Dim Target Detection Research Articles

Multiresolution Non-Local Mean Filter for Infrared Dim and Small Target Background Suppression

Structured clouds and ground building background suppression are difficult problems for dim and small target detection technique. In this paper, the dim and small target background suppression method based on combined curvelet transform with modified non-local means filter was presented to solve the problem. And local gradient statistics is introduced to boost ability of method which suppresses false by background structure. The innovation was that the curvelet transform was adopted to decompose the input infrared image, which extracts multi-scale and directional detail features of the image. Moreover non-local means filter improved by local gradient characteristic was introduced to suppress background details and enhance target information for suppression background. Compared with two-dimensional least mean square (TDLMS) and modified partial differential equation (MPDE) methods, through visual quality and value index, several groups of experimental results demonstrate that the presented method can suppress complicated background in dim and small target image effectively.

다수 표적 탐지를 위한 Track-Before-Detect 알고리듬 연구

Vision-based collision avoidance system for air traffic management requires a excellent multiple target detection algorithm under low signal-to-noise ratio (SNR) levels. The track-before-detect (TBD) approaches have significant applications such as detection of small and dim targets from an image sequence. In this paper, two detection algorithms with the TBD approaches are proposed to satisfy the multiple target detection requirements. The first algorithm, based on a dynamic programming approach, is designed to classify multiple targets by using a k-means clustering algorithm. In the second approach, a hidden Markov model (HMM) is slightly modified for detecting multiple targets sequentially. Both of the proposed approaches are used in numerical simulations with variations in target appearance properties to provide satisfactory performance as multiple target detection methods.

基于复滤波器组的红外弱小目标检测算法

基于复滤波器组的红外弱小目标检测算法

基于自适应侧抑制网络的红外弱小目标检测

基于自适应侧抑制网络的红外弱小目标检测

基于多步长梯度特征的红外弱小目标检测算法

基于多步长梯度特征的红外弱小目标检测算法

基于无下采样Contourlet变换和独立分量分析的红外弱小目标检测

基于无下采样Contourlet变换和独立分量分析的红外弱小目标检测

低对比度小目标检测

低对比度小目标检测

Research of Star-sky Image Registration Method for Dim Target Detection

Research of Star-sky Image Registration Method for Dim Target Detection

Relative Entropy Rate Based Multiple Hidden Markov Model Approximation

This paper proposes a novel relative entropy rate (RER) based approach for multiple HMM (MHMM) approximation of a class of discrete-time uncertain processes. Under different uncertainty assumptions, the model design problem is posed either as a min-max optimisation problem or stochastic minimization problem on the RER between joint laws describing the state and output processes (rather than the more usual RER between output processes). A suitable filter is proposed for which performance results are established which bound conditional mean estimation performance and show that estimation performance improves as the RER is reduced. These filter consistency and convergence bounds are the first results characterizing multiple HMM approximation performance and suggest that joint RER concepts provide a useful model selection criteria. The proposed model design process and MHMM filter are demonstrated on an important image processing dim-target detection problem.

The Detection of Multiple Targets with Low SNR Based on Greedy Algorithm

Aiming at the detection of dim targets with SNR≤2dB,Track before detection method based on greedy principle is presented.The state update is completed by choosing an extended node and the energy is accumulated along the trajectory.Finally the target is identified according to likelihood ratio test.In pre-processing stage,spatial-temporal filter is designed.On one hand,clutter is de-correlation based on compound kernel estimation spatial filter;On the other hand,noise is restrained via projection along time axis,the searching space is reduced about 80%.Furthermore,the independence and normality of residuals after kernel estimation are tested in nonparametric statistical theory.The comparison experiments with traditional dynamical programming algorithm show that the time complexity can reduce to mn.It not only has the predominant in time but also has higher probability of detection and lower false alarm than the former.

基于分类的红外云层背景弱小目标检测方法

基于分类的红外云层背景弱小目标检测方法

Infrared dim target detection based on fractal dimension and third-order characterization

We propose an improved algorithm based on fractal dimension and third-order characterization to detect dim target with cluttered background in an infrared (IR) image. We also illustrate the performance and efficiency comparisons between the presented algorithm and the traditional fractal detection method on real IR images. The experimental results show that the proposed algorithm is robust and efficient for IR dim target detection.

Dim target detection and tracking based on empirical mode decomposition

Dim target detection and tracking algorithm based on the empirical mode decomposition (EMD) is proposed. EMD is introduced to decompose the original image into a definite number of high frequency and low frequency components by means of sifting. With the EMD algorithm, it is valid to estimate the background and get the dim target by removing the background from the original image. The algorithm detects dim moving target effectively and estimate its trajectory accurately. The data analysis and experiments show that the proposed algorithm is adaptable to real-time target detection and tracking.

