A Nonlinear Sparse Representation-Based Binary Hypothesis Model for Hyperspectral Target Detection

2015 ◽  
Vol 8 (6) ◽  
pp. 2513-2522 ◽  
Author(s):  
Yuxiang Zhang ◽  
Liangpei Zhang ◽  
Bo Du ◽  
Shugen Wang
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Hypothesis Model ◽  
Binary Hypothesis

scholarly journals Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yidong Tang ◽  
Shucai Huang ◽  
Aijun Xue
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Kernel Method ◽  
Matching Pursuit ◽  
Classification Problem ◽  
Kernel Space ◽  
Hypothesis Model ◽  
Extended Surface ◽  
Number Of Classes ◽  
Binary Hypothesis

The sparse representation based classifier (SRC) and its kernel version (KSRC) have been employed for hyperspectral image (HSI) classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH) model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP) algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.

Robust infrared small target detection via non-negativity constraint-based sparse representation

2016 ◽  
Vol 55 (27) ◽  
pp. 7604 ◽  
Author(s):  
Minjie Wan ◽  
Guohua Gu ◽  
Weixian Qian ◽  
Kan Ren ◽  
Qian Chen
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Infrared Small Target

Dim moving target detection algorithm based on spatio-temporal classification sparse representation

2014 ◽  
Vol 67 ◽  
pp. 273-282 ◽  
Author(s):  
Zhengzhou Li ◽  
Zhen Dai ◽  
Hongxia Fu ◽  
Qian Hou ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Detection Algorithm ◽  
Moving Target ◽  
Moving Target Detection ◽  
Spatio Temporal

Speech Enhancement Using Modified MMSE-LSA and Phase Reconstruction in Voiced and Unvoiced Speech

2018 ◽  
Vol 33 (02) ◽  
pp. 1958002 ◽  
Author(s):  
Hairong Jia ◽  
Weimei Wang ◽  
Dong Wang ◽  
Xueying Zhang
Keyword(s):  
Reconstruction Method ◽  
Phase Reconstruction ◽  
Improved Method ◽  
Amplitude Estimation ◽  
Hypothesis Model ◽  
Simulation Results ◽  
Geometric Relationship ◽  
New Phase ◽  
Binary Hypothesis ◽  
Better Than

Aiming at the problem of auditory negative enhancement of typical phase reconstruction method, an improved method of phase reconstruction and MMSE-LSA estimation is proposed. First, the geometric relationship between noisy speech and clean speech in unvoiced segment is used to estimate the phase of the clean speech; Second, considering the randomness of speech appearance in the actual noise environment, a modified MMSE-LSA amplitude estimation is proposed by using the binary hypothesis model. Finally, the new phase reconstruction in voiced and unvoiced speech is combined with the modified MMSE-LSA. The simulation results show that the performance of the algorithm proposed in this paper is better than typical phase reconstruction method in terms of the SegSNR and PESQ.

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