Determination of visible coordinates of the low-orbit space objects and their photometry by the CCD camera with the analogue output. Initial image processing

The number of artificial space objects in the low Earth orbit has been continuously increasing. That raises the requirements for the accuracy of measurement of their coordinates and for the precision of the prediction of their motion. The accuracy of the prediction can be improved if the actual current orientation of the non-spherical satellite is taken into account. In so doing, it becomes possible to directly determine the atmospheric density along the orbit. The problem solution is to regularly conduct the photometric surveillances of a large number of satellites and monitor the parameters of their rotation around the centre of mass. To do that, it is necessary to get and promptly process large video arrays, containing pictures of a satellite against the background stars.     

星载立体测绘相机立方镜间姿态标定

介绍了测绘相机立方镜与星敏感器立方镜间坐标系转换关系的标定方法。采用4台自准直经纬仪分别对两个立方镜进行准直测量,建立立方镜坐标系,并推导了基于准直测量角度的立方镜坐标系的旋转矩阵公式;利用经纬仪间短边互瞄方法实现两个立方镜间姿态的传递。标定试验表明,该方法可以标定出两立方镜坐标轴间夹角以及测绘相机间的姿态关系,满足了相机研制要求。     

基于小波阈值降噪和经验模态分解的高光谱图像分类算法

为了充分降低高光谱图像中的噪声以获得高精度的分类结果,本文结合小波阈值降噪(WTD)和经验模态分解(EMD)的优点,提出了一种基于小波阈值降噪-经验模态分解的高精度支持向量机(SVM)高光谱图像分类算法(WTD-EMD-SVM)。首先对高光谱图像进行小波阈值降噪,除去高光谱数据中的高频噪声;然后再对高光谱图像进行EMD,获得含有高光谱数据本质特征的内固模态函数(IMF)和含有低频噪声的残差;最后采用内固模态函数重构高光谱图像,并对高光谱图像进行SVM分类。将其应用到AVIRIS数据92AV3C,仿真结果表明该算法不仅提高了高光谱图像分类精度,同时可减少支持向量数目,以提高高光谱图像分类速度。     

复合材料布带缠绕纠偏控制系统设计

针对复合材料缠绕过程中的布带跑偏问题,在分析纠偏工作原理和影响纠偏控制精度关键因素的基础上,以线阵CCD( Charge Coupled Device)图像传感器为检测元件,以步进电机、蜗轮蜗杆、纠偏辊等为执行机构,采用FUZZY-PID复合控制策略,建立了布带缠绕纠偏控制系统.经纠偏试验验证,该纠偏控制系统的纠偏精度完全满足缠绕工艺要求,使缠绕过程的纠偏自动化得以实现.     

码流映射的JPEG2000遥感图像无损信息隐藏

遥感图像经过飞行器JPEG2000压缩编码传输到地面,在传输过程中有时需要搭载其他一些遥测信息,将附加信息嵌入到压缩文件中。对JEPG2000的数据结构进行了研究,将压缩文件的数据部分变换成图像并将其分块,统计256个灰度值,找到出现次数最多的灰度值以及次数为0的灰度值。将这些灰度值所对应的字节进行映射,不同的灰度值代表不同的二进制秘密信息。接收端通过映射将秘密信息提取后,再将载体更改后的字节恢复。隐藏容量平均有了500bit的提升,载密图像的PSNR大于30dB,同时载体数据仍然可以无损恢复。试验结果说明,该算法可以无损地实现JPEG2000图像的信息搭载,同时文件没有膨胀。     

星载TDI CCD相机成像仿真系统的实现

以VC++ 6.0为开发环境, 基于航天TDI (Time Delay and Integration) CCD相机地物点与像点的映射模型, 开发了TDI CCD相机成像仿真软件系统, 建立了由卫星单轴气浮姿态控制仿真台和地面星载TDI CCD相机成像仿真软件系统构成的实时航天TDI CCD相机成像仿真平台, 可动态模拟航天TDI CCD相机在进行标称偏流角调节后卫星姿态角速度误差对航天TDI CCD相机成像的影响, 仿真结果与实际成像结果基本一致.      

真空低温环境用高精度CCD摄影测量系统

文章介绍了一套高精度CCD摄影测量系统,该系统是专门为在真空低温环境下的摄影测量而研制的,主要应用于卫星天线在模拟真空低温环境下的冷热变形测量试验。在试验过程中当卫星天线处于不同温度时,采用此系统对卫星天线表面进行高精度实时拍摄,将拍摄得到的数字图像传输给专门的图像处理软件分析,就能够得到卫星天线表面的变形数据。该系统的设计思想还可以扩展到其他小型设备在真空低温环境或其他特殊环境下的应用中。     

Comparison of residual and dense neural network approaches for building extraction from high-resolution aerial images

Applications including change detection, disaster management, and urban planning require precise building information, and therefore automatic building extraction has become a significant research topic. With the improvements in sensor and satellite technologies, more data has become available, and with the increased computational power, deep learning methods have emerged as successful tools. In this study, U-Net and FPN architectures using four different backbones (ResNet-50, ResNeXt-50, SE-ResNext-50, and DenseNet-121), and an Attention Residual U-Net approach were used for building extraction from high-resolution aerial images. Two publicly available datasets, Inria Aerial Image Labeling Dataset and Massachusetts Buildings Dataset were used to train and test the models. According to the results, Attention Residual U-Net model has the highest F1 score with 0.8154, IoU score with 0.7102, and test accuracy with 94.51% on the Inria dataset. On the Massachusetts dataset, FPN Dense-Net-121 model has the highest F1 score with 0.7565 and IoU score with 0.6188, and Attention Residual U-Net model has the highest test accuracy with 92.43%. It has been observed that, FPN with DenseNet backbone can be a better choice when working with small size datasets. On the other hand, Attention Residual U-Net approach achieved higher success when a sufficiently large dataset is provided.     

顾及不确定性分析的多数投票 SAR 影像变化检测

合成孔径雷达（Synthetic Aperture Radar，SAR）影像变化检测是遥感领域的一个重要研究方向。文章以投票法（Majority Voting，MV）为基础，提出一种全新的顾及不确定性分析的SAR影像变化检测方法（Uncertainty Analysis-based MV，UAMV）。首先选取三组典型的差分影像生成算法，生成三组互补的差分影像。然后使用模糊C均值聚类估算每组差分影像的关于变化类和未变化类的模糊隶属度函数。最后用模糊集合和信息熵理论优化MV，构建顾及不确定性分析的多数投票法，并用所构建方法融合三组差分影像的模糊隶属度函数，生成变化检测图。为验证文章方法的有效性，通过三组真实SAR影像数据进行实验分析。实验结果表明：1）通过用信息熵分析MV融合过程中的不确定性，能够显著提高MV的变化检测性能；2）与7种现有相关算法相比，UAMV方法能够取得更优的变化检测结果。该研究为SAR影像变化检测提供一种新的思路和方法。