Improved power-law-detector-based moving small dim target detection in infrared images

A novel algorithm based on an improved power-law detector is proposed to detect moving small dim targets against heavy cluttered background in IR image sequences. As the gray-scale value of a pixel in an image fluctuates when a target passes by, this fluctuation can be viewed as a weak transient signal, and can be detected by the power-law detector, which performs well for transient signal detection. The experimental results illustrate that the proposed algorithm is effective and adaptable for moving small dim target detection in image sequences with heavy clutter

基于光流直方图的云背景下低帧频小目标探测方法

基于光流直方图的云背景下低帧频小目标探测方法

Novel method on dual-band infrared image fusion for dim small target detection

The paper puts forward a fractal dimension algorithm that fuses mid-wave and long-wave infrared images and detects targets. Usually, the targets in infrared images are man-made, and their fractal dimensions are different from that of their natural background. In the fractal dimension algorithm, the source images are first decomposed by wavelet transformation. Then in the wavelet transformation domain, fractal dimensions are calculated and fusion rules that merge corresponding sub images of two matching source images are set up, and new sub images are created. Finally, the image is reconstructed by inverse wavelet transformation and a fused image is obtained. The fusion results have shown that the contrast between the targets and their background has changed significantly, and the targets can be easily detected by applying a contrast threshold. The experimental results have shown that the method using fractal dimension to fuse dualband infrared images and detect targets is superior to the one using mid-wave or longwave infrared images to detect targets alone.

Adaptive Rectification Filter for Detecting Small IR Targets

Clutter suppression is one of the most important subjects in the field of small target detection under infrared (IR) strong clutter background. While removing the clutter background, however, such methods may reverse the relative energy distribution of target and noise in the clutter suppressed image, and disturb the subsequent target segmentation and detection. This paper analyzes the causation of such problems, does research on the relationship between target energy characteristics and detection probability, and presents a novel filter of energy distribution adaptive rectification (EDARF). Based on the EDARF, an improved framework of dim small target detection is proposed to rectify the energy distribution in the clutter-suppressed images by conventional adaptive filters. The proposed EDARF's performance is estimated by experimental comparisons of three linear/nonlinear filters before and after using EDARF. Extensive experimental results show that the proposed EDARF improves efficiently the performance of detecting dim small targets against strong undulant cloud-cluttered backgrounds.

Neural-network-based single-frame detection of dim spot target in infrared images

Infrared (IR) images derived from cloudy skies are always too spatially varied for tiny targets to be detected, especially for single-frame detection. Using the neural networks (NN) nonlinear regression, discrimination, and self-leaning capability, an NN-based method is proposed for tiny point target detection in single-frame IR images with high background clutter. First, the background was estimated by an improved NN-based morphologic filter, the structure element of which was optimized by a two-layer NN. Second, noise characteristics were well studied, and thus a two-level segmentation is presented to delete noises as well as to further remove remaining background components. Last, images with several potential targets were fed to a BP NN that predicted the identity of the input, which was either a target or a pseudo-target. It is these two neural networks that separate target from background and pseudo-target, respectively, with different training destinations, thus avoiding the over-training problem. Results on real data indicate that, given the false alarm probability, the detection probability by this method reaches 98.3%, which is improved by 11.02% compared to the traditional approach with fixed SE and without trained NN.

Novel dim target detection and estimation algorithm based on double threshold partial differential equation

The aim of the present work is to propose a brand-new algorithm based on an adaptive double threshold nonlinear anisotropic diffusion equation (DTPDE) to detect and track moving dim targets against complex cluttered background in infrared (IR) image sequences. We also illustrate the performance comparisons of the proposed algorithm DTPDE and two-dimensional least mean squares (TDLMS) on real IR image sequence data. Extensive experiment results demonstrate the proposed novel algorithm's flexibility and adaptability in detecting moving weak dim targets.

Dim target detection based on nonlinear multifeature fusion by Karhunen-Loeve transform

Dim target detection and tracking is a key technique for imaging tracking systems and is a challenging and leading-edge subject. A new strategy for dim target detection is presented. Various nonlinear features that are representative of dim targets are extracted from image sequences. Then, an information fusion algorithm by Karhunen-Loeve (K-L) transform is used to obtain an integrated feature. As a correlation degree, using an integrated feature calculates a target's position coordinates. Experimental results demonstrate the proposed method is feasible and effective.