Hyperspectral images (HSIs) are widely used in various tasks such as earth observation and target detection. However, during the imaging process, HSIs are often corrupted by various noises. In this article, we firstly investigate the advantages of traditional physical restoration models and the denoising convolutional neural networks (CNN) for HSIs denoising tasks. The sparse based low-rank representation can explore the global correlations in both the spatial and spectral domains, and the CNN-based denoiser can represent the deep prior which cannot be designed by traditional restoration models. Then, we propose a HSI denoising model with low-rank representation and CNN denoiser prior in the flexible and extensible plug-and-play framework by combining the advantages of the two methods. The proposed model is user-friendly, requiring no retraining. Simulated data experiments show that, compared with competitive methods, the proposed one achieves better denoising results for both additive Gaussian noise and Poissonian noise in various quantitative evaluation indicators. Real data experiments show that the proposed model yields the best performance.

Coating structures with dynamically adjustable infrared emissivity are crucial in spacecraft components to cope with the transient thermal environments of space. For a long time, thermochromic phase change materials have been widely used in applications requiring emissivity adjustment, and optimizing the range of adjustable infrared emissivity has always been at the forefront of research. However, reducing the absorption of solar radiation has significant implications for the practical application and thermal stability of spacecraft components in space environments. In this paper, we propose a multilayer film structure based on the phase change material VO₂ combined with the materials ZnSe and ITO to achieve low solar radiation absorption and adjustable infrared emissivity for intelligent thermal radiators in space. Through finite element simulation analysis of the structure, we achieve a solar radiation absorption rate of 0.3 and an adjustable infrared emissivity of 0.49. According to Stefan–Boltzmann’s law, the structure exhibits strong radiative heat dissipation at high temperatures and weak energy dissipation at low temperatures to maintain the thermal stability of the device and ensure efficient operation. The intelligent thermal radiator operates based on the principles of Fabry–Perot resonance. Therefore, the multilayer structure based on the phase change material VO₂ demonstrates excellent performance in both solar radiation absorption and adjustable infrared emissivity, showcasing its tremendous potential in the field of intelligent thermal control in aerospace.

Although the existing deep-learning-based hyperspectral image (HSI) denoising methods have achieved tremendous success, recovering high-quality HSIs in complex scenes that contain mixed noise is still challenging. Besides, these methods have not fully explored the local and global spatial–spectral information of HSIs. To address the above issues, a novel HSI mixed noise removal network called subspace projection attention and residual channel attention network (SPARCA-Net) is proposed. Specifically, we propose an orthogonal subspace projection attention (OSPA) module to adaptively learn to generate bases of the signal subspace and project the input into such space to remove noise. By leveraging the local and global spatial relations, OSPA is able to reconstruct the local structure of the feature maps more precisely. We further propose a residual channel attention (RCA) module to emphasize the interdependence between feature maps and exploit the global channel correlation of them, which could enhance the channel-wise adaptive learning. In addition, multiscale joint spatial–spectral input and residual learning strategies are employed to capture multiscale spatial–spectral features and reduce the degradation problem, respectively. Synthetic and real HSI data experiments demonstrated that the proposed HSI denoising network outperforms many of the advanced methods in both quantitative and qualitative assessments.

Sea clutter usually greatly affects the target detection and identification performance of marine surveillance radars. In order to reduce the impact of sea clutter, a novel sea clutter suppression method based on chaos prediction is proposed in this paper. The method combines a generator trained by Generative Adversarial Networks (GAN) with a Long Short-Term Memory (LSTM) network to accomplish sea clutter prediction. By exploiting the generator’s ability to learn the distribution of unlabeled data, the accuracy of sea clutter prediction is improved compared with the classical LSTM-based model. Furthermore, effective suppression of sea clutter and improvements in the signal-to-clutter ratio of echo were achieved through clutter cancellation. Experimental results on real data demonstrated the effectiveness of the proposed method.