Target Detection Applications Research Articles

Parameters affecting target detection in VNIR and SWIR range

Hyperspectral data due to its fine spectral resolution facilitates detection of targets with high accuracies and thus has been used in many applications. A target detection application differs from most other remote sensing applications in the fact that targets are often too small and contained in only a few pixels. However, HSI detection gets limited by several parameters which can be broadly categorized in three groups: sensor parameters (noise, calibration etc.), spatial parameters (size, shape etc.), scene parameters (illumination variation, background etc.). These parameters may have varying effect on detectability of targets and thus need further investigations. Two specific spectral ranges VNIR and SWIR have been selected based on literature review to study effect of four scene parameters namely illumination, background, material composition and color of target on detection of known targets. The detection has been implemented using three popular detection algorithms namely, ACE, MF, SAM. Results indicate that scene parameters play a major role and affect detectability of targets differently in different spectral ranges.

Integrated amplified aptasensor with in-situ precise preparation of copper nanoclusters for ultrasensitive electrochemical detection of microRNA 21

MicroRNA 21 (MIR21) has garnered much attention in recent years as an important disease biomarker. The detection of it in human system shows great significance for the healthy evaluation and major diseases early detection. Herein, a novel approach tactfully manipulates the in-situ precise preparation of copper nanoclusters on overlapping Y-shaped ds-DNA for MIR21 analysis were developed in the proposed integrated aptasensor. In the presence of target MIR21, overlapping Y-shaped ds-DNA was constructed on electrode. Copper nanoclusters were in-situ prepared on this effective template for target detection. Taking advantage of exonuclease T7 triggered targets recycling, hybridization chain reaction (HCR) and copper nanoclusters triple amplification strategy, linear detection of MIR21 was achieved from 10pM to 0.1fM with a detection limit down to 10aM (S/N > 3). This approach provides a good model for integrating both synthesis and detection into one electrochemistry component. It showed promising potential for applications in aptamer related target detection in human serum analysis.

Covariance-based band selection and its application to near-real-time hyperspectral target detection

The matched filter (MF) and adaptive coherence estimator (ACE) show great effectiveness in hyperspectral target detection applications. Practical applications in which on-board processing is generally required demand real-time or near-real-time implementation of these detectors. However, a vast amount of hyperspectral data may make real-time or near-real-time implementation of the detection algorithms almost impossible. Band selection can be one of the solutions to this problem by reducing the number of spectral bands. We propose a new band selection method that prioritizes spectral bands based on their influence on the detection performance of the MF and ACE and discards the least influential bands. We validate the performance of our method using real hyperspectral images. We also demonstrate our technique on near-real-time detection tasks and show it to be a feasible approach to the tasks.

Performance comparison of some censoring CA-based CFAR processors in heterogeneous environments

Performance comparison of automatic censoring CA-based CFAR processors contribute to the development of more efficient censoring detectors. In this paper, the authors analyze the performance of the detection schemes which named: ACCA-odv- (Automatic Censored Cell Averaging -ordered data variability-), ADCCA- (Automatic Dual Censoring Cell Averaging-), ACGCA- (Automatic Censoring Greatest Cell Averaging-), and GGDC- (Goodness-of-fit Generalized likelihood test with Dual Censoring-)-CFAR's in heterogeneous environments. The assumed environments are represented by three situations: first, the homogeneous situation, second, the presence of interfering targets, and the third case is allowed to the presence of clutter edges. The obtained results, under the assumption of a Gaussian clutter and a mono pulse processing, show that most of the studied detectors perform well in a specific conditions and there is a need to further developments to ensure the required performances for recent target detection application.

Fractal detector design and application in maritime target detection

Fractal detector design and application in maritime target detection

Target Localization Using Microwave Time-Reversal Mirror in Reflection Mode

Time-reversal (TR) focusing is based on the fact that when a wave solution is reversed in time and back propagated, it focuses back at the source. TR techniques using microwave measurements have proved to be promising for radar detection and defect imaging in nondestructive evaluation of dielectric samples. This paper presents a TR technique for target detection and localization applications using far-field microwave measurements in reflection mode. Simulation results investigate the feasibility and robustness of this approach and determine the limits of this technique. Experimental results based on reflection mode measurements validate the approach for the detection of source and closely spaced multiple dielectric targets.

