply operations instead of increasingly complicated matrix inversions to update growing orthogonal projection operator matrices. Furthermore, the updated orthogonal projection operator matrix is replaced by a normalized vector to perform the inner-product operations with each pixel for finding a target per iteration. With these two major optimizations, the computational complexity of ATGP-OSP is substantially reduced. What is more, an FPGA-based implementation of the proposed Fast-ATGP using high-level synthesis (HLS) is developed. Specifically, an efficient architecture containing a bunch of pipelines being executed in parallel is further designed and evaluated on a Xilinx XC7VX690T FPGA. The experimental results demonstrate that our proposed FPGA-based Fast-ATGP is able to automatically detect multiple targets on a commonly used dataset (AVIRIS Cuprite Data) at a high-speed rate of 200 MHz with a significant speedup of nearly 34.3 times that of ATGP-OSP, while retaining nearly the same high detection accuracy.

Research on automatic target detection and recognition based on deep learning

With the development of computer technology, the related achievements of image processing have been applied. Among them, the results of automatic target detection and recognition are widely used in the fields of reconnaissance, early warning and traffic control with the application of UAV. But now, the research of automatic target detection and tracking is becoming smaller and smaller. The original automatic target detection and recognition algorithm seems to be inadequate. The bottleneck of low-level feature design and optimization makes the accuracy and efficiency of automatic target detection inefficient. Therefore, based on in-depth learning, this paper establishes a method to automatically learn effective image features from images to achieve automatic target detection. Through the simulation of target detection in VEDAI database. The results show that the recognition rate of the proposed model is more than 95%. The results show that the proposed method can realize the automatic detection and recognition of targets very well.

Automatic Detection of Ship Targets Based on Wavelet Transform for HF Surface Wavelet Radar

High-frequency surface wave radar (HFSWR) has a vital civilian and military significance for continuous maritime surveillance of activities within exclusive economic zone. However, HFSWR has lower spatial and temporal resolutions and the received signals are strongly polluted by different clutter and background noise. Therefore, ship target detection by HFSWR has become a challenging task. This letter presents an automatic ship target detection algorithm based on discrete wavelet transform (DWT). First, a peak signal-to-noise ratio-based algorithm is proposed to automatically determine the optimal scale of DWT for extraction of ship targets. Second, the high-frequency coefficients of DWT at the optimal scale are processed by a fuzzy set-based method to enhance the useful target information and depress the unwanted background noises. Third, a target-highlighted image is reconstructed by ignoring all the low-frequency coefficients and performing inverse DWT only to the enhanced high-frequency coefficients. Finally, the targets are extracted by adaptive threshold segmentation of the final target-highlighted image. Experimental results show that the proposed approach can automatically extract ship targets effectively for range Doppler images with complex background, and has a better target detection performance than the previous wavelet-based algorithm, thereby providing a new reliable image processing-based method of ship target detection for HFSWR.

An Automatic Target Detection Algorithm for Swath Sonar Backscatter Imagery, Using Image Texture and Independent Component Analysis

In the present paper, a methodological scheme, bringing together common Acoustic Seabed Classification (ASC) systems and a powerful data decomposition approach, called Independent Component Analysis (ICA), is demonstrated regarding its suitability for detecting small targets in Side Scan Sonar imagery. Traditional ASC systems extract numerous texture descriptors, leading to a large feature vector, the dimensionality of which is reduced by means of data decomposition techniques, usually Principal Component Analysis (PCA), prior to classification. However, in the target detection issue, data decomposition should point towards finding components that represent sub-ordinary image information (i.e., small targets) rather than a dominant one. ICA has long been proved to be suitable for separating targets from a background, and this study represents a novel exhibition of its applicability to Side Scan Sonar (SSS) images. The present study attempts to build a fully automated target detection approach that combines image based feature extraction, ICA, and unsupervised classification. The suitability of the proposed approach has been demonstrated using an SSS data-set containing more than 70 manmade targets, most of them metallic, validated through a marine magnetic survey or ground truthing inspection. The method exhibited very good performance as it was able to detect more than 77% of the targets and it produced less than seven false alarms per km2. Moreover, it was compared to cases where, in the exact same methodological scheme, no decomposition technique is used, or PCA is employed instead of ICA, achieving the highest detection rate, but, more importantly, producing more than six times less false alarms, thus proving that ICA successfully manages to maximize target to background separation.

Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map.

This paper proposes a new automatic and adaptive aircraft target detection algorithm in high-resolution synthetic aperture radar (SAR) images of airport. The proposed method is based on gradient textural saliency map under the contextual cues of apron area. Firstly, the candidate regions with the possible existence of airport are detected from the apron area. Secondly, directional local gradient distribution detector is used to obtain a gradient textural saliency map in the favor of the candidate regions. In addition, the final targets will be detected by segmenting the saliency map using CFAR-type algorithm. The real high-resolution airborne SAR image data is used to verify the proposed algorithm. The results demonstrate that this algorithm can detect aircraft targets quickly and accurately, and decrease the false alarm rate.

Automatic target recognition of synthetic aperture radar (SAR) images based on optimal selection of Zernike moments features

In the present study, a new algorithm for automatic target detection (ATR) in synthetic aperture radar (SAR) images has been proposed. First, moving and stationary target acquisition and recognition image chips have been segmented and then passed to a number of preprocessing stages such as histogram equalisation, position and size normalisation. Second, the feature extraction based on Zernike moments (ZMs) having linear transformation invariance properties and robustness in the presence of the noise has been introduced for the first time. Third, a genetic algorithm‐based feature selection and a support vector machine classifier have been presented to select the optimal feature subset of ZMs for decreasing the computational complexity. Experimental results demonstrate the efficiency of the proposed approach in target recognition of SAR imagery. The authors obtained results show that just a small amount of ZMs features is sufficient to achieve the recognition rates that rival other established methods, and so ZMs features can be regarded as a powerful discriminatory feature for automatic target recognition applications relevant to SAR imagery. Furthermore, it can be observed that the classifier performs fairly well until the signal‐to‐noise ratio falls beneath 5 dB for noisy images.

Detection and characterization of small vehicles using RADARSAT-2 Spotlight mode data

During the winter of 2009–2010, Defence R&D Canada – Ottawa collected time series data to characterize the Spotlight mode on RADARSAT-2 for change detection in urban vehicle compounds, specifically for small vehicles during wintertime conditions. Corresponding to each image acquisition, three trihedral corner reflectors and five full-sized pick-up trucks were deployed with known locations and orientations. This paper describes the collection, processing, and analysis of two time series of RADARSAT-2 Spotlight mode images acquired over the Shirleys Bay Campus (west Ottawa) between December 2009 and April 2010. The first time series consists of six Spotlight A (SLA) 19 mode ascending orbit images acquired on consecutive 24-day repeat cycle passes. The second time series consists of six SLA17 mode descending orbit images, also acquired on consecutive 24-day repeat cycle passes. The 24-day separation of the images ensured consistent geometry and allowed for precise (sub-pixel) co-registration of the individual images using interferometric processing. This paper describes the measurement of radiometric properties, including the radar cross section and the peak-to-clutter ratio, for the known targets and it describes the use of an automatic target detection algorithm and interactive target validation tools to assist with site monitoring. It was found that precise (sub-pixel) orthorectification and co-registration of the imagery is essential for use of a vector mask to constrain the automatic target detection analysis. A single set of detection parameters could be applied to all images (both ascending and descending); tuning of parameters for specific images was not required. Most target vehicles (pick-up trucks) were detected and there were few false alarms. For the 12 images and the selected detection parameters, the average probability of detection was greater than 98%, whereas the average False Alarm Rate was on the order of 10–4 per pixel (i.e., one false detection per 104 pixels). Identification of small vehicles was not possible via visual inspection; neither length nor orientation could be correctly estimated. However, the detection of targets with RADARSAT-2 Spotlight mode data can contribute to site monitoring.

