Point Target Detection Research Articles

DMD-based image-free system for real-time detection and positioning of point targets

The detection and positioning system of point targets has critical applications in many fields. However, its spatial and temporal resolution is limited for the image-based system due to a large amount of data. In this work, an image-free system with less data and high update rate is proposed for the detection and positioning of point targets. The system uses a digital micromirror device (DMD) for light modulation and a pixel array as the light intensity detector, and the DMD is divided into multiple blocks to selectively acquire the intensity information in the region of interest. The centroid position of a point target is calculated from the intensity on the adjacent rows or columns of the micromirror. Simulation indicates that the performance of the proposed method is close to or better than that of the traditional methods. In static experiments, the centroiding accuracy of the proposed system is about 0.013 pixel. In dynamic experiments, the centroiding accuracy is better than 0.07 pixel in the condition of signal-to-noise ratio (SNR) greater than 35.2 dB. Meanwhile, the built system has an update rate of 1 kHz in the range of 1024×768 pixels, and the method acquires only 8 bytes of data for one-time positioning of a point target, making it applicable to real-time detection and positioning of point targets.

Infrared moving dim point target detection based on spatial-temporal local contrast

Aiming at the detection of dim and small moving point targets in space-based infrared (IR) imaging systems, a detection method based on spatial-temporal local contrast (STLC) is presented. Firstly, use the spatial and temporal information to estimate the background gray value of each pixel, and obtain two prediction results respectively. Then, the spatial local contrast (SLC) and the temporal local contrast (TLC) are defined according to the original image and two estimated backgrounds. Next, the STLC is defined as the product of SLC and TLC. Finally, a threshold value is set in the STLC for target detection. Four groups of experiments are conducted, and the simulation results indicate that the proposed method has a great advantage in terms of background suppression factor (BSF) and gain of signal-to-noise (GSNR).

Point cloud target detection and tracking algorithm based on K-means and Kalman

This paper is mainly about point cloud image and data processing, virtualization and visualization. An improved K-means iterative clustering algorithm is proposed. In addition, cloth simulation filtering algorithm is used to extract road surface information. On this basis, bilateral filtering and statistic filtering are used to smooth image denoising. Kalman filter is used to track and predict point cloud targets. Using the variance of innovation sequence, the noise variance and observation noise variance can be estimated and corrected gradually in the process of system calculation. The error is relatively small, and the accuracy and reliability are improved. For disordered, occluded, sparsity and noisy, complex terrain image, how to improve the denoising effect, the accuracy and effectiveness of target extraction and tracking is the focus of this paper.

Improved Fisher MAP Filter for Despeckling of High-Resolution SAR Images Based on Structural Information Detection

Fisher distribution is a popular model for high-resolution (HR) synthetic aperture radar (SAR) images due to its high-peaked and heavy-tailed characteristics as well as its theoretical justification and mathematical tractability. Based on the Fisher modeling of SAR images, the maximum a posteriori (MAP) filter is suggested. In the Fisher model, the parameter of image looks is thought to be fixed to correspond to the formation mechanism of multi-look intensity images, and the other two parameters are accur ately assessed from the SAR image based on second-kind statistics. To improve the Fisher MAP filter especially in the aspect of speckle suppression, the Fisher MAP filter based on recognition of structural information is created using point target detection, the adaptive windowing method, homogeneous region detection, and selection of most homogeneous sub-window. The experiments on despeckling of HR SAR images demonstrate that the improved Fisher MAP filter based on structural information detection can suppress speckle in homogenous and edge regions, and effectively preserve fine details, edges, and point targets.

PCA-Based Matrix CFAR Detection for Radar Target.

