Hyperspectral Anomaly Detection via Graphical Connected Point Estimation and Multiple Support Vector Machines

Abstract

Most hyperspectral anomaly detection algorithms are based on various hypothetical models justified by different methods. The closer to the real-world scene distribution a hypothetical model is, the better detection performance usually results, albeit at the expense of increased model complexity. There is also a challenge in most of the detection methods that anomalous components cannot be completely separated from the background due to the difference between hypothetical models and the real-world scene. To address this problem, a novel strategy based on graph theory and the multiple support vector machines technique is proposed in this paper. Firstly, a graphical connected point-based estimation is utilized as a preprocessing procedure to separate potential anomalies from robust backgrounds of the image. Second, without building a hypothetical model, the hyperspectral image is classified into several categories by a clustering method according to the different characteristics of materials. Consequently, for each category, the support vector machine is used to classify anomalous components and background components belonging to specific categories, and the result map of each classification is obtained. Finally, all maps corresponding to various categories are fused by an effective fusion strategy, and the detection result is generated by our method. In the subsequent experiments, we use several state-of-art anomaly detection algorithms in a comparison with our method using both simulated and real-world HSI datasets. The experimental results demonstrate that the proposed method outperforms others.

Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing

“…A SVM (support vector machine) (Maktabi et al, 2020;Song et al, 2020;Wang et al, 2020;Xiang et al, 2020)based classification model for wheat blast was constructed. After retaining the first six principal components, the hyperspectral images of samples 1-1 and 1-2 were normalized and corrected by two correction methods to create the dataset.…”

“…From Table 3, we can see that the TSG filtering has greatly improved the accuracy of the hyperspectral classification model, which indicates that the TSG filtering algorithm proposed in this paper can obtain good results in practical applications. The hyperspectral images of samples 1-1 and 1-2 are used as the experimental samples, and the training and test sets are constructed by the preprocessing method proposed in this paper, and the classification model is established based on the support vector machine (SVM) (Fang et al, 2015;Song et al, 2020;Wang et al, 2020;Xiang et al, 2020) algorithm, and the hyperspectral images of samples 2-1, 2-2, and 2-3 are used as the validation set.…”

Hyperspectral Image Classification Using Residual 2d and 3d Convolutional Neural Network Joint Attention Model

“…For instance, the support vector machine (SVM) seeks to separate two-class data by learning an optimal decision that separates the training samples in a kernel-included high dimensional feature space. Some studies using SVM for hyperspectral image classification can improve result performance (Mountrakis et al, 2011;Li, Bioucas-Dias & Plaza 2013;Song et al, 2020). However, most traditional approaches presenting handdesigned feature descriptions based on expertise knowledge probably limit the application potential for the precise classification.…”