GPU Implementation of an Automatic Target Detection and Classification Algorithm for Hyperspectral Image Analysis

The detection of (moving or static) targets in remotely sensed hyperspectral images often requires real-time responses for swift decisions that depend upon high computing performance of algorithm analysis. The automatic target detection and classification algorithm (ATDCA) has been widely used for this purpose. In this letter, we develop several optimizations for accelerating the computational performance of ATDCA. The first one focuses on the use of the Gram-Schmidt orthogonalization method instead of the orthogonal projection process adopted by the classic algorithm. The second one is focused on the development of a new implementation of the algorithm on commodity graphics processing units (GPUs). The proposed GPU implementation properly exploits the GPU architecture at low level, including shared memory, and provides coalesced accesses to memory that lead to very significant speedup factors, thus taking full advantage of the computational power of GPUs. The GPU implementation is specifically tailored to hyperspectral imagery and the special characteristics of this kind of data, achieving real-time performance of ATDCA for the first time in the literature. The proposed optimizations are evaluated not only in terms of target detection accuracy but also in terms of computational performance using two different GPU architectures by NVIDIA: Tesla C1060 and GeForce GTX 580, taking advantage of the performance of operations in single-precision floating point. Experiments are conducted using hyperspectral data sets collected by three different hyperspectral imaging instruments. These results reveal considerable acceleration factors while retaining the same target detection accuracy for the algorithm.

Sparse Representation for Target Detection in Hyperspectral Imagery

In this paper, we propose a new sparsity-based algorithm for automatic target detection in hyperspectral imagery (HSI). This algorithm is based on the concept that a pixel in HSI lies in a low-dimensional subspace and thus can be represented as a sparse linear combination of the training samples. The sparse representation (a sparse vector corresponding to the linear combination of a few selected training samples) of a test sample can be recovered by solving an l0-norm minimization problem. With the recent development of the compressed sensing theory, such minimization problem can be recast as a standard linear programming problem or efficiently approximated by greedy pursuit algorithms. Once the sparse vector is obtained, the class of the test sample can be determined by the characteristics of the sparse vector on reconstruction. In addition to the constraints on sparsity and reconstruction accuracy, we also exploit the fact that in HSI the neighboring pixels have a similar spectral characteristic (smoothness). In our proposed algorithm, a smoothness constraint is also imposed by forcing the vector Laplacian at each reconstructed pixel to be minimum all the time within the minimization process. The proposed sparsity-based algorithm is applied to several hyperspectral imagery to detect targets of interest. Simulation results show that our algorithm outperforms the classical hyperspectral target detection algorithms, such as the popular spectral matched filters, matched subspace detectors, adaptive subspace detectors, as well as binary classifiers such as support vector machines.

Target Detection and Tracking Based on Ship Borne Infrared Imagery

A novel automatic target detection and tracking algorithm for tracking targets based on ship borne infrared imagery is proposed in this paper. The algorithm has two modes: detection and tracking. In detection mode, the proposed algorithm utilized difference between the consecutive frames and top-hat filter to determine the target position. In tracking mode, the Intensity Variation Function (IVF) is utilized. Before tracking and location the new position, the algorithm should determine the IVF whether reliable. If unreliable, the mode turns to detection. Experimental results by using real-life infrared image sequence are shown to validate the robustness of the proposed technique.

