Infrared Small Target Detection Using a Temporal Variance and Spatial Patch Contrast Filter

Gao, Jinyan; Lin, Zaiping; An, Wei

doi:10.1109/access.2019.2903808

Cited by 36 publications

(24 citation statements)

References 46 publications

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“…where T (x, y) represents the target component, B (x, y) denotes the background component, and N (x, y) is the noise component. A dim and small target component T (x, y) usually occupies only a few pixels in an IR image [47]. A representative small target and complex background are given in Fig.…”

Section: Ir Image Modelmentioning

confidence: 99%

Adaptive Background Suppression Method Based on Intelligent Optimization for IR Small Target Detection Under Complex Cloud Backgrounds

Ren

Wang

et al. 2020

IEEE Access

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To improve the detection of dim and small targets in infrared (IR) images containing highintensity cloud clutter, a novel adaptive background suppression method is proposed. By using threedimensional cooperative filtering and differential calculation, the different and complex background clutter is suppressed. To obtain the optimal parameters for the background suppression algorithm, an adaptive parameter optimization method is proposed. The adaptive parameter optimization problem is transformed into a multiobjective optimization problem in which the signal-to-clutter ratio gain and background suppression factor, which effectively reflect the background suppression performance, are chosen as the optimization objectives, and the parameters of the proposed background suppression algorithm are considered as the variables. To effectively solve the established multiobjective optimization problem, a particle swarm parameter optimization-based method is utilized. Experimental results indicate that the proposed adaptive background suppression method using these optimal parameters has good performance for IR images in real complex scenes, as well as performance superior to that of other baseline methods. INDEX TERMS Background suppression, infrared (IR) images, three-dimensional cooperative filtering, multiobjective optimization algorithm.

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Section: Ir Image Modelmentioning

confidence: 99%

Adaptive Background Suppression Method Based on Intelligent Optimization for IR Small Target Detection Under Complex Cloud Backgrounds

Ren

Wang

et al. 2020

IEEE Access

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“…In [48], Haykin et al applied the concept of timefrequency analysis by performing feature extraction and pattern classification to assist small target detection under dynamic background condition. In their approach authors [49] performed time-frequency analysis using Wigner-Ville distribution (WVD) by transforming echoes signal into a timefrequency image (time-varying nature of the received signal's spectral content of the iceberg). In addition, authors applied Hannning window function with fourier transforms to detect moving object in a sea clutter.…”

Section: Related Workmentioning

confidence: 99%

Hanning Weighted Window Analysis Assisted Time-Series Analysis Model for Slow Moving Target Detection in Sea Clutter

Rajesh¹,

Udayarani²,

Jayaramaiah³

2020

International Journal of Engineering Research and Technology

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The exponential rise in maritime movement, security threats from up-surging terrorism and smuggling has alarmed academia-industries to develop more efficient method for target detection in coastal area. This task becomes more complex and intricate in case of small moving target in seaclutter. Amongst the major available approaches such as multiple-radar based methods, wavelet analysis methods, Doppler measurements, Fourier transforms, Short Term Fourier Transform (STFT) based techniques, STFT has been found more robust. The ability to perform simultaneous time and frequency analysis for time-series assessment enables STFT to be used in small target detection in sea clutter; however the inherent limitations such as improper Windowing, window-size, number of samples, overlapping conditions affect overall performance. Considering it as motivation, in this paper we have developed an efficient Hanning Weighted Window Function (HWWF) model to be used in conjunction with STFT to perform Hanning-Weighted Overlapped Time-Series Analysis (HWOTSA) to detect slow moving target detection in sea clutter. The use of orthonormal transformation also called rotation enabled Hanning Window Analysis to use suitable STFT parameters and statistical test across the windows to achieve better accuracy of the target detection. HWOTSA assisted STFT in conjunction with the Probability Distribution Function Projection (PPM) over extracted features enabled accurate slow moving target detection in sea clutter. The MATLAB based simulation affirmed that the proposed method enables accurate and swift (slow) moving target detection in sea clutter, without introducing higher computational overheads and complexities. The estimation of Doppler measurement based target velocity estimation armour proposed method to be used in real-time applications for swift decision making.

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“…Many different approaches have been proposed for infrared object tracking such as saliency extraction [9], multiscale patch-based contrast measure and a temporal variance filter [14], feature learning and fusion, reliability weight estimation based on nonnegative matrix factorization [15], Poisson reconstruction and the Dempster-Shafer theory [16], three-dimensional scalar field [17], a double-layer region proposal network (RPN) [18], Siamese convolution network [19], a mixture of Gaussians with modified flux density [20], spatial-temporal total variation regularization and weighted tensor [21], two-stage U-skip context aggregation network [22], histogram similarity map based on the Epanechnikov kernel function [23], quaternion discrete cosine transform [24], non-convex optimization [25], Mexican-hat distribution of pixels [26], and Schatten regularization with reweighted sparse enhancement [27].…”

Section: Introductionmentioning

confidence: 99%

“…The comparison of the classification results of different algorithms used in this paper is shown in Figure 6. We compare the proposed algorithm with three image classification algorithms: convolutional neural network (CNN) [14], multi-class Support Vector Machine (SVM) from LIBSVM [54], and multi-label lazy KNN (ML-KNN) [55] in terms of operation speed and classification accuracy. The comparison of the classification results of different algorithms used in this paper is shown in Figure 6.…”

mentioning

confidence: 99%

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

Zhu

Lou

et al. 2020

Symmetry

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Infrared image recognition technology can work day and night and has a long detection distance. However, the infrared objects have less prior information and external factors in the real-world environment easily interfere with them. Therefore, infrared object classification is a very challenging research area. Manifold learning can be used to improve the classification accuracy of infrared images in the manifold space. In this article, we propose a novel manifold learning algorithm for infrared object detection and classification. First, a manifold space is constructed with each pixel of the infrared object image as a dimension. Infrared images are represented as data points in this constructed manifold space. Next, we simulate the probability distribution information of infrared data points with the Gaussian distribution in the manifold space. Then, based on the Gaussian distribution information in the manifold space, the distribution characteristics of the data points of the infrared image in the low-dimensional space are derived. The proposed algorithm uses the Kullback-Leibler (KL) divergence to minimize the loss function between two symmetrical distributions, and finally completes the classification in the low-dimensional manifold space. The efficiency of the algorithm is validated on two public infrared image data sets. The experiments show that the proposed method has a 97.46% classification accuracy and competitive speed in regards to the analyzed data sets.

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Infrared Small Target Detection Using a Temporal Variance and Spatial Patch Contrast Filter

Cited by 36 publications

References 46 publications

Adaptive Background Suppression Method Based on Intelligent Optimization for IR Small Target Detection Under Complex Cloud Backgrounds

Adaptive Background Suppression Method Based on Intelligent Optimization for IR Small Target Detection Under Complex Cloud Backgrounds

Hanning Weighted Window Analysis Assisted Time-Series Analysis Model for Slow Moving Target Detection in Sea Clutter

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

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