Optical and Synthetic Aperture Radar (SAR) imagery offers a wealth of complementary information on a given target, attributable to the distinct imaging modalities of each component image type. Thus, multimodal remote sensing data have been widely used to improve land cover classification. However, fully integrating optical and SAR image data is not straightforward due to the distinct distributions of their features. To this end, we propose a land cover classification network based on multimodal feature fusion, i.e., MFFnet. We adopt a dual-stream network to extract features from SAR and optical images, where a ResNet network is utilized to extract deep features from optical images and PidiNet is employed to extract edge features from SAR. Simultaneously, the iAFF feature fusion module is used to facilitate data interactions between multimodal data for both low- and high-level features. Additionally, to enhance global feature dependency, the ASPP module is employed to handle the interactions between high-level features. The processed high-level features extracted from the dual-stream encoder are fused with low-level features and inputted into the decoder to restore the dimensional feature maps, generating predicted images. Comprehensive evaluations demonstrate that MFFnet achieves excellent performance in both qualitative and quantitative assessments on the WHU-OPT-SAR dataset. Compared to the suboptimal results, our method improves the OA and Kappa metrics by 7.7% and 11.26% on the WHU-OPT-SAR dataset, respectively.

The rapid proliferation of Internet of Things (IoT) devices has led to an increased need for robust and efficient intrusion detection systems capable of identifying and mitigating novel threats. Traditional methods often struggle with the scarcity of labeled anomaly data, which is highly consequential, particularly in the context of IoT. In this study, we propose a novel few-shot learning (FSL) approach by leveraging a multistage attention Siamese network (MASiNet) for network traffic intrusion detection based on meta-learning framework. Unlike traditional methods, the proposed MASiNet model is capable of detecting intrusions with minimal labeled samples, addressing the challenge of scarce anomaly data. The model is trained using various attack samples and evaluates unknown samples by comparing similarities with a small set of known attack types. A well-structured cost function design, incorporating two specific losses, is introduced to optimize the effectiveness of the training process. Tested on the NSL_KDD and UNSW-NB15 data sets in a simulated FSL environment, the MASiNet model demonstrates superior performance in terms of accuracy, precision, and false alarm rate (FAR), outperforming existing methods. Furthermore, we have validated our approach through real-world evaluations. The proposed method provides an effective solution for intrusion detection in the context of FSL, offering a proficient solution that aligns with the dynamic nature of IoT networks.

Land cover classification using high-resolution Polarimetric Synthetic Aperture Radar (PolSAR) images obtained from satellites is a challenging task. While deep learning algorithms have been extensively studied for PolSAR image land cover classification, the performance is severely constrained due to the scarcity of labeled PolSAR samples and the limited domain acceptance of models. Recently, the emergence of the Segment Anything Model (SAM) based on the vision transformer (VIT) model has brought about a revolution in the study of specific downstream tasks in computer vision. Benefiting from its millions of parameters and extensive training datasets, SAM demonstrates powerful capabilities in extracting semantic information and generalization. To this end, we propose a dual-stream feature extraction network based on SAM, i.e., BSDSNet. We change the image encoder part of SAM to a dual stream, where the ConvNext image encoder is utilized to extract local information and the VIT image encoder is used to extract global information. BSDSNet achieves an in-depth exploration of semantic and spatial information in PolSAR images. Additionally, to facilitate a fine-grained amalgamation of information, the SA-Gate module is employed to integrate local–global information. Compared to previous deep learning models, BSDSNet’s impressive ability to represent features is akin to a versatile receptive field, making it well suited for classifying PolSAR images across various resolutions. Comprehensive evaluations indicate that BSDSNet achieves excellent results in qualitative and quantitative evaluation when performing classification tasks on the AIR-PolSAR-Seg dataset and the WHU-OPT-SAR dataset. Compared to the suboptimal results, our method improves the Kappa metric by 3.68% and 0.44% on the AIR-PolSAR-Seg dataset and the WHU-OPT-SAR dataset, respectively.

The complex and variable ocean environment noise has a very bad influence on the sonar detection signal, which leads to the low probability of finding the target. The adaptive filter can filter the uncertain noise signal, track the change of the signal, and continuously adjust the parameters to achieve the optimal filtering effect. As the traditional adaptive algorithm has the disadvantages of convergence speed and steady-state error, and the poor effect on the impulse noise, an variable step-size LMS algorithm is proposed according to the characteristics of underwater noise sources. The results show that the algorithm can converge quickly, maintain small steady-state error and have good performance on anti-impulse noise.

Metalens can achieve arbitrary light modulation by controlling the amplitude, phase, and polarization of the incident waves and have been applied across various fields. This paper presents a color router designed based on metalens, capable of effectively separating spectra from visible light to near-infrared light. Traditional design methods for meta-lenses require extensive simulations, making them time-consuming. In this study, we propose a deep learning network capable of forward prediction across a broad wavelength range, combined with a particle swarm optimization algorithm to design metalens efficiently. The simulation results align closely with theoretical predictions. The designed color router can simultaneously meet the theoretical transmission phase of the target spectra, specifically for red, green, blue, and near-infrared light, and focus them into designated areas. Notably, the optical efficiency of this design reaches 40%, significantly surpassing the efficiency of traditional color filters.