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.

scholarly journals Adaptive Iterated Shrinkage Thresholding-Based Lp-Norm Sparse Representation for Hyperspectral Imagery Target Detection

2020 ◽  
Vol 12 (23) ◽  
pp. 3991
Author(s):  
Xiaobin Zhao ◽  
Wei Li ◽  
Mengmeng Zhang ◽  
Ran Tao ◽  
Pengge Ma
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Matched Filter ◽  
Hyperspectral Imagery ◽  
Low Rank ◽  
Collaborative Representation ◽  
Support Vector ◽  
L1 Norm ◽  
Lp Norm ◽  
Norm Minimization

In recent years, with the development of compressed sensing theory, sparse representation methods have been concerned by many researchers. Sparse representation can approximate the original image information with less space storage. Sparse representation has been investigated for hyperspectral imagery (HSI) detection, where approximation of testing pixel can be obtained by solving l1-norm minimization. However, l1-norm minimization does not always yield a sufficiently sparse solution when a dictionary is not large enough or atoms present a certain level of coherence. Comparatively, non-convex minimization problems, such as the lp penalties, need much weaker incoherence constraint conditions and may achieve more accurate approximation. Hence, we propose a novel detection algorithm utilizing sparse representation with lp-norm and propose adaptive iterated shrinkage thresholding method (AISTM) for lp-norm non-convex sparse coding. Target detection is implemented by representation of the all pixels employing homogeneous target dictionary (HTD), and the output is generated according to the representation residual. Experimental results for four real hyperspectral datasets show that the detection performance of the proposed method is improved by about 10% to 30% than methods mentioned in the paper, such as matched filter (MF), sparse and low-rank matrix decomposition (SLMD), adaptive cosine estimation (ACE), constrained energy minimization (CEM), one-class support vector machine (OC-SVM), the original sparse representation detector with l1-norm, and combined sparse and collaborative representation (CSCR).

Fast high-order matched filter for hyperspectral image target detection

2018 ◽  
Vol 94 ◽  
pp. 151-155 ◽  
Author(s):  
Shuo Yang ◽  
Ziyang Song ◽  
Hongyi Yuan ◽  
Zhengxia Zou ◽  
Zhenwei Shi
Keyword(s):  
Target Detection ◽  
Hyperspectral Image ◽  
Matched Filter ◽  
High Order

scholarly journals Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5559
Author(s):  
Na Li ◽  
Ruihao Wang ◽  
Huijie Zhao ◽  
Mingcong Wang ◽  
Kewang Deng ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Infrared Imaging ◽  
Hyperspectral Image ◽  
Matching Pursuit ◽  
Small Sample Size ◽  
Small Sample ◽  
Hyperspectral Data ◽  
Classification Method ◽  
Diverse Density

To solve the small sample size (SSS) problem in the classification of hyperspectral image, a novel classification method based on diverse density and sparse representation (NCM_DDSR) is proposed. In the proposed method, the dictionary atoms, which learned from the diverse density model, are used to solve the noise interference problems of spectral features, and an improved matching pursuit model is presented to obtain the sparse coefficients. Airborne hyperspectral data collected by the push-broom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS) are applied to evaluate the performance of the proposed classification method. Results illuminate that the overall accuracies of the proposed model for classification of PHI and AVIRIS images are up to 91.59% and 92.83% respectively. In addition, the kappa coefficients are up to 0.897 and 0.91.

scholarly journals Anomaly targets detection of hyperspectral imagery based on wavelet transform and sparse representation

2018 ◽  
Vol 232 ◽  
pp. 02054
Author(s):  
Cheng Baozhi
Keyword(s):  
Wavelet Transform ◽  
Sparse Representation ◽  
Target Detection ◽  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Image Data ◽  
Detection Algorithm ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Low Efficiency

The research of anomaly target detection algorithm in hyperspectral imagery is a hot issue, which has important research value. In order to overcome low efficiency of current anomaly target detection in hyperspectral image, an anomaly detection algorithm for hyperspectral images based on wavelet transform and sparse representation was proposed. Firstly, two-dimensional discrete wavelet transform is used to denoise the hyperspectral image, and the new hyperspectral image data are obtained. Then, the results of anomaly target detection are obtained by using sparse representation theory. The real AVIRIS hyperspectral imagery data sets are used in the experiments. The results show that the detection accuracy and false alarm rate of the propoesd algorithm are better than RX and KRX algorithm.

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 Ensemble-Based Cascaded Constrained Energy Minimization for Hyperspectral Target Detection

2019 ◽  
Vol 11 (11) ◽  
pp. 1310 ◽  
Author(s):  
Rui Zhao ◽  
Zhenwei Shi ◽  
Zhengxia Zou ◽  
Zhou Zhang
Keyword(s):  
Target Detection ◽  
Energy Minimization ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Least Square ◽  
Learning System ◽  
Strong Nonlinearity ◽  
Generalization Ability ◽  
Detection Algorithms ◽  
Ensemble Techniques

Ensemble learning is an important group of machine learning techniques that aim to enhance the nonlinearity and generalization ability of a learning system by aggregating multiple learners. We found that ensemble techniques show great potential for improving the performance of traditional hyperspectral target detection algorithms, while at present, there are few previous works have been done on this topic. To this end, we propose an Ensemble based Constrained Energy Minimization (E-CEM) detector for hyperspectral image target detection. Classical hyperspectral image target detection algorithms like Constrained Energy Minimization (CEM), matched filter (MF) and adaptive coherence/cosine estimator (ACE) are usually designed based on constrained least square regression methods or hypothesis testing methods with Gaussian distribution assumption. However, remote sensing hyperspectral data captured in a real-world environment usually shows strong nonlinearity and non-Gaussianity, which will lead to performance degradation of these classical detection algorithms. Although some hierarchical detection models are able to learn strong nonlinear discrimination of spectral data, due to the spectrum changes, these models usually suffer from the instability in detection tasks. The proposed E-CEM is designed based on the classical CEM detection algorithm. To improve both of the detection nonlinearity and generalization ability, the strategies of “cascaded detection”, “random averaging” and “multi-scale scanning” are specifically designed. Experiments on one synthetic hyperspectral image and two real hyperspectral images demonstrate the effectiveness of our method. E-CEM outperforms the traditional CEM detector and other state-of-the-art detection algorithms. Our code will be made publicly available.

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