The low-rank and sparse decomposition model (LSDM) has been widely studied by researchers and has successfully solved the problem of hyperspectral image (HSI) anomaly detection (AD). The traditional LSDM usually ignores the information of the low-rank matrix, which only detects the anomalous targets by using the sparse component. To utilize both the sparse component and the low-rank component comprehensively, an anomaly detector for HSIs based on LSDM with density peak guided collaborative representation (LSDDPCRD) is proposed in this article. First, the LSDM technique with the mixture of Gaussian model is used to decompose the original HSI, which can also alleviate the background noise contamination problem. Then, the low-rank matrix is detected by the density peak guided collaborative representation detection algorithm, while the sparse matrix is calculated according to the Manhattan distance. In addition, an entropy-based adaptive fusing method is designed to combine the results obtained from the low-rank matrix and the sparse component. It could choose the fusing weights adaptively according to the characteristics of an HSI. The experimental results indicate that the LSDDPCRD performs better than eight classical and state-of-the-art AD algorithms (GRX, LRX, SRX-Segmented, CRD, RPCA-RX, LSMAD, LRASR, and LSDM-MoG) on four real HSIs.

Convolutional neural networks (CNNs) have exhibited extraordinary achievements in hyperspectral image (HSI) classification due to their detailed representation of features. However, the improvement of classification accuracy often leads to an evident increase in the complexity of the model, which makes it challenging for the model with the state-of-the-art performance to be applied in the actual scene. Considering MobileNetV3 as a lightweight feature extractor, this article proposes a model suitable for HSI classification based on MobileNetV3. To decrease the problem of massive redundant calculations in the existing spatial attention module, this article proposes a more concise and efficient spatial attention module based on the visual feature maps experiment. Besides, multiclass focal-loss is applied to solve the problem that the difficulty of classification varies for each sample. The experimental results demonstrate that in the case of using very few training sets, the proposed model can tremendously reduce the number of calculations and parameters while maintaining high accuracy.

Hyperspectral image (HSI) classification is one of the basic tasks of remote sensing image processing, which is to predict the label of each HSI pixel. Convolution neural network (CNN) and graph convolution neural network (GCN) have become the current research focus due to their outstanding performance in the field of HSI classification in recent years. However, GCN is a transductive learning method, which needs all nodes to participate in the training process to get the node embedding. Graph sample and aggregation (GraphSAGE) is an important branch of graph neural network, which can flexibly aggregate new neighbor nodes in non-Euclidean data of any structure, and capture long-range contextual relationships. Superpixel-based GraphSAGE can not only integrate the global spatial relationship of data, but also further reduce its computing cost. CNN can extract pixel-level features in a small area, and our center attention module (CAM) and center weighted convolution (CW-Conv) can also improve the feature extraction ability of CNN by enhancing the dominant position of target pixels. In order to make full use of the advantages of CNN and GraphSAGE, we propose a center weighted convolution and GraphSAGE (CW-SAGE) cooperative network for HSI classification. Specifically, graph simple and aggregate branch is constructed by superpixel-based encoder and decoder modules, then pixel-level features are extracted by a central attention convolutional neural network. Finally, the features of the two branches are spliced together for feature fusion. We conduct experiments on three hyperspectral datasets and compare the results with other current state-of-the-art methods. A series of experiments demonstrate the advantages of our method.

Hyperspectral images have been playing an important role in the field of ground object classification because of their rich spatial and spectral information. Aiming at how to extract complex feature information from hyperspectral images, we propose a new feature fusion network model(DAFFN) with dual attention mechanism, which is mainly used to capture more accurate global-local context attention features. The model extracts global context attention features using self-attention mechanism and local context attention features using cross - attention mechanism. Considering the problem that position information is easily lost during the conversion of attention mechanism, we propose a position self-calibration module that can be flexibly embedded into two attention modules. In addition, in order to better integrate global and local features, we also designed a multi-scale global and local feature fusion module (MSGL), which preserves more representative features with less communication costs by aggregating global and local attention features. We have carried out experiments on three commonly used hyperspectral datasets, and the classification results show that our model can achieve high classification accuracy even in the case of a limited number of samples.

Hyperspectral image classification methods based on deep learning and attention mechanism have been extensively studied in recent years because of their superior performance. However, the currently applied spatial attention mechanism and channel attention mechanism are separated from each other. For this reason, we propose a new dual-triple attention network (DTAN), which realizes the high-precision classification of hyperspectral images based on capturing cross-dimensional interactive information. Specifically, DTAN is divided into two branches to extract the spectral information and spatial information of the hyperspectral image, which are called the spectral branch and spatial branch. While applying the channel attention mechanism to the spectral unit, the cross-dimensional interaction between the channel dimension and the spatial dimension is constructed. When the spatial attention mechanism is applied to the spatial branch, the correlation with the channel domain is also considered. Moreover, we introduce an efficient channel attention (ECA) module into the DenseNet, which allows the DenseNet to achieve partial cross-channel interaction. A series of experiments proved that DTAN has a significant advantage compared to other models when the training samples are minimal.

The advancement of the aviation sector has made the temperature measurement technology for aero-engine turbine blades essential for maintaining the engine’s safe and steady performance. The non-contact temperature measurement technology is a trending research focus in turbine blade temperature measurement due to its benefits of not requiring direct touch with the object being measured and its suitability for high-temperature and high-speed conditions. This paper provides a concise overview of various key non-contact temperature measurement methods for aero-engines, such as fluorescence temperature measurement, fiber-optic temperature measurement, and radiation temperature measurement. It discusses the temperature measurement principle, technical characteristics, and the current research status both domestically and internationally. Based on this, this Review further discusses the main challenges faced by the non-contact temperature measurement technology and the development trend of the future.