Performance Evaluation of Leader–Follower-Based Mobile Molecular Communication Networks for Target Detection Applications

This paper proposes a leader–follower-based model of mobile molecular communication networks for target detection applications. The proposed model divides the application functionalities of molecular communication networks into two types of mobile bio-nanomachine: leader and follower bio-nanomachines. Leader bio-nanomachines distribute in the environment to detect a target and create an attractant gradient around the target. Follower bio-nanomachines move according to the attractant gradient established by leader bio-nanomachines; they approach the target and perform necessary functionalities, such as releasing drug molecules. This paper develops mathematical expressions for the proposed model, describes wet laboratory experiments designed to estimate model parameters, and performs biologically realistic computer simulation experiments to evaluate the performance of the proposed model. The main contributions of this paper are to demonstrate the functional division of molecular communication networks, which will facilitate the design and development of molecular communication networks. Furthermore, insight into the application-level performance of molecular communication networks will be provided based on the proposed model.

基于深度学习的目标跟踪方法研究现状与展望

The inverse synthetic aperture lidar (ISAL) have attracted increasing attention for its merits including small visual tracking which is considered as one of the important research topics in the field of computer vision due to its key role in versatile applications, such as precision guidance, intelligent video surveillance, human-computer interaction, robot navigation and public safety. The basic idea for implementing visual tracking is composed of finding the target object in a video or sequence of images, then determining its exact position in the next successive frames and finally generating the corresponding trajectory of this object. Visual tracking, however, is still a challenging problem in practice while taking into account the abrupt appearance changes of the target objects induced by their non-rigid transformation, the sophisticated lighting variation, the obstruction by the block or similar objects in the background and the camera jitter. Motivated by the successful applications in target detection and recognition in recent years, plenty of deep learning models have been integrated in the visual tracking and better performance over traditional methods was achieved in a series of data evaluations, which opens a new door in the field of visual tracking. In this paper, the overview and progress on visual tracking were summarized. The current challenges and corresponding solving approaches in this field are introduced firstly and in particular, several novel and mainstream visual tracking algorithms based on the deep learning are specially described and analyzed in details, including their basic ideas, advantages and disadvantages and future prospect.

RF Sensing Based Target Detector for Smart Sensing Within Internet of Things in Harsh Sensing Environments

In this paper, we explore surveillance and target detection applications of Internet of Things (IoT) with radio detection as the primary means of sensing. The problem of surveillance and target detection has found its place in numerous civilian and military applications, and IoT is well suited to address this problem. Radio frequency (RF) sensing techniques are the next generation technologies, which offer distinct advantages over traditional means of sensing used for surveillance and target detection applications of IoT. However, RF sensing techniques have yet to be widely researched due to lack of transmission and computational resources within IoT. Recent advancements in sensing, computing, and communication technologies have made radio detection enabled sensing techniques available to IoT. However, extensive research is yet to be done in developing reliable and energy efficient target detection algorithms for resource constrained IoT. In this paper, we have proposed a multi-sensor RF sensing-based target detection architecture for IoT. The proposed target detection architecture is adaptable to interference, which is caused due to the co-existence of sensor nodes within IoT and adopts smart sensing strategies to reliably detect the presence of the targets. A waveform selection criterion has been proposed to identify the optimum choice of transmit waveforms within a given set of sensing conditions to optimize the target detection reliability and power consumption within the IoT. A dual-stage target detection strategy has been proposed to reduce the computational burden and increase the lifetime of the sensor nodes.

