Search

The integrated imaging of LWIR cameras and SAR is one of the important directions of multi-sensor integration. In order to reduce the structural complexity of LWIR cameras and SAR-integrated imaging antenna, a dual-polarized metasurface-integrated antenna (MIA) is designed in this paper. It is composed of a microwave metasurface antenna and an optical metalens, and the metalens is embedded in the center of the metasurface antenna. The MIA uses the powerful electromagnetic wave control ability to simplify the optical and microwave signal transmission paths and reduce the number of devices. At the same time, in order to expand the function of the MIA, based on the principle of metasurface, the dual-linearly polarized and dual-circularly polarized MIAs are designed and simulated, respectively. The results show that the designed dual-polarized MIA has good performance. This paper provides a new scheme for the integrated imaging system of LWIR cameras and SAR with simple structure, diverse functions and easy integration. [ABSTRACT FROM AUTHOR]

Accurately localizing the pupil is an essential requirement of some new human–computer interaction methods. In the past, a lot of work has been done to solve the pupil localization problem based on the appearance characteristics of the eye, but these methods are often specific to the scenario. In this paper, we propose an improved U-net network to solve the pupil location problem. This network uses the attention mechanism to automatically select the contribution of coded and uncoded features in the model during the skip connection stage of the U-net network in the channel and spatial axis. It can make full use of the two features of the model in the decoding stage, which is beneficial for improving the performance of the model. By comparing the sequential channel attention module and spatial attention module, average pooling and maximum pooling operations, and different attention mechanisms, the model was finally determined and validated on two public data sets, which proves the validity of the proposed model. [ABSTRACT FROM AUTHOR]

With the development of earth observation satellites, it is very meaningful to study methods for intelligently processing remote sensing images (RSIs). As an important pretreatment technique, salient object detection (SOD) can produce accurate saliency predictions in natural scenes. However, due to the complex object types, high noise levels, and different imaging angles in RSIs, it is still difficult to generate accurate saliency predictions in such images. This paper proposes a SOD method for RSIs, namely, the two‐stage local attention network (TLANet). It uses two‐stage attention (TSA) to capture the relationships between the pixels of features at different scales and uses an adjacent semantic refinement (ASR) module to enhance the flow of information across scales. In addition, a cascade structure is introduced to the decoder so that more semantic features can guide the feature decoding process. Moreover, a new loss function is proposed to improve the prediction effects obtained for salient edges. Through comprehensive experiments conducted on two RSIs datasets, it is demonstrated that the TLANet is superior to the state‐of‐the‐art methods in both qualitative and quantitative comparisons. [ABSTRACT FROM AUTHOR]