An infrared dim target detection algorithm based on density peak search and region consistency

To suppress background clutter and improve detection accuracy, we propose a dim target detection algorithm based on density peak search and region consistency. A density peak search algorithm is first applied to extract candidate targets, and these are then classified and marked according to the local mosaic probability factor, which is important in order to suppress the backgroundsssss clutter and accurately strip the candidate target region from the background. Based on the regional stability of the dim targets, local mosaic gradient factors are used to screen real targets from candidates, and a facet kernel filter is used to extract the irregular contours of dim targets with the aim of enhancing them. Our experimental results show that compared with existing algorithms, the proposed method has better detection accuracy and robustness in various complex scenarios.

[1]  Yantao Wei,et al.  Multiscale patch-based contrast measure for small infrared target detection , 2016, Pattern Recognit..

[2]  Yuan Yan Tang,et al.  A Local Contrast Method for Small Infrared Target Detection , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Ding Yuan,et al.  Infrared small target detection based on local intensity and gradient properties , 2018 .

[4]  Ping Zhang,et al.  Infrared Small Target Detection Based on Spatial-Temporal Enhancement Using Quaternion Discrete Cosine Transform , 2019, IEEE Access.

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

[6]  Phillip Stanley-Marbell,et al.  Deriving Equations from Sensor Data Using Dimensional Function Synthesis , 2019 .

[7]  Mahdi Nasiri,et al.  Infrared small target enhancement based on variance difference , 2017 .

[8]  Guoyin Wang,et al.  Granular computing: from granularity optimization to multi-granularity joint problem solving , 2016, Granular Computing.

[9]  Lorenzo Bruzzone,et al.  Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Zhenming Peng,et al.  Infrared Small Target Detection by Density Peaks Searching and Maximum-Gray Region Growing , 2019, IEEE Geoscience and Remote Sensing Letters.

[11]  Xiaobo Hu,et al.  In-frame and inter-frame information based infrared moving small target detection under complex cloud backgrounds , 2016 .

[12]  Chaoqun Xia,et al.  Infrared Small Target Detection via Modified Random Walks , 2018, Remote. Sens..

[13]  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.

[14]  Zhenming Peng,et al.  Infrared Small Target Detection via Non-Convex Rank Approximation Minimization Joint l2, 1 Norm , 2018, Remote. Sens..

[15]  Sheng-Li Sun,et al.  Space moving target detection and tracking method in complex background , 2018, Infrared Physics & Technology.

[16]  James R. Zeidler,et al.  Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data , 1993, IEEE Trans. Image Process..

[17]  Xin Zhou,et al.  Infrared small-target detection using multiscale gray difference weighted image entropy , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[18]  Lorenzo Bruzzone,et al.  Infrared Small Target Detection Based on Facet Kernel and Random Walker , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Yiquan Wu,et al.  Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  Zhang Peng,et al.  The design of Top-Hat morphological filter and application to infrared target detection , 2006 .

[21]  Tianxu Zhang,et al.  Robust contact-point detection from pantograph-catenary infrared images by employing horizontal-vertical enhancement operator , 2019 .

[22]  Xiaokang Wang,et al.  NQA , 2019, ACM Trans. Embed. Comput. Syst..

[23]  Shengxiang Qi,et al.  A fast-saliency method for real-time infrared small target detection , 2016 .

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

[25]  Yantao Wei,et al.  An infrared small target detection method based on multiscale local homogeneity measure , 2018 .