Adaptive Threshold Estimation via Extreme Value Theory

Determining a detection threshold to automatically maintain a low false alarm rate is a challenging problem. In a number of different applications, the underlying parametric assumptions of most automatic target detection algorithms are invalid. Therefore, thresholds derived using these incorrect distribution assumptions do not produce desirable results when applied to real sensor data. Monte Carlo methods for threshold determination work well but tend to perform poorly when targets are present. In order to mitigate these effects, we propose an algorithm using extreme value theory through the use of the generalized Pareto distribution (GPD) and a Kolmogorov-Smirnov statistical test. Unlike previous work based on GPD estimates, this algorithm incorporates a way to adaptively maintain low false alarm rates in the presence of targets. Both synthetic and real-world detection results demonstrate the usefulness of this algorithm.

Region-of-interest-based algorithm for automatic target detection in infrared images

We present a region-of-interest-based segmentation (ROI-S) algorithm and apply it for automatic target detection. The proposed algorithm requires no templates or a priori knowledge of the targets. An automatic ROI extraction approach based on localized texture and statistical features is used to locate targets in an IR scene without any prior knowledge of their type, exact size, and orientation. Two locally adaptive histogram-based segmentation techniques are applied to extract the target signature. The Bayes decision rule is applied for a bimodal histogram while entropic correlation is used for all other cases. Geometric and statistical features are automatically extracted for each suspected ROI. We suggest a unique variance-based metric for discriminating targets from clutter and for evaluating the probability of correct detection. The proposed system is successfully tested on several hundred single-frame IR images that contain multiple examples of military vehicles, with various sizes and brightness levels and in various background scenes and orientations. A high probability of correct detection (greater than 90%) with a low false alarm rate is achieved.

Automatic target detection using binary template matching

This paper presents a new automatic target detection (ATD) algorithm to detect targets such as battle tanks and armored personal carriers in ground-to-ground scenarios. Whereas most ATD algorithms were developed for forward-looking infrared (FLIR) images, we have developed an ATD algorithm for charge-coupled device (CCD) images, which have superior quality to FLIR images in daylight. The proposed algorithm uses fast binary template matching with an adaptive binarization, which is robust to various light conditions in CCD images and saves computation time. Experimental results show that the proposed method has good detection performance.

Multiobject detection of targets with fine details, scale and translation variations

We propose an enabling algorithm for automatic target detection wherein the targets are almost similar but differ in fine details. Multiple translated and scaled target images are processed in the Mellin transform domain and subsequently detected using a learning vector quantization (LVQ) neural network.

Application of a passive polarimetric infrared sensor in improved detection and classification of targets

Automatic detection and recognition of targets by means of passive IR sensors suffer from limitations due to lack of sufficient contrast between the targets and their background, and among the facets of a target. In this paper the results of a suite of polarization-sensitive automatic target detection and recognition algorithms on sets of simulated and real polarimetric IR imagery are presented. A custom designed Polarimetric IR (PIR) imaging sensor is used for collecting real polarimetric target data-three of the four Stokes parameters under a variety of conditions. Then a set of novel algorithms are designed and tested that uses the target and background Stokes parameters for detection, segmentation and classification of targets. The empirical performance results are obtained in terms of the probabilities of detection, false alarm rate, segmentation accuracy, and recognition probabilities as functions of number of pixels on target, aspect and depression angles and under several background conditions (clutter densities) on the polarimetric and non-polarimetrirc data. These results show that a noticeable improvement over the non-polarimetric ATR can be achieved.

Detecting anisotropic scattering with hidden Markov models

The authors propose that hidden Markov models (HMMs) and multiaperture synthetic aperture radar (MASAR) imaging can be used to construct automatic target detection algorithms. Their preliminary studies show that HMM MASAR detection: effectively exploits anisotropic scattering differences between target and multiple clutter classes; is computationally efficient; can be used with either single polarisation or multipolarisation SAR imagery; and can be used with either coherent or noncoherent subapertures. Further, the results indicate that the accuracy of HMM MASAR detection is comparable to other techniques while requiring orders of magnitude less computation. These results suggest that the HMM MASAR detection technique could be effectively deployed in fielded automatic target recognition systems.