Criterion to Evaluate the Quality of Infrared Small Target Images

In this paper, we propose a new criterion to estimate the quality of infrared small target images. To describe the criterion quantitatively, two indicators are defined. One is the “degree of target being confused” that represents the ability of infrared small target image to provide fake targets. The other one is the “degree of target being shielded”, which reflects the contribution of the image to shield the target. Experimental results reveal that this criterion is more robust than the traditional method (Signal-to-Noise Ratio). It is not only valid to infrared small target images which Signal-to-Noise Ratio could correctly describe, but also to the images that the traditional criterion could not accurately estimate. In addition, the results of this criterion can provide information about the cause of background interfering with target detection.

[1]  Doreen M. Sasaki,et al.  Analysis of the cascade of track-before-detect and track-after-detect tracking algorithms , 1998, Defense, Security, and Sensing.

[2]  Dai Jing-min,et al.  Moving Targets Detection and Tracking Based on Nonlinear Adaptive Filtering , 2007, 2007 International Conference on Computational Intelligence and Security Workshops (CISW 2007).

[3]  Wei Wang,et al.  Wavelet de-noising based on higher order statistics for infrared target detection , 2007, International Symposium on Multispectral Image Processing and Pattern Recognition.

[4]  Lei Yang,et al.  Variance WIE based infrared images processing , 2006 .

[5]  Heggere S. Ranganath,et al.  Single-frame image processing techniques for low-SNR infrared imagery , 2008, SPIE Defense + Commercial Sensing.

[6]  Andrew J. Nevis.  Image characterization and target recognition in the surf zone environment , 1996, Defense, Security, and Sensing.

[7]  Jie Yang,et al.  New criterion to evaluate the complex degree of sea-sky infrared backgrounds , 2005 .

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

[9]  Daniel Lacroix,et al.  Uncooled VOx thermal imaging systems at BAE Systems , 2008, SPIE Defense + Commercial Sensing.

[10]  Wang Hai.  A Fast Algorithm for Two-dimensional Otsu Adaptive Threshold Algorithm , 2007 .

[11]  Zou Yu.  Fast Small Offshore Target Detection Based on Object Region Characteristic , 2005 .

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

[13]  Xubang Shen,et al.  Expanding the usage of the star sensor in spacecraft , 2007, Other Conferences.

[14]  Henry Leung,et al.  Small target detection in clutter using recursive nonlinear prediction , 2000, IEEE Trans. Aerosp. Electron. Syst..

[15]  Peng-Lang Shui,et al.  Method for moving point target detection in image sequences based on directional cumulation , 2007, Other Conferences.

[16]  Sun Wei.  INFRARED TARGET SEGMENTATION ALGORITHM BASED ON MORPHOLOGICAL METHOD , 2004 .

[17]  Wenbo Wu,et al.  Extraction of the land resources in mining area based on TM and INSAR images , 2007, International Symposium on Multispectral Image Processing and Pattern Recognition.

[18]  A. Mahalanobis,et al.  Design and application of quadratic correlation filters for target detection , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[19]  John Barnett,et al.  Statistical Analysis Of Median Subtraction Filtering With Application To Point Target Detection In Infrared Backgrounds , 1989, Photonics West - Lasers and Applications in Science and Engineering.

[20]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[21]  D. Yonovitz.  Tunable wavelet target extraction preprocessor , 2007, SPIE Defense + Commercial Sensing.

[22]  James R. Zeidler,et al.  Enhanced detectability of small objects in correlated clutter using an improved 2-D adaptive lattice algorithm , 1997, IEEE Trans. Image Process..

[23]  Yuan Cao,et al.  Point target detection of infrared images with eigentargets , 2007 .