In the research of anomaly detection methods, obtaining a pure background without abnormal pixels can effectively improve the detection performance and reduce the false-alarm rate. Therefore, this paper proposes a spatial density background purification (SDBP) method for hyperspectral anomaly detection. First, a density peak clustering (DP) algorithm is used to calculate the local density of pixels within a single window. Then, the local densities are sorted into descending order and the m pixels that have the highest local density are selected from high to low. Therefore, the potential abnormal pixels in the background can be effectively removed, and a purer background set can be obtained. Finally, the collaborative representation detector (CRD) is employed for anomaly detection. Considering that the neighboring area of each pixel will have homogeneous material pixels, we adopt the double window strategy to improve the above method. The local densities of the pixels between the large window and the small window are calculated, while all pixels are removed from the small window. This makes the background estimation more accurate, reduces the false-alarm rate, and improves the detection performance. Experimental results on three real hyperspectral datasets such as Airport, Beach, and Urban scenes indicate that the detection accuracy of this method outperforms other commonly used anomaly detection methods.

Hyperspectral images (HSIs) have high spatial resolution and spectral resolution, and using HSI as a change detection (CD) data source is crucial for detecting surface changes. However, there is a large amount of real noise in HSIs, and most deep learning-based CD methods require a large number of ground-truth labels for training, which is difficult and expensive to label manually. To reduce the dependence of CD on ground-truth labels and weaken the interference of noise on CD in HSIs, in this paper we propose a hyperspectral image change detection framework with self-supervised contrastive learning pre-trained model (CDSCL). CDSCL consists of two parts: self-supervised contrastive learning pre-trained model and CD classification network. The main contributions of this article are as follows: 1) a data augmentation strategy based on Gaussian noise is proposed to improve the ability of the model to extract variation information from HSIs with different random Gaussian noises; 2) based on Information Bottleneck (IB) theory, a progressive feature extraction module (PFEM) is developed to remove redundant or irrelevant details in changing information spectrum; 3) a contrastive loss function based on Pearson correlation coefficient and negative cosine correlation is designed to make the features extracted by the two branches of the siamese network close to each other. Experimental results on four real hyperspectral datasets demonstrate that the CD performance of CDSCL outperforms the most representative CD methods.

Window-based operation is a general technique for hyperspectral anomaly detection. However, the problem remains that background knowledge containing abnormal information often affects the attributes of test pixels. In this paper, a dual collaborative representation (DCR)-based hyperspectral anomaly detection method is proposed to solve the above problem effectively, which consists of the following main steps. First, lowrank and sparse matrix decomposition (LRSMD) is employed to obtain a low-rank background matrix. Then, the density peak (DP) clustering algorithm is applied to the low-rank background matrix to calculate the density information of the pixels in a sliding dual window. Specifically, pixels with the highest density are selected as the pure background pixel set to approximately represent the test pixels in this work. Next, the test pixels are approximated by the linear combination of pixels in the inner window. Finally, a decision function based on the residuals of this dual-stage collaborative representation is utilized to detect abnormal pixels. Experimental results on several hyperspectral datasets demonstrate that the proposed DCR method can both improve the separability between abnormal pixels and their corresponding background and show better detection performance with respect to state-of-the-art anomaly detection methods in terms of detection accuracy.

General machine vision algorithms are difficult to detect LCD sub-pixel level defects. By studying the LCD screen images, we found that the pixels in the LCD screen are regularly arranged. The spectrum distribution of LCD images, which is obtained by the Fourier transform, is relatively consistent. According to this feature, a method of sub-pixel defect detection based on notch filter and image registration is proposed. First, we take a defect-free template image to establish registration template and notch-filtering template; then we take the defect images for image registration with registration template, and solve the offset problem. After the notch-filter template filtering the background texture, the defect is more obvious; Finally the defects are obtained by the threshold segmentation method. The experiment results show that the proposed method can detect sub-pixel defects accurately and quickly.

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