The development of deep learning has brought new prospects into the field of hyperspectral classification, and the classification ability of this method for the classification of hyperspectral images (HSIs) has been continuously improved. However, there are still some problems that must be solved; for example, the spectral–spatial features of HSIs are not effectively extracted, the labeled samples in the data set are limited, and the number of samples in different categories is imbalanced. To facilitate the progress of hyperspectral classification, a spectral–spatial fractal residual convolutional neural network with data balance augmentation is proposed here. In this network, a data balance augmentation approach that can solve the problems of limited labeled data and imbalanced categories is proposed. In addition, the spectral–spatial residual module is proposed to learn the spectral–spatial information and alleviate the problem of model degradation effectively. In addition, the spectral–spatial focal structure, which can guarantee the integrity of the information, is introduced. Moreover, the spectral–spatial dimensional transformation module, which can reduce the size and number of hyperspectral feature maps without losing the fine features, is presented. In particular, the proposed network has a strong ability to classify the categories that have a small number of samples and reaches the state-of-the-art level for three benchmark data sets.

Unsupervised anomaly detection methods can identify surface defects in industrial images by leveraging only normal samples for training. Due to the risk of overfitting when learning from a single class, anomaly synthesis strategies are introduced to enhance detection capability by generating artificial anomalies. However, existing strategies heavily rely on anomalous textures from auxiliary datasets. Moreover, their limitations in the coverage and directionality of anomaly synthesis may result in a failure to capture useful information and lead to significant redundancy. To address these issues, we propose a novel Progressive Boundary-guided Anomaly Synthesis (PBAS) strategy, which can directionally synthesize crucial feature-level anomalies without auxiliary textures. It consists of three core components: Approximate Boundary Learning (ABL), Anomaly Feature Synthesis (AFS), and Refined Boundary Optimization (RBO). To make the distribution of normal samples more compact, ABL first learns an approximate decision boundary by center constraint, which improves the center initialization through feature alignment. AFS then directionally synthesizes anomalies with more flexible scales guided by the hypersphere distribution of normal features. Since the boundary is so loose that it may contain real anomalies, RBO refines the decision boundary through the binary classification of artificial anomalies and normal features. Experimental results show that our method achieves state-of-the-art performance and the fastest detection speed on three widely used industrial datasets, including MVTec AD, VisA, and MPDD. The code will be available at: https://github.com/cqylunlun/PBAS.

Defect detection of industrial products often uses computer vision methods. Detecting anomalies in the image can reflect the defect of the product. In order to adapt to the scene of less defect samples and unclear defect classification standards in industrial production and improve the accuracy and robustness of detection, this paper proposes a new unsupervised anomaly detection and Localization framework based on self-updated memory and center clustering (SMCC). Distinct from previous works, it uses a pre-trained model to extract image features, and then uses a Gaussian mixture model to cluster and obtain cluster centers, so that normal sample features are compactly distributed around the cluster centers, thereby better distinguishing normal and abnormal sample features. The advantage of the self-updated memory bank is to reduce the use of memory and adjust the parameters of the pre-trained network to make it more suitable for the distribution of the current dataset. Our experiments on the MVTec AD and other datasets show the effectiveness of SMCC for anomaly detection and localization.

Recently, the classification of hyperspectral images has made great process. Especially, the classification methods based on three-dimensional convolutional neural network have remarkable performance due to the uniqueness of hyperspectral images. However, the hyperspectral classification still faces great challenges due to a series of problems such as the insufficient extraction of spectral-spatial features, the lack of labeled samples, the large amount of noise, the tendency of overfitting and so on. Therefore, SSDANet is proposed to solve the above problems and promote the further development of hyperspectral classification technology based on deep learning. SSDANet is a spectral-spatial three-dimensional convolutional neural network with a deep and wide structure that can significantly improve classification performance. In SSDANet, the spectral-spatial dense connectivity is put forward to protect the integrity of information. It is made up of the spectral branch and the spatial branch, which can learn and reuse the spectral-spatial features. Besides, the spectral-spatial attention mechanism is proposed to adapt the special structure of hyperspectral images. It can excite important spectral-spatial information and suppress unimportant spectral-spatial information. In addition, a series of optimization methods including data augmentation, batch normalization, dropout, exponential decay learning rate, and L2 regularization are adopted to alleviate the problem of overfitting and improve the classification results. To verify the performance of SSDANet, experiments were implemented on two widely used datasets—Pavia University and Indian Pines. Under the condition of limited labeled samples, the classification evaluation indexes of OA, AA, and Kappa on the two datasets all exceeded 99%, reaching state-of-the-art performance.

Anomaly detection plays a vital role in the inspection of industrial images. Most existing methods require separate models for each category, resulting in multiplied deployment costs. This highlights the challenge of developing a unified model for multiclass anomaly detection. However, the significant increase in interclass interference leads to severe missed detections. Furthermore, the intraclass overlap between normal and abnormal samples, particularly in synthesis-based methods, cannot be ignored and may lead to over-detection. To tackle these issues, we propose a novel center-aware residual anomaly synthesis (CRAS) method for multiclass anomaly detection. CRAS leverages center-aware residual learning to couple samples from different categories into a unified center, mitigating the effects of interclass interference. To further reduce intraclass overlap, CRAS introduces distance-guided anomaly synthesis that adaptively adjusts noise variance based on normal data distribution. Experimental results on diverse datasets and real-world industrial applications demonstrate the superior detection accuracy and competitive inference speed of CRAS.