scholarly journals Hyperspectral Anomaly Detection via Graph Dictionary-Based Low Rank Decomposition with Texture Feature Extraction

2020 ◽  
Vol 12 (23) ◽  
pp. 3966
Author(s):  
Shangzhen Song ◽  
Yixin Yang ◽  
Huixin Zhou ◽  
Jonathan Cheung-Wai Chan
Keyword(s):  
Feature Extraction ◽  
Anomaly Detection ◽  
Sparse Matrix ◽  
Texture Feature ◽  
Low Rank ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Texture Feature Extraction ◽  
Rank Decomposition ◽  
Low Rank Decomposition

The accuracy of anomaly detection in hyperspectral images (HSIs) faces great challenges due to the high dimensionality, redundancy of data, and correlation of spectral bands. In this paper, to further improve the detection accuracy, we propose a novel anomaly detection method based on texture feature extraction and a graph dictionary-based low rank decomposition (LRD). First, instead of using traditional clustering methods for the dictionary, the proposed method employs the graph theory and designs a graph Laplacian matrix-based dictionary for LRD. The robust information of the background matrix in the LRD model is retained, and both the low rank matrix and the sparse matrix are well separated while preserving the correlation of background pixels. To further improve the detection performance, we explore and extract texture features from HSIs and integrate with the low-rank model to obtain the sparse components by decomposition. The detection results from feature maps are generated in order to suppress background components similar to anomalies in the sparse matrix and increase the strength of real anomalies. Experiments were run on one synthetic dataset and three real datasets to evaluate the performance. The results show that the performance of the proposed method yields competitive results in terms of average area under the curve (AUC) for receiver operating characteristic (ROC), i.e., 0.9845, 0.9962, 0.9699, and 0.9900 for different datasets, respectively. Compared with seven other state-of-the-art algorithms, our method yielded the highest average AUC for ROC in all datasets.

Low-rank decomposition on transformed feature maps domain for image denoising

2020 ◽  
Author(s):  
Qiong Luo ◽  
Baichen Liu ◽  
Yang Zhang ◽  
Zhi Han ◽  
Yandong Tang
Keyword(s):  
Image Denoising ◽  
Low Rank ◽  
Feature Maps ◽  
Rank Decomposition ◽  
Low Rank Decomposition

scholarly journals Hyperspectral Anomaly Detection with Harmonic Analysis and Low-Rank Decomposition

2019 ◽  
Vol 11 (24) ◽  
pp. 3028 ◽  
Author(s):  
Pei Xiang ◽  
Jiangluqi Song ◽  
Huan Li ◽  
Lin Gu ◽  
Huixin Zhou
Keyword(s):  
Anomaly Detection ◽  
Harmonic Analysis ◽  
Hyperspectral Image ◽  
Low Rank ◽  
Superior Performance ◽  
Alternative Methods ◽  
Qualitative Assessment ◽  
Redundant Information ◽  
Rank Decomposition ◽  
Low Rank Decomposition

Hyperspectral anomaly detection methods are often limited by the effects of redundant information and isolated noise. Here, a novel hyperspectral anomaly detection method based on harmonic analysis (HA) and low rank decomposition is proposed. This paper introduces three main innovations: first and foremost, in order to extract low-order harmonic images, a single-pixel-related HA was introduced to reduce dimension and remove redundant information in the original hyperspectral image (HSI). Additionally, adopting the guided filtering (GF) and differential operation, a novel background dictionary construction method was proposed to acquire the initial smoothed images suppressing some isolated noise, while simultaneously constructing a discriminative background dictionary. Last but not least, the original HSI was replaced by the initial smoothed images for a low-rank decomposition via the background dictionary. This operation took advantage of the low-rank attribute of background and the sparse attribute of anomaly. We could finally get the anomaly objectives through the sparse matrix calculated from the low-rank decomposition. The experiments compared the detection performance of the proposed method and seven state-of-the-art methods in a synthetic HSI and two real-world HSIs. Besides qualitative assessment, we also plotted the receiver operating characteristic (ROC) curve of each method and report the respective area under the curve (AUC) for quantitative comparison. Compared with the alternative methods, the experimental results illustrated the superior performance and satisfactory results of the proposed method in terms of visual characteristics, ROC curves and AUC values.

