Minjie Wan

Robust infrared small target detection via non-negativity constraint-based sparse representation

Infrared (IR) small target detection is one of the vital techniques in many military applications, including IR remote sensing, early warning, and IR precise guidance. Over-complete dictionary based sparse representation is an effective image representation method that can capture geometrical features of IR small targets by the redundancy of the dictionary. In this paper, we concentrate on solving the problem of robust infrared small target detection under various scenes via sparse representation theory. First, a frequency saliency detection based preprocessing is developed to extract suspected regions that may possibly contain the target so that the subsequent computing load is reduced. Second, a target over-complete dictionary is constructed by a varietal two-dimensional Gaussian model with an extent feature constraint and a background term. Third, a sparse representation model with a non-negativity constraint is proposed for the suspected regions to calculate the corresponding coefficient vectors. Fourth, the detection problem is skillfully converted to an l1-regularized optimization through an accelerated proximal gradient (APG) method. Finally, based on the distinct sparsity difference, an evaluation index called sparse rate (SR) is presented to extract the real target by an adaptive segmentation directly. Large numbers of experiments demonstrate both the effectiveness and robustness of this method.

Infrared small target enhancement: grey level mapping based on improved sigmoid transformation and saliency histogram

Abstract Infrared (IR) small target enhancement plays a significant role in modern infrared search and track (IRST) systems and is the basic technique of target detection and tracking. In this paper, a coarse-to-fine grey level mapping method using improved sigmoid transformation and saliency histogram is designed to enhance IR small targets under different backgrounds. For the stage of rough enhancement, the intensity histogram is modified via an improved sigmoid function so as to narrow the regular intensity range of background as much as possible. For the part of further enhancement, a linear transformation is accomplished based on a saliency histogram constructed by averaging the cumulative saliency values provided by a saliency map. Compared with other typical methods, the presented method can achieve both better visual performances and quantitative evaluations.

Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

Infrared image enhancement is a crucial pre-processing technique in intelligent urban surveillance systems for Smart City applications. Existing grayscale mapping-based algorithms always suffer from over-enhancement of the background, noise amplification, and brightness distortion. To cope with these problems, an infrared image enhancement method based on adaptive histogram partition and brightness correction is proposed. First, the grayscale histogram is adaptively segmented into several sub-histograms by a locally weighted scatter plot smoothing algorithm and local minima examination. Then, the fore-and background sub-histograms are distinguished according to a proposed metric called grayscale density. The foreground sub-histograms are equalized using a local contrast weighted distribution for the purpose of enhancing the local details, while the background sub-histograms maintain the corresponding proportions of the whole dynamic range in order to avoid over-enhancement. Meanwhile, a visual correction factor considering the property of human vision is designed to reduce the effect of noise during the procedure of grayscale re-mapping. Lastly, particle swarm optimization is used to correct the mean brightness of the output by virtue of a reference image. Both qualitative and quantitative evaluations implemented on real infrared images demonstrate the superiority of our method when compared with other conventional methods.

Stokes-vector-based polarimetric imaging system for adaptive target/background contrast enhancement.

A novel method to optimize the polarization state of a polarimetric imaging system is proposed to solve the problem of target/background contrast enhancement in an outdoor environment adaptively. First, the last three elements of the Stokes vector are selected to be the observed objects polarization features, the discriminant projection of which is regarded as the detecting function of our imaging system. Then, the polarization state of the system, which can be seen as a physical classifier, is calculated by training samples with a support vector machine method. Finally, images processed by the system with the designed optimal polarization state become discriminative output directly. By this means, the target/background contrast is enhanced greatly, which results in a more accurate and convenient target discrimination. Experimental results demonstrate that the effectiveness and discriminative ability of the optimal polarization state are credible and stable.

A Level Set Method for Infrared Image Segmentation Using Global and Local Information

Infrared image segmentation plays a significant role in many burgeoning applications of remote sensing, such as environmental monitoring, traffic surveillance, air navigation and so on. However, the precision is limited due to the blurred edge, low contrast and intensity inhomogeneity caused by infrared imaging. To overcome these challenges, a level set method using global and local information is proposed in this paper. In our method, a hybrid signed pressure function is constructed by fusing a global term and a local term adaptively. The global term is represented by the global average intensity, which effectively accelerates the evolution when the evolving curve is far away from the object. The local term is represented by a multi-feature-based signed driving force, which accurately guides the curve to approach the real boundary when it is near the object. Then, the two terms are integrated via an adaptive weight matrix calculated based on the range value of each pixel. Under the framework of geodesic active contour model, a new level set formula is obtained by substituting the proposed signed pressure function for the edge stopping function. In addition, a Gaussian convolution is applied to regularize the level set function for the purpose of avoiding the computationally expensive re-initialization. By iteration, the object of interest can be segmented when the level set function converges. Both qualitative and quantitative experiments verify that our method outperforms other state-of-the-art level set methods in terms of accuracy and robustness with the initial contour being set randomly.

