Dim Small Target Research Articles

A Dim-Small Target Tracking Approach Based on Robust Hyperspectral Features

The dim-small target tracking is a central problem in several related applications. Subjected to the lack of objective information, traditional target tracking is difficult to meet the needs of dim-small target tracking. A dim-small target’s description based on robust spectral features is proposed to solve this problem. By creating a multi-dimensional feature space and extending the limited RGB information to the hyperspectral information, a new type of target’s description is presented to track the dim-small target with hyperspectral features.

Study of Detection Method of Dim Small Targets Based on Artificial Immune Algorithm

Study of Detection Method of Dim Small Targets Based on Artificial Immune Algorithm

Infrared Small Targets Detection based on MRF Model

Aiming at the difficulty in detecting infrared dim small target under strong background clutter, we propose a novel algorithm of infrared dim small target detection based on Markov random fields(MRF) model. We at first use adaptive morphological filter to suppress the background clutter, then introduce new potential function and energy function according to MRF theory and infrared small target image features, and build a target detection model to confirm automatically the location and size of the target. Simulation results show that the algorithm is effective.

A Real-time Target Detection Algorithm for Panorama Infrared Search and Track System

With regard to target detection in high resolution panorama images attained by circumferential scan Infrared Search and Tracking system, a rough-to-meticulous real-time target detection algorithm is proposed based on analysis of characteristics of targets and background. In the rough detection phase, it attains initial high rate target detection by quick real-time algorithm, based on the gray high frequency and movement characteristics of the target in the whole panorama image. In the meticulous detection phase, focusing on the detected suspected target sliced images, it has further delicate detection and recognition on the basis of targets’ characteristics to exclude those false jamming. The detection result of the test images shows, the algorithm enables stable detection with low-rate false alarm for distant dim small targets, and has been applied to the development of engineering sample of the Panorama Infrared Search and Tracking system.

基于复杂度和方向梯度的红外弱小目标检测方法

In order to solve the problem of detecting small dim targets in infrared images with the complicated air background,an improved algorithm based on image complexity and orientation gradient is put forward.The information entropy reflects the complexity of the image.The weighted information entropy with the variance and the change rate of local pixels suppresses both the inside and the edge of clouds.Since the image of the complexity is obtained,targets can be extracted by the means of multi-degree and multi-orientation gradient under the circumstance that the complexity of the background is higher than that of the targets.Experiments indicate that the algorithm can detect small dim targets effectively with complicated cloud background.

Infrared dim small target enhancement using toggle contrast operator

A new infrared dim small target enhancement algorithm based on toggle contrast operator is proposed. Toggle contrast operator is modified and used to construct operators using the image features derived from dilation and erosion operators. Then, based on the constructed operators, the operators which could be used to estimate the clutter background of the original infrared dim small target image are proposed using the same strategy as the definition of opening. Finally, the infrared dim small target is well enhanced through subtracting the estimated background from the original image. Experimental results on infrared images with different types of targets verified that the proposed method could effectively enhance infrared dim small target, which would be very useful for infrared dim small target detection and tracking.

The Extracting System Design of Small Dim Targets in Complex Background

For the moving small dim targets in visible image sequences with low SNR and complex background, whose contained characters are simple and poor, they are extracted difficultly. This paper proposes a new method to detect and extract positions of small dim moving targets. According to the features of moving targets, in a very short time interval, the target trajectory is considered as a straight line approximately. Firstly, it makes use of threshold segmentation methods to extract the positions of targets in each frame, then building the motion line equations after the joint of multi-frame processing results. Finally, the position of small dim targets are detected out and extracted, and false targets are eliminated accurately. Hardware system was designed and the algorithm is implemented on hardware systems successfully. The experiment results show related functions of the system and extracting algorithm is feasible, the system is stable and has a strong process ability, which can effectively detect and extract small and dim target in complex background correctly.

Hit-or-miss transform based infrared dim small target enhancement

To efficiently enhance a dim infrared small target embedded in heavy clutter, a hit-or-miss transform based method is proposed in this paper. First, the gray level hit-or-miss transform is given and discussed. Then, by analyzing the structuring elements used in the hit-or-miss transform following the purpose of infrared small target enhancement, a simple infrared small target enhancement method is proposed by using flat structuring elements and a threshold parameter. The threshold imports the properties of infrared small target into the gray level hit-or-miss transform, which improves the performance of the hit-or-miss transform for infrared small target enhancement. Experimental results on infrared dim small target images with different clutter backgrounds verified that the proposed method was efficient for infrared small target enhancement.

Survey on Dim Small Target Detection in Clutter Background: Wavelet, Inter-Frame and Filter Based Algorithms

Dim small target is an active and important research area in image processing and pattern recognition. Various algorithms have been proposed to detect and track dim small target. This paper reviews some algorithms for dim small target detection, including the wavelet based algorithms, inter-frame difference based algorithms and filter based algorithms. Also, the further development of the technologies has been briefly analyzed.

红外序列图像中运动弱小目标检测

红外序列图像中运动弱小目标检测

Top-hat selection transformation for infrared dim small target enhancement

To improve the performance of top-hat transformation for infrared dim small target enhancement, a top-hat selection transformation is proposed in this paper. First, the top-hat selection transformation is defined and analysed based on analysing and improving of classical top-hat transformation. Then, the top-hat selection transformation is used to enhance infrared dim small targets. The calculation of parameters used in top-hat selection transformation for infrared dim small target enhancement is discussed in detail. Various experiments verified that top-hat selection transformation was effective for infrared dim small target enhancement.

Analysis of new top-hat transformation and the application for infrared dim small target detection

To improve the performance of top-hat transformation for infrared dim small target detection in a simple and effective way, the definition, properties, multi-scale operations of new top-hat transformation and the application for infrared dim small target detection are addressed in this paper. The definition of new top-hat transformation uses two different but correlated structuring elements to reorganize the classical top-hat transformation, and takes into account of the difference information between the target and surrounding regions. Given this definition, the new top-hat transformation has some special properties and three types of multi-scale operations, which are discussed in detail. Subsequently, one application case of multi-scale operation for noise suppression is given. Good performance of the application for infrared dim small target detection is obtained, which could be ascribed to the proper selection of structuring elements based on the properties. The experimental results of the application demonstrate that new top-hat transformation can detect infrared dim small target more efficiently than classical top-hat transformation and some other widely used methods.

Enhancement of dim small target through modified top-hat transformation under the condition of heavy clutter

A new algorithm to enhance dim small target through modified top-hat transformation is proposed in this paper. Firstly, the property of top-hat transformation is analyzed following the property of small target regions. Secondly, a judging value is calculated following the properties of target region and top-hat transformation. Finally, the judging value is imported into the top-hat transformation to form the modified top-hat transformation. Because of the judging value in modified top-hat transformation, dim target can be significantly enhanced and heavy clutter can be effectively suppressed. Experimental results verified that the modified top-hat transformation for target enhancement under the conditions of heavy clutter and dim target intensity was effective and robust.

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

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.