In radar target detection, constant false alarm rate (CFAR), which stands for the adaptive threshold adjustment with variation of clutter to maintain the constant probability of false alarm during the detection, plays an important role. Matrix CFAR detection performed on the manifold of Hermitian positive-definite (HPD) covariance matrices is an efficient detection method that is based on information geometry. However, the HPD covariance matrix, which is constructed by a small bunch of pulses, describes the correlations among received data and suffers from severe information redundancy that limits the improvement of detection performance. This paper proposes a Principal Component Analysis (PCA) based matrix CFAR detection method for dealing with the point target detection problems in clutter. The proposed method can not only reduce dimensionality of HPD covariance matrix, but also reduce the redundant information and enhance the distinguishability between target and clutter. We first apply PCA to the cell under test, and construct a transformation matrix to map higher-dimensional matrix space to a lower-dimensional matrix space. Subsequently, the corresponding detection statistics and detection decision on matrix manifold are derived. Meanwhile, the corresponding signal-to-clutter ratio (SCR) is improved. Finally, the simulation experiment and real sea clutter data experiment show that the proposed method can achieve a better detection performance.

A Median-Ratio Scene-Based Non-Uniformity Correction Method for Airborne Infrared Point Target Detection System.

Infrared detectors suffer from severe non-uniform noise which highly reduces image resolution and point target signal-to-noise ratio. This is the restriction for airborne point target detection systems in reaching the background limit. The existing methods are either not accurate enough, or too complex to be applied to engineering. To improve the precision and reduce the algorithm complexity of scene-based Non-Uniformity Correction (NUC) for an airborne point target detection system, a Median-Ratio Scene-based NUC (MRSBNUC) method is proposed. The method is based on the assumption that the median value of neighboring pixels is approximately constant. The NUC coefficients are calculated recursively by selecting the median ratio of adjacent pixels. Several experiments were designed and conducted. For both the clear sky scene and scene with clouds, the non-uniformity is effectively reduced. Furthermore, targets were detected in outfield experiments. For Target 1 48.36 km away and Target 2 50.53 km away, employing MRSBNUC the SNR of the target increased 2.09 and 1.73 times respectively compared to Two-Point NUC. It was concluded that the MRSBNUC method can reduce the non-uniformity of the detector effectively which leads to a longer detection distance and fewer false alarms of the airborne point target detection system.

Kirsch Direction Template Despeckling Algorithm of High-Resolution SAR Images-Based on Structural Information Detection

In order to overcome the drawback of the traditional Kirsch template despeckling usings fixed windows, an improved Kirsch direction template despeckling algorithm, based on structural information detection, is proposed for high-resolution synthetic aperture radar (SAR) images. First, the point targets are detected and preserved in the current region. Second, the window is enlarged adaptively based on the statistical characteristics of the local region. Finally, the window finally obtained is classified. The averaged filter is directly adopted if the region is homogeneous, or else the Kirsch template filter is used. Combining point target detection, adaptive windowing, and region classification, altogether the proposed algorithm can effectively improve the performance of the traditional Kirsch direction template despeckling. Despeckling experiments on simulated and real high-resolution SAR images demonstrate that the Kirsch direction template despeckling algorithm based on structural information detection can not only sufficiently suppress speckle in homogenous and edge regions, but also effectively preserve point targets and edge information, leading to good despeckling results.

Adaptive Morphological Contrast Enhancement Based on Quantum Genetic Algorithm for Point Target Detection

Robust point target detection of infrared clutter background has drawn great interest of scholars. Recently, morphological filter is playing a significant role in detecting infrared point target. Generally, the background clutter and targets are diverse in the case of each image. Traditional fixed structural elements and dimensions cannot be adjusted adaptively to acquire to successful point target detection in different complex backgrounds. Therefore, a new method is introduced based on quantum genetic algorithm to optimize and obtain structural element which is used as morphological filter for small target detection in original infrared images.Then,morphological contrast enhancement is further proposed to enhance energy of point targets after the filtered image is obtained.Thus, an enormous background clutter and noise are suppressed and the contrast between target and background are observably increased. Finally, by setting proper threshold, the point targets can be detected perfectly. Experimental evaluation results show that the proposed adaptive morphological contrast enhancement based on quantum genetic algorithm is effective and robust with respect to detection accuracy compared with the traditional morphological filter and other filtering algorithms.