Fabric defect detection method based on cascaded low-rank decomposition

2020 ◽  
Vol 32 (4) ◽  
pp. 483-498
Author(s):  
Chunlei Li ◽  
Chaodie Liu ◽  
Zhoufeng Liu ◽  
Ruimin Yang ◽  
Yun Huang
Keyword(s):  
Defect Detection ◽  
Detection Method ◽  
Sparse Matrix ◽  
Saliency Map ◽  
Low Rank ◽  
Content Type ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Rank Decomposition ◽  
Low Rank Decomposition

PurposeThe purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.Design/methodology/approachThis paper proposed a fabric defect detection algorithm based on cascaded low-rank decomposition. First, the constructed Gabor feature matrix is divided into a low-rank matrix and sparse matrix using low-rank decomposition technique, and the sparse matrix is used as priori matrix where higher values indicate a higher probability of abnormality. Second, we conducted the second low-rank decomposition for the constructed texton feature matrix under the guidance of the priori matrix. Finally, an improved adaptive threshold segmentation algorithm was adopted to segment the saliency map generated by the final sparse matrix to locate the defect regions.FindingsThe proposed method was evaluated on the public fabric image databases. By comparing with the ground-truth, the average detection rate of 98.26% was obtained and is superior to the state-of-the-art.Originality/valueThe cascaded low-rank decomposition was first proposed and applied into the fabric defect detection. The quantitative value shows the effectiveness of the detection method. Hence, the proposed method can be used for accurate defect detection and automated analysis system.

Hyperspectral Anomaly Detection via Tensor- Based Endmember Extraction and Low-Rank Decomposition

2020 ◽  
Vol 17 (10) ◽  
pp. 1772-1776 ◽  
Author(s):  
Shangzhen Song ◽  
Huixin Zhou ◽  
Lin Gu ◽  
Yixin Yang ◽  
Yiyi Yang
Keyword(s):  
Anomaly Detection ◽  
Low Rank ◽  
Endmember Extraction ◽  
Rank Decomposition ◽  
Low Rank Decomposition

Hyperspectral Anomaly Detection Through Spectral Unmixing and Dictionary-Based Low-Rank Decomposition

2018 ◽  
Vol 56 (8) ◽  
pp. 4391-4405 ◽  
Author(s):  
Ying Qu ◽  
Wei Wang ◽  
Rui Guo ◽  
Bulent Ayhan ◽  
Chiman Kwan ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Spectral Unmixing ◽  
Low Rank ◽  
Rank Decomposition ◽  
Low Rank Decomposition

Low-rank decomposition-based anomaly detection

2013 ◽  
Author(s):  
Shih-Yu Chen ◽  
Shiming Yang ◽  
Konstantinos Kalpakis ◽  
Chein-I Chang
Keyword(s):  
Anomaly Detection ◽  
Low Rank ◽  
Rank Decomposition ◽  
Low Rank Decomposition

scholarly journals Fabric defect fetection via weighted low-rank decomposition and Laplacian regularization

2020 ◽  
Vol 15 ◽  
pp. 155892502095765
Author(s):  
Xuan Ji ◽  
Jiuzhen Liang ◽  
Lan Di ◽  
Yunfei Xia ◽  
Zhenjie Hou ◽  
...  
Keyword(s):  
Sparse Matrix ◽  
True Positive Rate ◽  
Low Rank ◽  
True Positive ◽  
Decomposition Model ◽  
Fabric Defect ◽  
Positive Rate ◽  
Decomposition Models ◽  
Rank Decomposition ◽  
Low Rank Decomposition

Low-rank decomposition models have potential for fabric defect detection, where a feature matrix is decomposed into a low-rank matrix that corresponding to repeated texture structure and a sparse matrix that represent defective regions. Two limitations, however, still exist. First, previous work might fail to detect some large homogeneous defective block. Second, when the background and defective regions are relatively coherent or the texture of fabric image is complex, it is difficult to use previous methods to separate them. To deal with these problems, a new weighted low-rank decomposition model with Laplace regularization (WLRL) is proposed in this paper: (1) a weighted low-rank decomposition model that can decompose the original image into background and defective regions, and (2) a Laplace regularization that can enlarge the distance between the background and the defective regions. The performance of the proposed method WLRL is evaluated on the box- and star-patterned fabric databases, and superior results are shown compared with state-of-the-art methods, that is, 98.70% ACC (accuracy) and 72.83% TPR (true positive rate) for box-patterned fabrics, 99.09% ACC (accuracy) and 83.63% TPR (true positive rate) for star-patterned fabrics.

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