Small target detection using bilateral filter and temporal cross product in infrared images

Abstract We introduce a spatial and temporal target detection method using spatial bilateral filter (BF) and temporal cross product (TCP) of temporal pixels in infrared (IR) image sequences. At first, the TCP is presented to extract the characteristics of temporal pixels by using temporal profile in respective spatial coordinates of pixels. The TCP represents the cross product values by the gray level distance vector of a current temporal pixel and the adjacent temporal pixel, as well as the horizontal distance vector of the current temporal pixel and a temporal pixel corresponding to potential target center. The summation of TCP values of temporal pixels in spatial coordinates makes the temporal target image (TTI), which represents the temporal target information of temporal pixels in spatial coordinates. And then the proposed BF filter is used to extract the spatial target information. In order to predict background without targets, the proposed BF filter uses standard deviations obtained by an exponential mapping of the TCP value corresponding to the coordinate of a pixel processed spatially. The spatial target image (STI) is made by subtracting the predicted image from the original image. Thus, the spatial and temporal target image (STTI) is achieved by multiplying the STI and the TTI, and then targets finally are detected in STTI. In experimental result, the receiver operating characteristics (ROC) curves were computed experimentally to compare the objective performance. From the results, the proposed algorithm shows better discrimination of target and clutters and lower false alarm rates than the existing target detection methods.

[1]  Meng Hwa Er,et al.  Max-mean and max-median filters for detection of small targets , 1999, Optics & Photonics.

[2]  Sun-Gu Sun,et al.  Target detection using local fuzzy thresholding and binary template matching in forward-looking infrared images , 2007 .

[3]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[4]  Fei Zhang,et al.  Detecting and tracking dim moving point target in IR image sequence , 2005 .

[5]  Dana H. Brooks,et al.  Detecting small moving objects using temporal hypothesis testing , 2002 .

[6]  John K. Goutsias,et al.  Automatic target detection and tracking in forward-looking infrared image sequences using morphological connected operators , 2004, J. Electronic Imaging.

[7]  M. Skolnik,et al.  Introduction to Radar Systems , 2021, Advances in Adaptive Radar Detection and Range Estimation.

[8]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[9]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[10]  Jiaxiong Peng,et al.  An extended track-before-detect algorithm for infrared target detection , 1997 .

[11]  N. Obuchowski Receiver operating characteristic curves and their use in radiology. , 2003, Radiology.

[12]  Wei Ying,et al.  A small target detection algorithm based on multi-scale energy cross , 2003, IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003.

[13]  M. Pepe The Statistical Evaluation of Medical Tests for Classification and Prediction , 2003 .

[14]  William L. Wolfe,et al.  Introduction to infrared system design , 1996 .

[15]  Dana H. Brooks,et al.  Temporal filters for point target detection in IR imagery , 1997, Defense, Security, and Sensing.

[16]  Xia Mao,et al.  Criterion to Evaluate the Quality of Infrared Small Target Images , 2009 .

[17]  Kyu-Ik Sohng,et al.  Small Target Detection Using Bilateral Filter Based on Edge Component , 2010 .

[18]  A. N. de Jong IRST and its Perspective , 1995 .

[19]  Lei Yang,et al.  Adaptive detection for infrared small target under sea-sky complex background , 2004 .