Infrared simulation and implementation of virtual ocean scene

Research of modeling infrared radiation characteristic of ocean background plays an important role in areas like marine remote sensing, prevention and control of ocean pollution, meteorological observation, and so on. In this paper, we build a three-D ocean surface model based on P-M wave spectrum, then set up the camera projection model. We use LOWTRAN 7 to calculate solar irradiance and sky background radiance, and then use thermal radiation theory to calculate the thermal radiation of the ocean itself and use bidirectional reflectance distribution function to calculate the reflection of the sea to the solar radiation and sky background radiance. Finally, with all the above radiation components considered, we generate the ocean background infrared simulation image.

Target recognition method based on polarization parameters

Traditional target detection must be based on imaging. It costs plenty of calculation but returns poor results. Polarization is a common property of the vector wave. As is known to all, light is electromagnetic wave. Polarization state is the inherent nature of light. After acting on different objects, the polarization state of light will obviously change which can be reflected by Mueller matrix. Therefore analyzing Mueller matrix is an efficient way to attain polarization information. This paper proposes a method of target detection based on Mueller matrix. Firstly, Mueller matrix should be analyzed to extract three parameters: depolarization index DI(M), duplexing attenuation parameter D(M) and polarization parameter P(M), which describe the polarization of targets. By measuring the Mueller matrix of typical artificial target and natural background under different incident angles, corresponding DI(M), D(M) and P(M) trend charts are drawn respectively. According to the trend charts, a reasonable method is proposed to separates each of the three images into two parts—target and background. However, the human eye is not sensitive to gray images, which can only distinguish a dozen grayscale but thousands of colors. Because of the high resolution for colors, we can use false color fusion technique to turn grayscales into colorful images to increase the rate of the human eye observation and recognition effectively. The technology has a broad application prospect in optical constants of inversion, military reconnaissance and camouflage detection. Proved by a mass of experiment, the method has high efficiency and high recognition rate.

Infrared moving small target detection based on saliency extraction and image sparse representation

Moving small target detection in infrared image is a crucial technique of infrared search and tracking system. This paper present a novel small target detection technique based on frequency-domain saliency extraction and image sparse representation. First, we exploit the features of Fourier spectrum image and magnitude spectrum of Fourier transform to make a rough extract of saliency regions and use a threshold segmentation system to classify the regions which look salient from the background, which gives us a binary image as result. Second, a new patch-image model and over-complete dictionary were introduced to the detection system, then the infrared small target detection was converted into a problem solving and optimization process of patch-image information reconstruction based on sparse representation. More specifically, the test image and binary image can be decomposed into some image patches follow certain rules. We select the target potential area according to the binary patch-image which contains salient region information, then exploit the over-complete infrared small target dictionary to reconstruct the test image blocks which may contain targets. The coefficients of target image patch satisfy sparse features. Finally, for image sequence, Euclidean distance was used to reduce false alarm ratio and increase the detection accuracy of moving small targets in infrared images due to the target position correlation between frames.

Fast randomized Hough transformation track initiation algorithm based on multi-scale clustering

A fast randomized Hough transformation track initiation algorithm based on multi-scale clustering is proposed to overcome existing problems in traditional infrared search and track system(IRST) which cannot provide movement information of the initial target and select the threshold value of correlation automatically by a two-dimensional track association algorithm based on bearing-only information . Movements of all the targets are presumed to be uniform rectilinear motion throughout this new algorithm. Concepts of space random sampling, parameter space dynamic linking table and convergent mapping of image to parameter space are developed on the basis of fast randomized Hough transformation. Considering the phenomenon of peak value clustering due to shortcomings of peak detection itself which is built on threshold value method, accuracy can only be ensured on condition that parameter space has an obvious peak value. A multi-scale idea is added to the above-mentioned algorithm. Firstly, a primary association is conducted to select several alternative tracks by a low-threshold .Then, alternative tracks are processed by multi-scale clustering methods , through which accurate numbers and parameters of tracks are figured out automatically by means of transforming scale parameters. The first three frames are processed by this algorithm in order to get the first three targets of the track , and then two slightly different gate radius are worked out , mean value of which is used to be the global threshold value of correlation. Moreover, a new model for curvilinear equation correction is applied to the above-mentioned track initiation algorithm for purpose of solving the problem of shape distortion when a space three-dimensional curve is mapped to a two-dimensional bearing-only space. Using sideways-flying, launch and landing as examples to build models and simulate, the application of the proposed approach in simulation proves its effectiveness , accuracy , and adaptivity of correlation threshold selection.