Infrared small target detection via adaptive M-estimator ring top-hat transformation

Abstract Top-Hat transformation is an essential technology in the field of infrared small target detection. Many modified Top-Hat transformation methods have been proposed based on the different structure of structural elements. However, these methods are still hard to handle the dim targets and complex background. It can be summarized as two reasons, one is that the structural elements cannot suppress the background adaptively due to the fixed value of structural elements in image. Another is that simple structural element cannot utilize the local feature for target enhancement. To overcome these two limitations, a special ring Top-Hat transformation based on M-estimator and local entropy is proposed in this paper. First, an adaptive ring structural element based on M-estimator is used to suppress the complex background. Second, a novel local entropy is proposed to weight structural element for capturing local feature and target enhancement. Finally, a comparison experiment based on massive infrared image data (more than 500 infrared target images) is done. And the results demonstrate that the proposed algorithm acquires better performance compared with some recent methods.

[1]  Xiangzhi Bai,et al.  Hit-or-miss transform based infrared dim small target enhancement , 2011 .

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

[3]  Jue Zhang,et al.  A New Saliency-Driven Fusion Method Based on Complex Wavelet Transform for Remote Sensing Images , 2017, IEEE Geoscience and Remote Sensing Letters.

[4]  Jiefeng Guo,et al.  Analysis of selection of structural element in mathematical morphology with application to infrared point target detection , 2007, SPIE/COS Photonics Asia.

[5]  Jie Ma,et al.  A Robust Directional Saliency-Based Method for Infrared Small-Target Detection Under Various Complex Backgrounds , 2013, IEEE Geoscience and Remote Sensing Letters.

[6]  Xin Zhou,et al.  Entropy-based window selection for detecting dim and small infrared targets , 2017, Pattern Recognit..

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

[8]  Yansheng Li,et al.  Infrared Small Target Detection via Low-Rank Tensor Completion With Top-Hat Regularization , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Xianghai Wang,et al.  A convex active contour model driven by local entropy energy with applications to infrared ship target segmentation , 2017 .

[10]  Deyu Meng,et al.  Infrared small-dim target detection based on Markov random field guided noise modeling , 2018, Pattern Recognit..

[11]  Xiangzhi Bai,et al.  Analysis of different modified top-hat transformations based on structuring element construction , 2010, Signal Process..

[12]  Xiangzhi Bai,et al.  Analysis of new top-hat transformation and the application for infrared dim small target detection , 2010, Pattern Recognit..

[13]  Haibin Duan,et al.  Biological Eagle-Eye-Based Visual Platform for Target Detection , 2018, IEEE Transactions on Aerospace and Electronic Systems.

[14]  Huchuan Lu,et al.  Inverse Sparse Tracker With a Locally Weighted Distance Metric , 2015, IEEE Transactions on Image Processing.

[15]  Johan A. K. Suykens,et al.  Robust Low-Rank Tensor Recovery With Regularized Redescending M-Estimator , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Xin Zhou,et al.  Small Infrared Target Detection Based on Weighted Local Difference Measure , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Yongjun Zhang,et al.  Robust infrared small target detection using local steering kernel reconstruction , 2018, Pattern Recognit..

[18]  Q. Henry Wu,et al.  A pseudo top-hat mathematical morphological approach to edge detection in dark regions , 2002, Pattern Recognit..

[19]  Yu Han,et al.  Edge-Preserving Image Smoothing Via a Total Variation Regularizer and a Nonconvex Regularizer , 2019 .

[20]  Constantine Kotropoulos,et al.  M-estimators for robust multidimensional scaling employing ℓ2, 1 norm regularization , 2018, Pattern Recognit..

[21]  Mengxi Xu,et al.  A sparse representation-based method for infrared dim target detection under sea–sky background , 2015 .

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

[23]  Xiangzhi Bai,et al.  Infrared dim small target enhancement using toggle contrast operator , 2012 .

[24]  Etienne Decencière,et al.  Image filtering using morphological amoebas , 2007, Image Vis. Comput..

[25]  Sungho Kim,et al.  Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track , 2012, Pattern Recognit..