Military object detection using multiple information extracted from hyperspectral imagery

Object detection is a very significant task for a huge range of applications. For example, the detection of military vehicles is very useful for the defense and intelligence. In recent years, hyperspectral imagery (HSI) which is generated by remote sensing systems can provide tremendous information about the spectral characteristics. Due to this characteristic, object detection using HSI becomes hot research topic. In this paper, we propose a strategy for military object detection by extracting multiple information from HSI. Firstly, we generate the superpixels from HSI by principle component analysis (PCA) and k-means clustering. Then, self-similarity method is used to calculate the correlation between each superpixel and the object spectral. At last, the shape information is extracted from the masses which have high correlation value and is used to detect the specific military objectives. Results from HSI demonstrate the benefits of the proposed strategy regarding its effectiveness at detecting specific objectives.

[1]  秦翰林.  Infrared small moving target detection using sparse representation-based image decomposition , 2016 .

[2]  Antonio J. Plaza,et al.  Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Qian Du,et al.  A comparative study for orthogonal subspace projection and constrained energy minimization , 2003, IEEE Trans. Geosci. Remote. Sens..

[4]  Sumit Srivastava,et al.  Domain Adaptation for Automatic OLED Panel Defect Detection Using Adaptive Support Vector Data Description , 2017, International Journal of Computer Vision.

[5]  Pierrick Coupé,et al.  SuperPatchMatch: An Algorithm for Robust Correspondences Using Superpixel Patches , 2017, IEEE Transactions on Image Processing.

[6]  Yongzhao Zhan,et al.  Hyperspectral image classification using multilayer superpixel graph and loopy belief propagation , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[7]  Jingdong Wang,et al.  Salient Object Detection: A Discriminative Regional Feature Integration Approach , 2013, International Journal of Computer Vision.

[8]  Wei Wei,et al.  Salient object detection in hyperspectral imagery using spectral gradient contrast , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[9]  Michael W. Kudenov,et al.  Review of snapshot spectral imaging technologies , 2013, Optics and Precision Engineering.

[10]  Hasan S. Bilge,et al.  Content based image retrieval with sparse representations and local feature descriptors : A comparative study , 2017, Pattern Recognit..

[11]  Lu Wang,et al.  Extraction of Moving Objects From Their Background Based on Multiple Adaptive Thresholds and Boundary Evaluation , 2010, IEEE Transactions on Intelligent Transportation Systems.

[12]  Antonio Torralba,et al.  Visualizing Object Detection Features , 2015, International Journal of Computer Vision.

[13]  Jun Zhou,et al.  Salient object detection in hyperspectral imagery , 2013, 2013 IEEE International Conference on Image Processing.

[14]  Bo Du,et al.  A Nonlinear Sparse Representation-Based Binary Hypothesis Model for Hyperspectral Target Detection , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Jiandong Tian,et al.  RGBD Salient Object Detection via Deep Fusion , 2016, IEEE Transactions on Image Processing.

[16]  Yongjun Zhang,et al.  A novel spatio-temporal saliency approach for robust dim moving target detection from airborne infrared image sequences , 2016, Inf. Sci..

[17]  Chein-I. Chang.  Hyperspectral Imaging: Techniques for Spectral Detection and Classification , 2003 .