Anomaly Detection in Hyperspectral Imagery Based on Spectral Gradient and LLE

2011 ◽  
Vol 121-126 ◽  
pp. 720-724
Author(s):  
Liang Liang Wang ◽  
Zhi Yong Li ◽  
Ji Xiang Sun
Keyword(s):  
Anomaly Detection ◽  
Euclidean Distance ◽  
Hyperspectral Imagery ◽  
Detection Algorithm ◽  
Hyperspectral Data ◽  
Experimental Results ◽  
Feature Analysis ◽  
Detection Problem ◽  
Local Linear Embedding ◽  
Linear Embedding

The local linear embedding algorithm(LLE) is applied into the anomaly detection algorithm on the basis of the feature analysis of the hyperspectral data. Then, to deal with the problem of declining capacity of identifying the neighborhood caused by the Euclidean distance, an improved LLE algorithm is developed. The improved LLE algorithm selects neighborhood pixels according to the spectral gradient, thus making the anomaly detection more robust to the changes of light and terrain. Experimental results prove the feasibility of using LLE algorithm to solve the anomaly detection problem, and the effectiveness of the algorithm in improving the detection performance.

New Measure Based Manifold Algorithm and Application in Anomaly Detection of Hyperspectral Imagery

2011 ◽  
Vol 80-81 ◽  
pp. 797-803
Author(s):  
Liang Liang Wang ◽  
Zhi Yong Li ◽  
Ji Xiang Sun ◽  
Chun Du
Keyword(s):  
Anomaly Detection ◽  
Manifold Learning ◽  
High Dimension ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Detection Performance ◽  
Hyperspectral Data ◽  
Experimental Results ◽  
Nonlinear Structure ◽  
Spectral Angle

Hyperspectral data is endowed with characteristics of intrinsic nonlinear structure and high dimension. In this paper, a nonlinear manifold learning algorithm - ISOMAP is applied to anomaly detection. Then an improved ISOMAP algorithm is developed based on the analysis of the inherent characteristics of hyperspectral imagery. The improved ISOMAP algorithm selects neighborhood according to a novel measure of combination of spectral gradient and spectral angle in order to make the algorithm more robust to the changes of light and terrain. Experimental results prove the effectiveness of the algorithm in improving the detection performance.

scholarly journals Hyperspectral Anomaly Detection via Dictionary Construction-Based Low-Rank Representation and Adaptive Weighting

2019 ◽  
Vol 11 (2) ◽  
pp. 192 ◽  
Author(s):  
Yixin Yang ◽  
Jianqi Zhang ◽  
Shangzhen Song ◽  
Delian Liu
Keyword(s):  
Anomaly Detection ◽  
Detection Algorithm ◽  
Hyperspectral Data ◽  
Low Rank ◽  
Weighting Matrix ◽  
Adaptive Weighting ◽  
Background Space ◽  
Low Rank Representation ◽  
Construction Strategy ◽  
Response Output

Anomaly detection (AD), which aims to distinguish targets with significant spectral differences from the background, has become an important topic in hyperspectral imagery (HSI) processing. In this paper, a novel anomaly detection algorithm via dictionary construction-based low-rank representation (LRR) and adaptive weighting is proposed. This algorithm has three main advantages. First, based on the consistency with AD problem, the LRR is employed to mine the lowest-rank representation of hyperspectral data by imposing a low-rank constraint on the representation coefficients. Sparse component contains most of the anomaly information and can be used for anomaly detection. Second, to better separate the sparse anomalies from the background component, a background dictionary construction strategy based on the usage frequency of the dictionary atoms for HSI reconstruction is proposed. The constructed dictionary excludes possible anomalies and contains all background categories, thus spanning a more reasonable background space. Finally, to further enhance the response difference between the background pixels and anomalies, the response output obtained by LRR is multiplied by an adaptive weighting matrix. Therefore, the anomaly pixels are more easily distinguished from the background. Experiments on synthetic and real-world hyperspectral datasets demonstrate the superiority of our proposed method over other AD detectors.

Research on Second Distribution of Real Value Detectors Based on Heuristic Movement

2014 ◽  
Vol 1049-1050 ◽  
pp. 1506-1513
Author(s):  
Tian Bo Wang ◽  
Feng Bin Zhang ◽  
Chun He Xia
Keyword(s):  
Theoretical Analysis ◽  
Anomaly Detection ◽  
Heuristic Algorithm ◽  
Negative Selection ◽  
Optimal Solution ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Generalization Ability ◽  
Real Value ◽  
Omission Rate

Traditional anomaly detection algorithm has improved to some degree the mechanism of negative selection. However, there still remain many problems such as the randomness of detector generation, incompleteness of self-set and the generalization ability of detectors, which would cause a lot of loopholes in non-self-space. This paper proposes a heuristic algorithm based on the second distribution of real value detectors for the remains of loopholes of the non-self-space in the first distribution. The algorithm proposed can distribute real value detectors through omission data based on the methods of partition and movement. A method is then proposed to solve the problem on how to get the optimal solution to the parameters related in the algorithm. Theoretical analysis and experimental results prove the universality and effectiveness of the method. It is found that the algorithm can effectively avoid the generation of loopholes and thus reduce the omission rate of detector sets.

Fast Anomaly Detection Algorithm for Hyperspectral Imagery Based on Line-by-line Processing

2017 ◽  
Vol 46 (4) ◽  
pp. 410003 ◽  
Author(s):  
付立婷 FU Li-ting ◽  
邓河 DENG He ◽  
刘春红 LIU Chun-hong
Keyword(s):  
Anomaly Detection ◽  
Hyperspectral Imagery ◽  
Detection Algorithm ◽  
On Line

Improved weighted local linear embedding algorithm based on Laplacian eigenmaps

2021 ◽  
Vol 24 (4) ◽  
pp. 323-330
Author(s):  
Qing Wu ◽  
Rongrong Jing ◽  
En Wang
Keyword(s):  
Theoretical Analysis ◽  
Numerical Experiments ◽  
Euclidean Distance ◽  
Geodesic Distance ◽  
Recognition Rate ◽  
Laplacian Eigenmaps ◽  
Manifold Structure ◽  
Local Linear Embedding ◽  
Local Linear ◽  
Linear Embedding

To solve the shortcomings of local linear embedding (LLE), such as sensitive to noise and poor generalization ability for new samples, an improved weighted local linear embedding algorithm based on Laplacian eigenmaps (IWLLE-LE) is proposed in this paper. In the proposed algorithm, Laplacian eigenmaps are used to reconstruct the objective function of dimensionality reduction. The weights of it are introduced by combining the geodesic distance with Euclidean distance, which can effectively represent the manifold structure of nonlinear data. Compared the existing LLE algorithm, the proposed one better maintains the original manifold structure of the data. The merit of the proposal is enhanced by the theoretical analysis and numerical experiments, where the classification recognition rate is 2%–8% higher than LLE.

Anomaly detection algorithm for hyperspectral imagery based on summation of spectral angles

2013 ◽  
Vol 6 (3) ◽  
pp. 325-331
Author(s):  
杜小平 DU Xiao-ping ◽  
刘明 LIU Ming ◽  
夏鲁瑞 XIA Lu-rui ◽  
陈杭 CHEN Hang
Keyword(s):  
Anomaly Detection ◽  
Hyperspectral Imagery ◽  
Detection Algorithm
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