Most traditional hyperspectral image (HSI) classification methods relied on hand-crafted or shallow-based descriptors, which limits their applicability and performance. Recently, deep learning has gradually become the mainstream method of HSI classification, because it can automatically extract deep abstract features for classification. However, it remains a challenge to learn more meaningful features for HSI classification from a small training sample set. In this paper, a 3D cascaded spectral–spatial element attention network (3D-CSSEAN) is proposed to solve this issue. The 3D-CSSEAN integrates the spectral–spatial feature extraction and attention area extraction for HSI classification. Two element attention modules in the 3D-CSSEAN enable the deep network to focus on primary spectral features and meaningful spatial features. All attention modules are implemented though several simple activation operations and elementwise multiplication operations. In this way, the training parameters of the network are not added too much, which also makes the network structure suitable for small sample learning. The adopted module cascading pattern not only reduces the computational burden in the deep network but can also be easily operated via plug–expand–play. Experimental results on three public data sets show that the proposed 3D-CSSEAN achieved comparable performance with the state-of-the-art methods.

Ancient murals have suffered from continuous damage over time, and especially paint loss disease. Therefore, disease labeling, as the basis for ancient mural restoration, plays an important role in the protection of cultural relics. The predominant method of disease labeling is currently manual labeling, which is highly dependent on expert experience, time consuming, inefficient and results in inconsistent accuracy of the marking effect. In this paper, we propose a labeling framework for paint loss disease of ancient murals based on hyperspectral image classification and segmentation. The proposed framework involves first the extraction of features from the hyperspectral image, and then image segmentation is performed based on the spatial features to obtain more accurate region boundaries. Then, the hyperspectral image’s regions are classified based on their spatial-spectral characteristics, and the candidate areas of paint loss disease are obtained. Finally, by leveraging the true color image segmentation results, the proposed disease labeling strategy combines the results of classification and segmentation to propose the final paint loss disease labeling areas. The experimental results show that the proposed method can not only combine the hyperspectral space and spectral information effectively to obtain accurate labeling of paint loss disease, but can also mark the paint loss disease not easily observed using ordinary digital cameras. Compared with the state-of-the-art methods, the proposed framework could be promising for accurate and effective paint loss disease labeling for ancient murals.

Remarkable strides have been made in super-resolution methods based on deep learning for hyperspectral images (HSIs), which are capable of enhancing the spatial resolution. However, these methods predominantly focus on capturing local features using convolutional neural networks (CNNs), neglecting the comprehensive utilization of global spatial-spectral information. To address this limitation, we innovatively propose a Multi-scale Spatial-spectral CNN-transformer network for Hyperspectral image Super Resolution, namely MSHSR. MSHSR not only applies the local spatial-spectral characteristics but also innovatively facilitates the collaborative exploration and application of spatial details and spectral data globally. Specifically, we first design a multi-scale spatial-spectral fusion module (MSSFM), which integrates dilated-convolution parallel branches (DPB) and a hybrid spectral attention mechanism (HSAM) to address the strong local correlations in HSIs, effectively capturing and fusing multi-scale local spatial-spectral information. Furthermore, in order to fully exploit the global contextual consistency in HSIs, we introduce a sparse spectral transformer module (SSTM). This module processes the previously obtained local spatial-spectral features, thoroughly exploring the elaborate global inter-relationship and long-range dependencies among different spectral bands through a coarse-to-fine strategy. Extensive experimental results on three hyperspectral datasets demonstrate the superior performance of our method, outperforming comparison methods both in quantitative metrics and visual performance.

Noncontact heart rate (HR) measurement is a very important trend in clinical medicine. Recently, a variety of deep networks have been applied to estimate HRs from facial videos. However, due to limited data resources and poor parameter optimization, few existing models have achieved incredible performance in complicated scenarios, such as those with illumination changes, different skin tones, and facial motion. To address these challenges, this paper proposes a novel multi-stage deep network (MSDN) that can decentralize the learnable parameters into different stages to reduce the difficulty of learning through multiple training steps. Specifically, the proposed network consists of three stages in an end-to-end way. In the first stage, an HR-aware feature extractor utilizes the next convolutional neural network (ConvNeXt) embedded with a newly designed bandpass filter as its backbone to extract spatial-temporal features for determining heart rate changes. Moreover, pseudolabels are generated to make the features compatible with illumination, motion, and color variance. In the second stage, various modules, including singular value decomposition (SVD) pooling and enhanced difference convolution modules, are then designed and combined with a Transformer encoder to construct a feature-compressed remote photoplethysmography (rPPG) generator. In the third stage, an HR estimator with an interbeat interval analyzer and a 1D filter is newly designed for HR estimation. Extensive experiments are performed on three publicly available databases (i.e., VIPL-HR, COHFACE, and PURE), and the results demonstrate the effectiveness of the proposed method through ablation studies and comparison experiments with state-of-the-art (SOTA) methods.

JPEG-LS (a lossless (LS) compression standard developed by the Joint Photographic Expert Group) compressed image restoration is a significant problem in remote sensing applications. It faces the following two challenges: first, bridging small pixel-value gaps from wide numerical ranges; and second, removing banding artifacts in the condition of lacking available context information. As far as we know, there is currently no research dealing with the above issues. Hence, we develop this initial line of work on JPEG-LS compressed remote sensing image restoration. We propose a novel CNN model called CARNet. Its core idea is a context-aware residual learning mechanism. Specifically, it realizes residual learning for accurate restoration by adopting a scale-invariant baseline. It enables large receptive fields for banding artifact removal through a context-aware scheme. Additionally, it eases the information flow among stages by utilizing a prior-guided feature-fusion mechanism. Alternatively, we design novel R IQA models to provide a better restoration performance assessment for our study by utilizing gradient priors of JPEG-LS banding artifacts. Furthermore, we prepare a new dataset of JPEG-LS compressed remote sensing images to supplement existing benchmark data. Experiments show that our method sets the state-of-the-art for JPEG-LS compressed remote sensing image restoration.

Directional modulation (DM) technique has the ability to enhance the physical layer security (PLS) of wireless communications. Conventional DM schemes are usually based on a single antenna array with the basic assumption that eavesdroppers (Eves) and legitimate users (LUs) are in different directions. However, it is possible that Eves are in the same direction as LUs in practical applications. As a result, signals received by Eves will be approximately the same or even in better quality than those received by LUs. To address the neighbor security issue, we introduce a multiple antenna arrays model at the transmitter side with the help of the artificial noise (AN)-aided DM technique to achieve secure and precise DM transmission in this paper. Meanwhile, to recover the mixed useful signals, two novel DM schemes based on single- and multi-carrier multiple antenna arrays model are proposed, respectively. In addition, the symbol error rate (SER), secrecy rate, and robustness performance of the proposed DM schemes were analyzed and simulated. Simulations validate the effectiveness of the proposed DM schemes and demonstrate that multiple antenna arrays model based DM methods outperform single antenna array model aided DM methods in security.