There are more and more applications of change detection methods based on deep learning in high-resolution remote sensing images.However,downsampling and cropping strategies deployed to fit the GPU(Graphic Processing Unit)memory con-straints on processing large-size remote sensing images often result in incomplete semantic information and loss of fine details.In this paper,a collaborative supervised network based on feature pyramids is proposed to enable the network to learn local and overall features from cropped and downsampled image blocks.In addition,a feature-sharing mechanism is introduced to fuse global fea-tures and local features.We evaluated the network on the LEVIR-CD(a remote sensing change detection dataset)and S2Looking(a building change detection dataset)by comparing it with some representative change detection networks.The comparison experi-ments show that the proposed network performs better in multi-scale change detection,with a 2.69%improvement in precision on LEVIR-CD,and 6.83%and 2.68%improvement in precision and recall on the S2Looking dataset,respectively.
The Transformer model has recently achieved success in image classification tasks. Additionally, the polarimetric synthetic aperture radar (PolSAR) terrain classification has been confirmed to achieve better classification results in the complex domain compared to the real domain. Therefore, this paper introduces the Transformer into the complex domain and proposes a new classification network which named complex-valued Transformer-Unet hybrid model (CT-Unet), for PolSAR terrain classification. Its hybrid approach is to embed Transformer module into the U-Net encoder section for feature extraction on PolSAR data. The experimental results on PolSAR terrain classification using the Xi’an dataset demonstrate that our proposed model effectively improves the accuracy of PolSAR terrain classification.
面对我国现代航天高密度发射的需求,精准高效的遥测参数装订与核对尤为重要.本文基于XTCE标准定义的遥测参数格式,构建了XTCE标准对遥测参数数据底座,实现遥测参数的统一标准、统一管理和统一平台.阐述了XTCE标准的结构、火箭遥测数据库的生成和数据底座的构造.实现了对大型火箭遥测参数的自动装订,大幅提高了工作效率.
航天设备与地面设备相比,制造成本高,对空间环境的适应性要求也高.为了延长航天器寿命,提高其在轨工作的可靠性,需要考虑航天器在空间环境下的可维护性需求.针对航天资产在轨软件实现功能维护的需求,研究空间环境应用背景下的高可靠在轨可重构技术.基于FPGA芯片在航天器领域中应用的广泛性、灵活性及可靠性,设计了一种FPGA架构下的高可靠在轨重构系统.该系统的优势在于充分利用星载设备中普遍使用的"SRAM型FPGA+反熔丝FPGA"的硬件架构,在实现SRAM型FPGA动态刷新功能的基础上仅通过软件更改来增加在轨重构功能,极大降低了硬件更改的成本,扩展了可重构功能的应用范围.在某航天器星载设备中应用该在轨重构系统,通过实际飞行经历,验证了该架构系统设计方案的可行性、可扩展性及可靠性.
首先本文给出了天线跟踪控制系统组成及原理框图,阐述了系统闭环工作流程;其次,针对天线跟踪目标过程中,跟踪接收机输出的目标与天线波束中心角误差受随机误差分量的影响,采用Kalman滤波算法对获得的实时角位置数据进行滤波处理;然后,对天线跟踪控制系统进行了建模,给出了系统动力学模型,对采用的双电机消隙设计方案进行了仿真;最后,通过挂飞试验验证了系统工作的可靠性.