An Improved CA-CFAR Method for Ship Target Detection in Strong Clutter Using UHF Radar

In this letter, the application of ultra-high frequency (UHF) radar for ship target detection over river is investigated for the first time. Due to the wide beam width of antenna, the ship detection of the UHF radar suffers from the broaden river clutters scattering from water waves. In addition, due to the high resolution, the ship echoes are seriously extended in both range and Doppler dimensions, which is different from the traditional point target signal. Extended target detection in strong river clutter is applied in this letter. Conventional constant false alarm rate (CFAR) detector is limited in this situation, which is applicable to the point target detection in clutter free background. An improved cell-averaging (CA)-CFAR method is proposed based on a joint estimation of threshold in range, Doppler and time serials of range-Doppler (R-D) spectra. Based on the time stationarity of clutter, this three-dimensional (3D) CA-CFAR combines several R-D spectra collected in continuous time to estimate the clutter threshold. The effectiveness of this improved method is validated using both simulated and field data.

Infrared point target detection based on multiscale homogeneous feature fusion

In view of the difficult detection of infrared dim point target from complex backgrounds, a multiscale homogeneous feature (MHF) based multispectral image fusion detection method is proposed in this paper. Inspired by the local contrast measure (LCM), we extract two local statistical features from the perspective of the homogeneity of gray difference distribution to characterize local structure of the infrared point target. Based on these two local features, we obtain the MHF map that can effectively highlight the potential point targets and suppress the backgrounds simultaneously. For the pixel-size electronic noise (PSEN) and some similar local structures to the point target, the multispectral image fusion detection is a positive way to alleviate these interferences and promote the robustness of the dim point target detection. Experimental results on six real scenarios and synthetic scenarios demonstrate that the proposed method not only works more stably for different target sizes and brightness, but also can achieve superior detection performance compared with the state-of-art detection methods.

红外时间延迟积分点目标探测系统中速度失配影响研究

红外时间延迟积分点目标探测系统中速度失配影响研究

Infrared moving point target detection using a spatial-temporal filter

Infrared point target detection technology has widely applications, such as in tracking and surveillance system. In this paper, an moving point target detection method is proposed to detect targets from infrared sequence images. Firstly, a modified spatial-temporal total variation model is proposed for background prediction. Then the subtraction image is obtained by subtracting the predicted background from the corresponding sequence image. Finally, the target can be detected from the result that the product of the subtraction image and a temporal contrast filter. The effectiveness of the modified spatial-temporal filter on infrared moving point targets detection is verified by detecting targets from different infrared sequence images.

Spatial-temporal local contrast for moving point target detection in space-based infrared imaging system

Infrared (IR) moving point target detection is an important technique in many onboard applications such as remote sensing, IR searching and tracking (IRST) and early warning system. However, it has been facing great challenges due to the complicated background and the limited processing resources in the onboard system. In this paper, a novel spatial-temporal local contrast method is proposed for moving point target detection in space-based IR imaging system. Firstly, a simple but effective spatial filter based on multi-direction filtering fusion is designed to obtain the spatial local contrast map (SLCM). An enhanced time domain difference method is also proposed to obtain the temporal local contrast map (TLCM). Then, the spatial–temporal local contrast map (STLCM) is obtained by multiplying the newly defined SLCM and TLCM. Finally, an enhanced threshold segmentation method is proposed for target detection and false alarm suppression. To verify the performance of our detection algorithm, we conduct several groups of experiments on four different real IR image sequences with simulated targets. The final experimental results show that our algorithm significantly outperforms other methods in terms of background suppression and target detection.

An Omnidirectional Morphological Method for Aerial Point Target Detection Based on Infrared Dual-Band Model

Aerial infrared point target detection under nonstationary background clutter is a crucial yet challenging issue in the field of remote sensing. This paper presents a novel omnidirectional multiscale morphological method for aerial point target detection based on a dual-band model. Considering that the clutter noise conforms to the Gaussian distribution, the single-band detection model under the Neyman-Pearson (NP) criterion is established first, and then the optimal fused probability of detection under the dual-band model is deduced according to the And fusion rule. Next, the omnidirectional multiscale morphological Top-hat algorithm is proposed to extract all the possible targets distributing in every direction, and the local difference criterion is employed to eliminate the residual background edges further. The dynamic threshold-to-noise ratio (TNR) is adjusted to obtain the optimal probability of detection under the constant false alarm rate (CFAR) criterion. Finally, the dim point target is extracted after dual-band data correlation. The experimental result demonstrates that the proposed method achieves a high probability of detection and performs well with respect to suppressing complex background when compared with common algorithms. In addition, it also has the advantage of low complexity and easy implementation in real-time systems.

