利用二阶方向导数极大值检测红外小目标

为提高复杂环境下红外小目标的检测率,提出了基于二阶方向导数极大值的红外小目标检测算法。该算法首先对二阶方向导数的性质进行了分析,对极大值进行阈值翻转操作,将背景中的平坦成分和边缘成分剔除。接着,根据小面模型对背景进行预测,并以预测误差为权值进一步增强小目标区域。以上2个步骤的计算可通过4个卷积实现,加快了检测速度。最后,对少量候选小目标计算局部对比度,降低了虚警率。实验结果表明：该检测算法在6种复杂背景下平均信杂比增益为78.413 0,平均背景抑制因子为35.079 6,具有较强的鲁棒性和较高的检测率。     

An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system

To achieve higher detection rate and lower false alarm rate in dim and small target detection, this paper proposed an improved algorithm based on the contrast mechanism of human visual system (HVS) for infrared small target detection in an image with complicated background. According to the contrast mechanism of HVS, Laplacian of Gaussian (LoG) filter is exploited to deal with the input image, which can not only suppress the background noise and clutter but also enhances the target intensity significantly. As a result it increases the contrast ratio between target and background. To further eliminate residual clutter, we process the filtered image with morphological method in all directions. True target is finally obtained by applying local thresholding segmentation to the pre-processed image. Experimental results demonstrate its superior and reliable detection performance by high detection rate and low false alarm rate.     

基于光流直方图的云背景下低帧频小目标探测方法

对低帧频、云层背景下,低信噪比的弱点目标探测率降低的问题.提出了光流直方图(OFH)的定义.并且给出了OFH的性质.分析了低帧频下红外图像探测弱点目标时探测率降低的原凶,提出了一种基于OFH背景补偿的红外点目标探测算法.利用OFH得到背景的运动欠量.进行运动背景补偿;然后利用目标与云层运动差异性,得到帧间比较结果,并对比较结果通过Robinson滤波器进一步滤除残留的边缘,达到降低虚警的目的.实验结果表明,该算法中以显著提高往复杂背景下红外点目标检测概率,并凡能够探测出信噪比为1的目标.     

Prediction of the performance of an algorithm for the detection of small targets in infrared images

Detection of small targets in infrared (IR) images is important in IR image processing. For the prediction of performance of a detection algorithm, it is necessary to calculate the probability of detection and probability of false alarm. A method is developed to calculate the probabilities in this paper. The detection is divided into two parts: the first part, which is called pre-detection, is to find out candidates for targets in a single frame of an image; and the second part is to localize the target in multiple frames of the image. Under some assumptions, the pre-detection probability, the false detection probability of single frame, detection probability and false alarm probability are derived. The algorithm for the detection of small target in IR image, which is used for the derivation of the probabilities, is contrast threshold detection based on background prediction, and a pipeline filter is used for multiframe image processing. The results show the relationship of the probabilities to the contrast of target to background, SNR, and contrast threshold.     

预警卫星半实物仿真系统设计与实现

国防支援计划(DSP)是美国现役的预警卫星系统,主要对导弹进行早期探测和预警。为评估DSP卫星对目标的探测能力,文中分析了预警卫星的部署情况和工作模式,提出了预警卫星仿真系统的总体设计方案,确定该系统的功能模块以及实现方式;根据预警卫星双波段探测的工作模式构建了红外双色扫描探测模型,提出了基于双霍夫变换的多帧关联目标检测算法,利用目标的运行特性有效去除背景噪声,并结合双波段探测信息融合策略降低卫星虚警率,最后利用双星定位方法预估目标轨迹并对不同发射角目标的预测轨迹与真实轨迹之间的误差进行对比分析。通过试验运行,该系统达到了预期结果,具备对目标导弹的探测和跟踪能力,为导弹性能评估提供一种仿真手段和依据。     

A combined approach of Kullback–Leibler divergence and background subtraction for moving object detection in thermal video

In this work, a robust method for moving object detection in thermal video frames has been proposed by including Kullback–Leibler divergence (KLD) based threshold and background subtraction (BGS) technique. A trimmed-mean based background model has been developed that is capable enough to reduce noise or dynamic component of the background. This work assumed that each pixel has normally distributed. The KLD has computed between background pixel and a current pixel with the help of Gaussian mixture model. The proposed threshold is useful enough to classify the state of each pixel. The post-processing step uses morphological tool for edge linking, and then the flood-fill algorithm has applied for hole-filling, and finally the silhouette of targeted object has generated. The proposed methods run faster and have validated over various real-time based problematic thermal video sequences. In the experimental results, the average value of F₁-score, area under the curve, the percentage of correct classification, Matthew’s correlation coefficient show higher values whereas total error and percentage of the wrong classification show minimum values. Moreover, the proposed-1 method achieved higher accuracy and execution speed with minimum false alarm rate that has been compared with proposed-2 as well as considered peer methods in the real-time thermal video.     