A Review on Hot-IP Finding Methods and Its Application in Early DDoS Target Detection

On the high-speed connections of the Internet or computer networks, the IP (Internet Protocol) packet traffic passing through the network is extremely high, and that makes it difficult for network monitoring and attack detection applications. This paper reviews methods to find the high-occurrence-frequency elements in the data stream and applies the most efficient methods to find Hot-IPs that are high-frequency IP addresses of IP packets passing through the network. Fast finding of Hot-IPs in the IP packet stream can be effectively used in early detection of DDoS (Distributed Denial of Service) attack targets and spreading sources of network worms. Research results show that the Count-Min method gives the best overall performance for Hot-IP detection thanks to its low computational complexity, low space requirement and fast processing speed. We also propose an early detection model of DDoS attack targets based on Hot-IP finding, which can be deployed on the target network routers.

Spatial correlation of the high intensity zone in deep-water acoustic field**Project supported by the National Natural Science Foundation of China (Grant Nos. 11434012 and 41561144006).

The spatial correlations of acoustic field have important implications for underwater target detection and other applications in deep water. In this paper, the spatial correlations of the high intensity zone in the deep-water acoustic field are investigated by using the experimental data obtained in the South China Sea. The experimental results show that the structures of the spatial correlation coefficient at different ranges and depths are similar to the transmission loss structure in deep water. The main reason for this phenomenon is analyzed by combining the normal mode theory with the ray theory. It is shown that the received signals in the high intensity zone mainly include one or two main pulses which are contributed by the interference of a group of waterborne modes with similar phases. The horizontal-longitudinal correlations at the same receiver depth but in different high intensity zones are analyzed. At some positions, more pulses are received in the arrival structure of the signal due to bottom reflection and the horizontal-longitudinal correlation coefficient decreases accordingly. The multi-path arrival structure of receiving signal becomes more complex with increasing receiver depth.

Polarization-based material classification technique using passive millimeter-wave polarimetric imagery.

The polarization properties of thermal millimeter-wave emission capture inherent information of objects, e.g., material composition, shape, and surface features. In this paper, a polarization-based material-classification technique using passive millimeter-wave polarimetric imagery is presented. Linear polarization ratio (LPR) is created to be a new feature discriminator that is sensitive to material type and to remove the reflected ambient radiation effect. The LPR characteristics of several common natural and artificial materials are investigated by theoretical and experimental analysis. Based on a priori information about LPR characteristics, the optimal range of incident angle and the classification criterion are discussed. Simulation and measurement results indicate that the presented classification technique is effective for distinguishing between metals and dielectrics. This technique suggests possible applications for outdoor metal target detection in open scenes.

FPGA Implementation of an Algorithm for Automatically Detecting Targets in Remotely Sensed Hyperspectral Images

Timely detection of targets continues to be a relevant challenge for hyperspectral remote sensing capability. The automatic target-generation process using an orthogonal projection operator (ATGP-OSP) has been widely used for this purpose. Hyperspectral target-detection applications require timely responses for swift decisions, which depend upon (near) real-time performance of algorithm analysis. Reconfigurable field-programmable gate arrays (FPGAs) are promising platforms that allow hardware/software codesign and the potential to provide powerful onboard computing capabilities and flexibility at the same time. In this paper, we present an FPGA implementation for the ATGP-OSP algorithm. Our system includes a direct memory access module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-7 XC7VX690T FPGA and tested using real hyperspectral data collected by NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite mining district in Nevada and the World Trade Center in New York. Experimental results demonstrate that our hardware version of the ATGP-OSP algorithm can significantly outperform a software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing.

Correlation analysis of X-band sea clutter in complex domain

The correlation analysis of sea clutter data in a complex domain is conducted in this study. Specific to X-band sea clutter, the statistical characteristics of the complex correlation, particularly the phase characteristics which are closely related to the phase difference of the sea clutter and the Doppler properties, are analyzed in detail based on the experimental data, recorded by the McMaster University IPIX radar in 1993. That the phase term of the complex correlation presents linear change means that there exists the linearity of phase differences between different time intervals in the X-band sea clutter. This investigation explores the regularities about the effect of wind on the complex correlation with similar patterns for different polarization modes. The regularities indicate that the wind direction can be inferred from the distribution pattern of the complex correlation. Moreover, a model describing the relationships between the statistics of the complex correlation and wind parameters is proposed. The application for target detection based on the differences of characteristics of complex correlations between the sea clutter and the target are also investigated and the proposed features have been confirmed. The principle of the method is fundamental for broader future applications.

