scholarly journals A Sparse Representation Method for a Priori Target Signature Optimization in Hyperspectral Target Detection

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 3408-3424 ◽  
Author(s):  
Ting Wang ◽  
Hongsheng Zhang ◽  
Hui Lin ◽  
Xiuping Jia
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
A Priori ◽  
Representation Method

scholarly journals Joint Hapke Model and Spatial Adaptive Sparse Representation with Iterative Background Purification for Martian Serpentine Detection

2021 ◽  
Vol 13 (3) ◽  
pp. 500
Author(s):  
Xing Wu ◽  
Xia Zhang ◽  
John Mustard ◽  
Jesse Tarnas ◽  
Honglei Lin ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Energy Minimization ◽  
Infrared Imaging ◽  
Hyperspectral Image ◽  
Matched Filter ◽  
Laboratory Data ◽  
Robust Detection ◽  
Spatially Adaptive ◽  
Representation Method

Visible and infrared imaging spectroscopy have greatly revolutionized our understanding of the diversity of minerals on Mars. Characterizing the mineral distribution on Mars is essential for understanding its geologic evolution and past habitability. The traditional handcrafted spectral index could be ambiguous as it may denote broad mineralogical classes, making this method unsuitable for definitive mineral investigation. In this work, the target detection technique is introduced for specific mineral mapping. We have developed a new subpixel mineral detection method by joining the Hapke model and spatially adaptive sparse representation method. Additionally, an iterative background dictionary purification strategy is proposed to obtain robust detection results. Laboratory hyperspectral image containing Mars Global Simulant and serpentine mixtures was used to evaluate and tailor the proposed method. Compared with the conventional target detection algorithms, including constrained energy minimization, matched filter, hierarchical constrained energy minimization, sparse representation for target detection, and spatially adaptive sparse representation method, the proposed algorithm has a significant improvement in accuracy about 30.14%, 29.67%, 29.41%, 9.13%, and 8.17%, respectively. Our algorithm can detect subpixel serpentine with an abundance as low as 2.5% in laboratory data. Then the proposed algorithm was applied to two well-studied Compact Reconnaissance Imaging Spectrometer for Mars images, which contain serpentine outcrops. Our results are not only consistent with the spatial distribution of Fe/Mg phyllosilicates derived by spectral indexes, but also denote what the specific mineral is. Experimental results show that the proposed algorithm enables the search for subpixel, low-abundance, and scientifically valuable mineral deposits.

Enhanced covariances matrix sparse representation method for DOA estimation

2015 ◽  
Vol 51 (16) ◽  
pp. 1288-1290 ◽  
Author(s):  
Wei Cui ◽  
Tong Qian ◽  
Jing Tian
Keyword(s):  
Sparse Representation ◽  
Doa Estimation ◽  
Representation Method

A novel sparse representation method based on virtual samples for face recognition

2012 ◽  
Vol 24 (3-4) ◽  
pp. 513-519 ◽  
Author(s):  
Deyan Tang ◽  
Ningbo Zhu ◽  
Fu Yu ◽  
Wei Chen ◽  
Ting Tang
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Virtual Samples ◽  
Representation Method

Integrating Sparse and Collaborative Representation Classifications for Image Classification

2017 ◽  
Vol 17 (02) ◽  
pp. 1750007 ◽  
Author(s):  
Chunwei Tian ◽  
Guanglu Sun ◽  
Qi Zhang ◽  
Weibing Wang ◽  
Teng Chen ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Linear Combination ◽  
Image Recognition ◽  
Test Sample ◽  
Collaborative Representation ◽  
Training Samples ◽  
Sparse Representation Classification ◽  
The Difference ◽  
Representation Method

Collaborative representation classification (CRC) is an important sparse method, which is easy to carry out and uses a linear combination of training samples to represent a test sample. CRC method utilizes the offset between representation result of each class and the test sample to implement classification. However, the offset usually cannot well express the difference between every class and the test sample. In this paper, we propose a novel representation method for image recognition to address the above problem. This method not only fuses sparse representation and CRC method to improve the accuracy of image recognition, but also has novel fusion mechanism to classify images. The implementations of the proposed method have the following steps. First of all, it produces collaborative representation of the test sample. That is, a linear combination of all the training samples is first determined to represent the test sample. Then, it gets the sparse representation classification (SRC) of the test sample. Finally, the proposed method respectively uses CRC and SRC representations to obtain two kinds of scores of the test sample and fuses them to recognize the image. The experiments of face recognition show that the combination of CRC and SRC has satisfactory performance for image classification.

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
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