一种被动声呐信号检测的低虚警率方法

针对被动声呐多目标信号检测中的噪声背景归一化问题,提出了一种基于数学形态学滤波的噪声背景归一化新方法。该方法利用数学形态学处理中的膨胀和腐蚀算子,以及基于多项式拟合的数据均值估计方法,构造出了一种能够较为准确的估计噪声门限的方法,并以之进行噪声背景归一化,在较好保留信号信息的前提下较大程度的抑制了噪声,有效降低了多目标信号检测的虚警概率。通过计算机仿真对比了该算法与S3PM算法、OTA算法的性能,结果表明该噪声背景归一化算法能够在检测概率损失较小的情况下较大幅度地降低检测的虚警率。实际被动声呐数据处理的对比结果同样验证了该算法的有效性。      

基于光谱角累加的高光谱图像异常检测算法

针对传统方法中用作背景的像素中存在干扰像素的问题,提出一种基于光谱角累加的高光谱图像异常检测算法。通过计算测试区域待测像素光谱向量与其他光谱向量之间的夹角,并将其累加,得到图像中每个像素的异常程度;然后使用波段选择预处理方法进一步提高检测性能。HyMap高光谱数据验证表明,在虚警概率设为0.008时,检测概率达到0.73,即在提高异常检测可靠度的同时,降低了虚警概率。     

基于扩展数学形态学的高光谱图像异常检测

提出了一种新型的基于扩展数学形态和光谱相似度测量的高光谱图像异常榆测方法.在日标与背景未知的情况下.同时利用光谱和空间信息实现日标的定位与检测,实现离光谱遥感数据的日标检测.通过扩展的膨胀和腐蚀操作实现目标特征提取;通过正交投影散度计算扩展形态学操作的累加距离确定排序关系并利用其融合特征提取结果实现特征提取结果的融合.算法性能通过合成的OMIS数据进行评价.与经典异常检测RX算法进行比较.并应用于具有相似光谱特征目标的区分.实验证明,本文提出的算法性能优于RX算法.具有低虚警率的异常目标检测结果,并且能够较好地区分了相似光谱特征的异常日标.     

Detection method for small and dim targets from a time series of images observed by a space-based optical detection system

To revisit cataloged space targets, a space-based optical detection system normally observes space targets continuously in a target tracking mode. In the time series of images produced by continuous observation, there are not only the target but also complicated background clutter (a mass of stars) and noises. The existing method only can detect the target with an signal-to-noise ratio (SNR) greater than 6 from these images. This paper presents a detection method for the target with an SNR less than 6. The proposed method consists of an SNR enhancement algorithm and an adaptive background and noise suppression algorithm. Simulation and analytical results show the proposed method detects the target submerged in noise and background clutter when SNR is equal to 3 and the detection probability and the false alarm probability both reach very high performance. This proposed method can help solve the problem of revisiting some weak cataloged space targets.     

基于关键帧的核密度估计背景建模方法

在充分研究现有运动目标检测算法的基础上,提出了一种新的非参数核密度估计背景模型。应用高斯核密度估计进行背景建模,不需要事先假定背景特征的密度分布,根据视频序列像素灰度的相似性原理从训练样本中提取关键帧,减小了密度估计的样本量。将剩余的灰度值按距离最近原则归并到关键帧中去,降低目标检测的虚警率和误检率。实验结果表明:该算法在检测精度影响极小的情况下,大大提高了原算法的速度,可用于室外的实时视频监控系统。     

基于支持向量描述的自适应高光谱异常检测算法

提出了一种应用于高光谱异常检测的自适应支持向量数据描述方法.根据高光谱数据和局部异常检测模型的特点,通过局部背景分波段二阶分布统计,分析了核参量与局部背景总体标准差的变化关系,构造了随检测背景变化的局部检测核参量,使得检测算法针对不同背景分布自适应地调整检测核参量.克服了传统支持向量描述算法由于采用固定核参量带来的复杂背景下检测性能下降的问题.通过模拟数据和真实高光谱数据的测试检验,接收机特性曲线表明该算法相对于传统固定核参量支持向量数据描述方法,在相同虚警概率下检测概率提高了10%.     

基于多结构元素形态滤波与自适应阈值分割相结合的红外弱小目标检测

针对低信噪比灰度图像中弱小目标检测的难题,分析了红外弱小目标成像的特点,提出了基于多结构元素形态滤波与自适应阈值分割相结合的目标检测算法.利用目标运动的连续性、规律性和噪音产生的随机性,结合数学形态学结构元素的特点,研究了一种多结构元素形态滤波的管道滤波方法,通过流水线管道检测目标运动轨迹.实验结果表明,该算法应用于复杂背景下低信噪比的红外弱小目标图像能够得到较理想的结果,并且目标检测概率高,速度快,虚警率低.     

