The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Si...The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Since buildings are inherently elevated objects, these images need to be co-registered with their elevation data for reliable building detection results. However, accurate co-registration is extremely difficult for off-nadir VHR images acquired over dense urban areas. Therefore, this research proposes a Disparity-Based Elevation Co-Registration (DECR) method for generating a Line-of-Sight Digital Surface Model (LoS-DSM) to efficiently achieve image-elevation data co-registration with pixel-level accuracy. Relative to the traditional photogrammetric approach, the RMSE value of the derived elevations is found to be less than 2 pixels. The applicability of the DECR method is demonstrated through elevation-based building detection (EBD) in a challenging dense urban area. The quality of the detection result is found to be more than 90%. Additionally, the detected objects were geo-referenced successfully to their correct ground locations to allow direct integration with other maps. In comparison to the original LoS-DSM development algorithm, the DECR algorithm is more efficient by reducing the calculation steps, preserving the co-registration accuracy, and minimizing the need for elevation normalization in dense urban areas.展开更多
非视域(NLOS)成像是一种综合成像和计算重构的技术,指在不直接拍摄场景的情况下通过获取介质上隐藏场景的散射或反射信息对其进行重建。目前的NLOS成像还处于早期发展阶段,场景模型、目标信息重建等尚无系统研究方法。为此,提出一种针...非视域(NLOS)成像是一种综合成像和计算重构的技术,指在不直接拍摄场景的情况下通过获取介质上隐藏场景的散射或反射信息对其进行重建。目前的NLOS成像还处于早期发展阶段,场景模型、目标信息重建等尚无系统研究方法。为此,提出一种针对无遮挡、非自发光场景的NLOS成像方法。基于光辐射理论,分析该场景下漫反射面的成像与隐藏物体形状的关系,确定NLOS成像模型与重建目标。使用渲染软件结合运动图像专家组7(MPEG7)数据集,生成符合实际物理意义的漫反射被动非视域全影(DS-NLOS)数据集。构建被动非视域重建网络模型(Re-NLOS),采用视觉Transformer(Vi T)结构结合生成式对抗网络(GAN)提取采集的漫反射面图像的全局特征,并恢复隐藏物体形状。在DS-NLOS数据集上的实验结果表明,该方法能够从漫反射面上恢复隐藏物体的形状信息,在测试集20个类别的物体上的峰值信噪比(PSNR)和结构相似性(SSIM)相比漫反射面全影图像平均提高了5.85 d B和0.0381,对真实室内场景也具有一定的恢复能力。展开更多
文摘The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Since buildings are inherently elevated objects, these images need to be co-registered with their elevation data for reliable building detection results. However, accurate co-registration is extremely difficult for off-nadir VHR images acquired over dense urban areas. Therefore, this research proposes a Disparity-Based Elevation Co-Registration (DECR) method for generating a Line-of-Sight Digital Surface Model (LoS-DSM) to efficiently achieve image-elevation data co-registration with pixel-level accuracy. Relative to the traditional photogrammetric approach, the RMSE value of the derived elevations is found to be less than 2 pixels. The applicability of the DECR method is demonstrated through elevation-based building detection (EBD) in a challenging dense urban area. The quality of the detection result is found to be more than 90%. Additionally, the detected objects were geo-referenced successfully to their correct ground locations to allow direct integration with other maps. In comparison to the original LoS-DSM development algorithm, the DECR algorithm is more efficient by reducing the calculation steps, preserving the co-registration accuracy, and minimizing the need for elevation normalization in dense urban areas.
文摘非视域(NLOS)成像是一种综合成像和计算重构的技术,指在不直接拍摄场景的情况下通过获取介质上隐藏场景的散射或反射信息对其进行重建。目前的NLOS成像还处于早期发展阶段,场景模型、目标信息重建等尚无系统研究方法。为此,提出一种针对无遮挡、非自发光场景的NLOS成像方法。基于光辐射理论,分析该场景下漫反射面的成像与隐藏物体形状的关系,确定NLOS成像模型与重建目标。使用渲染软件结合运动图像专家组7(MPEG7)数据集,生成符合实际物理意义的漫反射被动非视域全影(DS-NLOS)数据集。构建被动非视域重建网络模型(Re-NLOS),采用视觉Transformer(Vi T)结构结合生成式对抗网络(GAN)提取采集的漫反射面图像的全局特征,并恢复隐藏物体形状。在DS-NLOS数据集上的实验结果表明,该方法能够从漫反射面上恢复隐藏物体的形状信息,在测试集20个类别的物体上的峰值信噪比(PSNR)和结构相似性(SSIM)相比漫反射面全影图像平均提高了5.85 d B和0.0381,对真实室内场景也具有一定的恢复能力。
文摘非视域成像是对探测器视线外被遮挡的物体进行光学成像的新兴技术,基于光锥变换反演法的非视域成像可以看作是一个反卷积的过程,传统维纳滤波反卷积方法是使用经验值或者反复尝试得到瞬态图像的功率谱密度噪信比(power spectral density noise-to-signal ratio,PSDNSR)进行维纳滤波反卷积,但非视域成像每个隐藏场景的PSDNSR都不同,先验估计难以适用.因此本文提出使用捕获瞬态图像的中频域信息来估计PSDNSR进行维纳滤波从而实现非视域成像.实验表明,基于中频域维纳滤波的非视域成像算法估计的PSDNSR能够落在一个重建效果较好的量级上.相比其他方法,本文算法能一步直接估计出PSDNSR,没有迭代运算,计算复杂度低,能够在保证重建效果的前提下提升重建效率.