A comparative study of target detection algorithms in hyperspectral imagery applied to agricultural crops in Colombia

Contexto: Las imágenes hiperespectrales (HSI) contienen información en alta resolución espectral, en cientos de bandas contiguas sobre un rango del espectro electromagnético. Se ha tomado ventaja de la información espectral mediante algoritmos de clasificación, detección de cambios, de anomalías o detección de objetivos. Específicamente, en las dos últimas décadas han sido propuestos diferentes algoritmos para detectar objetivos en HSI. Sin embargo, encontrar un algoritmo de detección con un desempeño óptimo para diferentes escenarios y objetivos, aún es materia de investigación, debido a la alta variabilidad espectral y diversidad de escenarios del mundo real.Objetivo: La presente investigación realiza un estudio comparativo de algoritmos de detección de objetivos en imágenes hiperespectrales aplicados a la agricultura colombiana.Método: Las evaluaciones se realizaron sobre 20 HSI reales adquiridas por el sensor satelital Hyperion y 6 HSI sintéticas con diferentes niveles de ruido. En el desarrollo de la investigación se implantaron 5 objetivos sintéticos; y se extrajeron más de 115 firmas espectrales reales, 11 de ellas fueron usadas como objetivo en la evaluación de los algoritmos, permitiendo la caracterización de 5 cultivos agrícolas del nororiente colombiano en 5 áreas de estudio diferentes. Los resultados muestran que el algoritmo de Estimación de Coherencia Adaptativo (ACE) presenta un mejor desempeño con probabilidades de detección PD > 90% para diferentes escenarios y objetivos de tipo agrícola, tanto en imágenes sintéticas como reales.Conclusiones: En aplicaciones de detección de objetivos en HSI, es crítico encontrar un algoritmo que presente un rendimiento óptimo para diferentes escenarios y objetivos, debido a la variabilidad espectral generada por las diferentes condiciones geográficas de Colombia. Por otra parte, este trabajo permite mostrar que es posible realizar nuevas investigaciones y aplicaciones a nivel nacional tomando ventaja de las técnicas de imágenes hiperespectrales y algoritmos de detección espectral; específicamente en el sector de la agricultura colombiana.

Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga–Koontz Transform

Principal component analysis (PCA) is a popular technique in remote sensing for dimensionality reduction. While PCA is suitable for data compression, it is not necessarily an optimal technique for feature extraction, particularly when the features are exploited in supervised learning applications (Cheriyadat and Bruce, 2003) [1]. Preserving features belonging to the target is very crucial to the performance of target detection/recognition techniques. Fukunaga–Koontz Transform (FKT) based supervised band reduction technique can be used to provide this requirement. FKT achieves feature selection by transforming into a new space in where feature classes have complimentary eigenvectors. Analysis of these eigenvectors under two classes, target and background clutter, can be utilized for target oriented band reduction since each basis functions best represent target class while carrying least information of the background class. By selecting few eigenvectors which are the most relevant to the target class, dimension of hyperspectral data can be reduced and thus, it presents significant advantages for near real time target detection applications. The nonlinear properties of the data can be extracted by kernel approach which provides better target features. Thus, we propose constructing kernel FKT (KFKT) to present target oriented band reduction. The performance of the proposed KFKT based target oriented dimensionality reduction algorithm has been tested employing two real-world hyperspectral data and results have been reported consequently.

