为实现自由飞行目的,研究了通过自由飞行航空数据网(free flight aeronautical data network,FFADN),采取多跳形式提供对空中和地面节点数据连通的通信方式。依托航空电信网(ATN),概述了FFADN的基本概念、应用背景、主要特点,介绍了FFAD...为实现自由飞行目的,研究了通过自由飞行航空数据网(free flight aeronautical data network,FFADN),采取多跳形式提供对空中和地面节点数据连通的通信方式。依托航空电信网(ATN),概述了FFADN的基本概念、应用背景、主要特点,介绍了FFADN研究现状,分析了FFADN关键技术,对FFADN的发展进行了展望,提出了民用FFADN网络模型,为未来ATN多层次网络的发展与应用提供了一定借鉴。展开更多
This paper examines the utility of high-resolution airborne RGB orthophotos and LiDAR data for mapping residential land uses within the spatial limits of suburb of Athens, Greece. Modem remote sensors deliver ample in...This paper examines the utility of high-resolution airborne RGB orthophotos and LiDAR data for mapping residential land uses within the spatial limits of suburb of Athens, Greece. Modem remote sensors deliver ample information from the AOI （area of interest） for the estimation of 2D indicators or with the inclusion of elevation data 3D indicators for the classification of urban land. In this research, two of these indicators, BCR （building coverage ratio） and FAR （floor area ratio） are automatically evaluated. In the pre-processing step, the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an nDSM （normalized digital surface model） comprised of upsampled elevation data with considerable improvement regarding region filling and ＂straightness＂ of elevation discontinuities. Following this step, a MFNN （multilayer feedforward neural network） is used to classify all pixels of the AOI into building or non-building categories. The information derived from the BCR and FAR building indicators, adapted to landscape characteristics of the test area is used to propose two new indices and an automatic post-classification based on the density of buildings.展开更多
文摘为实现自由飞行目的,研究了通过自由飞行航空数据网(free flight aeronautical data network,FFADN),采取多跳形式提供对空中和地面节点数据连通的通信方式。依托航空电信网(ATN),概述了FFADN的基本概念、应用背景、主要特点,介绍了FFADN研究现状,分析了FFADN关键技术,对FFADN的发展进行了展望,提出了民用FFADN网络模型,为未来ATN多层次网络的发展与应用提供了一定借鉴。
文摘This paper examines the utility of high-resolution airborne RGB orthophotos and LiDAR data for mapping residential land uses within the spatial limits of suburb of Athens, Greece. Modem remote sensors deliver ample information from the AOI （area of interest） for the estimation of 2D indicators or with the inclusion of elevation data 3D indicators for the classification of urban land. In this research, two of these indicators, BCR （building coverage ratio） and FAR （floor area ratio） are automatically evaluated. In the pre-processing step, the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an nDSM （normalized digital surface model） comprised of upsampled elevation data with considerable improvement regarding region filling and ＂straightness＂ of elevation discontinuities. Following this step, a MFNN （multilayer feedforward neural network） is used to classify all pixels of the AOI into building or non-building categories. The information derived from the BCR and FAR building indicators, adapted to landscape characteristics of the test area is used to propose two new indices and an automatic post-classification based on the density of buildings.