强杂波背景红外弱小目标检测算法

针对强杂波背景远距离红外弱小信号目标的特点,提出了一种基于自适应滤波的红外弱小信号检测方法。算法首先对图像进行消噪声处理,其次运用自适应滤波方式消除背景增强目标信号,最后进行基于点源目标(试验采集)成像信号特性的判决法则删除虚假目标,算法有效解决了光电探测设备高检测概率与低虚警率的矛盾。实验结果表明:该方法能够在单帧图像上有效提取出小区域信噪比为4的弱小信号目标,检测概率不低于0.75,虚警率不高于1次/100帧。     

光子成像静止点目标的管道滤波探测方法

构建了具有单光子成像能力的光子成像系统,提出应用管道滤波方法实现对光子受限静止点目标的探测。分析了光子图像静止点目标与点状噪声特征,根据静止点目标在序列图像中位置的确定性以及噪声点的不相关性,研究了基于管道滤波的光子成像静止点目标探测方法。为降低目标探测的虚警概率,优化了管道滤波直径。以实验采集得到的多组光子图像序列为样本,获得了探测概率、虚警概率与信号光子数、噪声光子数、管道长度以及检测阈值的关系。检测结果显示,对信号与噪声的平均发生率为0.4和5.215的序列图像,当管道长度为9、检测阈值为2时,探测概率达0.9以上且虚警概率〈0.08。对比多组图像序列的检测结果表明,影响探测概率的主要参数是信号光子数,而影响虚警概率的主要参数是噪声光子数。     

A robust infrared dim target detection method based on template filtering and saliency extraction

Dim target detection in infrared image with complex background and low signal-clutter ratio (SCR) is a significant and difficult task in the infrared target tracking system. A robust infrared dim target detection method based on template filtering and saliency extraction is proposed in this paper. The weighted gray map is obtained from the infrared image to highlight the target which is brighter than its neighbors and has weak correlation with its background. The target saliency map is then calculated by phase spectrum of Fourier Transform, so that the dim target detection could be converted to salient region extraction. The potential targets are finally extracted by combining the two maps. Moreover, position discrimination between targets in the two maps is used to exclude the false alarms and extract the targets. Experimental results on measured images indicate that our method is feasible, adaptable and robust in different backgrounds. The ROC (Receiver Operating Characteristic) curves obtained from the simulated images demonstrate the proposed method outperforms some existing typical methods in both detection rate and false alarm rate, for target detection with low SCR.     

Target detection algorithm in side-scan sonar image based on pixel importance value measurement

Side-scan sonar detection application always combines with unstable results.A two-stage novel pixel importance value measurement algorithm is proposed to stabilize the detection ability and false alarm probability simultaneously.In first stage of the algorithm,a new feature defined as pixel importance value(PIV) is proposed in terms of distances between the target pixel and each other pixels.PIV measurement of current pixel is defined as the weighted sum of all remaining segmented pixels.The weighted part refers to Gaussian kernel,which means closer pixels gets higher weight.Thus,targets with higher PIV can be located.In the second stage,we use convolutional neural network as classifier to eliminate the dot-like false targets.Our experiment data is obtained by autonomous underwater vehicle,where we demonstrate superior performance of our algorithm over the state-of-the-art sonar detection algorithms in terms of 90.39% recall rate and 2.39% false alarm probability.     

基于支持向量描述的自适应高光谱异常检测算法

提出了一种应用于高光谱异常检测的自适应支持向量数据描述方法.根据高光谱数据和局部异常检测模型的特点,通过局部背景分波段二阶分布统计,分析了核参量与局部背景总体标准差的变化关系,构造了随检测背景变化的局部检测核参量,使得检测算法针对不同背景分布自适应地调整检测核参量.克服了传统支持向量描述算法由于采用固定核参量带来的复杂背景下检测性能下降的问题.通过模拟数据和真实高光谱数据的测试检验,接收机特性曲线表明该算法相对于传统固定核参量支持向量数据描述方法,在相同虚警概率下检测概率提高了10%.     

基于独立成分分析的高光谱图像异常检测

针对高光谱图像中背景及目标先验知识未知条件下的异常目标检测问题,提出了一种基于独立成分分析(ICA)的异常探测算法.首先估计原始数据的虚拟维(VD)以确定要分离的独立成分个数,在此基础上进行快速独立成分分析(FastICA),然后基于平均局部奇异度选择含异常信息较多的独立成分,最后使用丰度量化算法得到异常目标的丰度图像...     

时空域双边滤波背景抑制算法研究

红外图像中的强边缘一直是制约红外弱小目标检测概率的重要因素,同时是产生目标虚警的重要来源。采用能够同时利用邻域空间欧式距离和灰度值相似性的双边滤波,并针对天基红外系统中帧间背景缓慢变化的特点,设计一种基于时空域的双边滤波法,实验验证该方法对复杂背景的强边缘具有很好地抑制效果,信杂比增益大于3.6,同时能够很好地保留目标能量,目标能量损失因子D factor小于0.2,有效地提高目标的局域信杂比。