Extracting Target Detection Knowledge Based on Spatiotemporal Information in Wireless Sensor Networks

Wireless sensor networks (WSNs) have been deployed for many applications of target detection, such as intrusion detection and wildlife protection. In these applications, the first step is to detect whether the target is present or not. However, most of the existing work uses the “simple disk model” as signal model, which may not capture the sensing environment. In this work, we utilize a more realistic signal model to describe sensing process of sensors. On the other hand, the “majority rule” is widely used to make the final decision, which may not obtain the true judgment. To this end, we utilize a more realistic signal model and also use a probabilistic decision model to make the final decision. Moreover, we propose a probabilistic detection algorithm in which all sensors' local measurement values are fully used. This algorithm does not need any artificial threshold compared with traditional algorithms. It makes the most of spatiotemporal information to obtain the final decision. For the spatial perspective, sensors are distributed in different locations cooperating with each other. Meanwhile, for the temporal perspective, multiround subdecisions are fused. The effectiveness of the proposed method is validated by extensive simulation results, which show high detection probabilities and low false alarm probabilities.

Top-of-Atmosphere Image Simulation in the 4.3- <inline-formula> <tex-math notation="LaTeX">$\mu\mbox{m}$</tex-math></inline-formula> Mid-infrared Absorption Bands

Mid-infrared atmospheric absorption bands centered at 4.3 $\mu\mbox{m}$ are applied in target detection. Image simulation, as an important tool for the adaption and optimization of a sensor, ought to be conducted for the development of instruments using this spectral range. In this paper, a top-of-atmosphere (TOA) image simulation method is proposed, and this method is tested on two bands of the sensor SPIRIT III (band S1: 4.21–4.37 $\mu\mbox{m}$ , S2: 4.23–4.47 $\mu\mbox{m}$ ). Band translation models are established for the generation of surface emissivity images, and an analytic radiative transfer model is modified and utilized to simulate TOA radiance fast and accurately. Accuracy analysis of the proposed method shows relative errors of within $\pm$ 6% and $\pm$ 1% in simulated surface emissivity and TOA radiance, respectively. Moreover, image simulation is often used for band selection in the sensor design stage. To illustrate how our proposed simulation method was applied in band selection, we used simulated TOA radiance of bands S1 and S2 as an example and compared their possibility of false alarms caused by high-temperature objects. Experimental results show that high-temperature objects are more unlikely to become false alarms on band-S1 images. Therefore, the spectral range of S1 is a better option for target detection application than S2. This TOA simulation method can be also applied in band selection among other 4.3- $\mu\mbox{m}$ absorption bandwidths, as was done in this paper.

Signal Feature Extraction of GMI Sensor in Longitudinally Excitated Amorphous Wire and Its Application in Target Detection

In order to expand the dynamic range of the GMI sensor in longitudinally excitated amorphous wire and improve its precision, waveforms of the GMI sensor are analyzed on the background of weak magnetic field measurement. Then three features extraction methods are studied in detail. According to the advantages and disadvantages of different methods, an improved method which combines the energy features of the wavelet decomposition and the amplitude features is proposed. First, fit the amplitude change ratio curve respectively with Gaussian function and polynomial function, which not only solves the problem of nonlinearity, but also improves the measurement accuracy. Considering the difference of signals’ in-pulse features at different positions, the ‘db5’ wavelet is introduced to decompose the signals. Then the BP neural network trained by the energy features of the wavelet is used to locate the target’s approximate position, as a result, the problem of multi-value is solved. At last, experiments of target detection in weak magnetic field prove that the method proposed is effective.

Improving target discrimination ability of active polarization imagers by spectral broadening.

Active polarization imagers using liquid crystal variable retarders (LCVR) usually operate at one given wavelength for the sake of polarimetric accuracy. However, this often requires to use narrowband filters which reduces the amount of light entering the system and thus the signal-to-noise ratio. For applications where good target/background discriminability (contrast) is required rather than polarimetric accuracy, this may not be the best choice. In this Article, we address contrast optimization in the case of broadband active polarimetric imaging for target detection applications. Through numerical and experimental studies, we show that broadening the spectrum of the light entering the system can increase the contrast between two regions of a scene. Furthermore, we show that this contrast can be further increased by taking into account the spectral dependence of the scene and of the polarimetric properties of the imaging system in the optimization of the measurement procedure.