Infrared moving point target detection based on an anisotropic spatial-temporal fourth-order diffusion filter

Infrared moving point target detection is a challenging task in infrared tracking systems. In this paper, an anisotropic spatial-temporal fourth-order diffusion filter (ASTFDF) is proposed for background prediction from infrared frame images. Using it, we can detect infrared point targets by subtracting the predicted background from the original image. Experiments in detecting targets from several different infrared image frame sequences indicated that the proposed ASTFDF can predict the background effectively and that the target can be detected precisely while achieving a higher background suppression factor (BSF) and signal-to-clutter ratio gain (SCRG) compared with state-of-the-art methods.

STRUCTURAL INFORMATION DETECTION BASED FILTER FOR GF-3 SAR IMAGES

Abstract. GF-3 satellite with high resolution, large swath, multi-imaging mode, long service life and other characteristics, can achieve allweather and all day monitoring for global land and ocean. It has become the highest resolution satellite system in the world with the C-band multi-polarized synthetic aperture radar (SAR) satellite. However, due to the coherent imaging system, speckle appears in GF-3 SAR images, and it hinders the understanding and interpretation of images seriously. Therefore, the processing of SAR images has big challenges owing to the appearance of speckle. The high-resolution SAR images produced by the GF-3 satellite are rich in information and have obvious feature structures such as points, edges, lines and so on. The traditional filters such as Lee filter and Gamma MAP filter are not appropriate for the GF-3 SAR images since they ignore the structural information of images. In this paper, the structural information detection based filter is constructed, successively including the point target detection in the smallest window, the adaptive windowing method based on regional characteristics, and the most homogeneous sub-window selection. The despeckling experiments on GF-3 SAR images demonstrate that compared with the traditional filters, the proposed structural information detection based filter can well preserve the points, edges and lines as well as smooth the speckle more sufficiently.

Moving Point Target Detection Based on Higher Order Statistics in Very Low SNR

This letter presents an approach for the detection of moving point targets on high-frame-rate image sequences with low spatial resolution and low SNR based on higher order statistical theory. We propose a novel method for analyzing the time-domain evolution of image data for distinguishing between the background and the target in situations when the spatial signal of the target is swamped by noise. Our method is formulated to detect a time-domain transient signal of unknown scale and arrival time in noisy background. We proposed a bispectrum-based model to characterize the temporal behavior of pixels, and the detection ability under different frame rates and SNRs is analyzed. The method is evaluated using both simulated and real-world data, and we provide a comparison to other widely used point target detection approaches. Our experimental results demonstrate that our algorithm can efficiently detect extremely low SNR targets that are virtually invisible to humans based on time-domain analysis of image sequences.

高灵敏度空间点目标探测系统设计

高灵敏度空间点目标探测系统设计

Automatic detection of the unknown number point targets in FMICW radar signals

Automatic detection of the unknown number point targets in FMICW radar signals

Point target detection utilizing super-resolution strategy for infrared scanning oversampling system

To improve the resolution of remote sensing infrared images, infrared scanning oversampling system is employed with information amount quadrupled, which contributes to the target detection. Generally the image data from double-line detector of infrared scanning oversampling system is shuffled to a whole oversampled image to be post-processed, whereas the aliasing between neighboring pixels leads to image degradation with a great impact on target detection. This paper formulates a point target detection method utilizing super-resolution (SR) strategy concerning infrared scanning oversampling system, with an accelerated SR strategy proposed to realize fast de-aliasing of the oversampled image and an adaptive MRF-based regularization designed to achieve the preserving and aggregation of target energy. Extensive experiments demonstrate the superior detection performance, robustness and efficiency of the proposed method compared with other state-of-the-art approaches.