针对传统局部不变特征的景象匹配算法冗余点多、实时性差、抗几何变换不突出的情况,提出基于CenSurE-star的无人机(UAV)景象匹配算法。首先采用Cen Sur E特征星型滤波器(CenSurE-star)提取基准图和实时图中的特征点,并生成FREAK二进制...针对传统局部不变特征的景象匹配算法冗余点多、实时性差、抗几何变换不突出的情况,提出基于CenSurE-star的无人机(UAV)景象匹配算法。首先采用Cen Sur E特征星型滤波器(CenSurE-star)提取基准图和实时图中的特征点,并生成FREAK二进制描述符;然后将汉明距离作为特征点的相似性判定度量,采用K近邻距离比值的方法提取匹配点对;最后利用基于RANSAC的定位模型得到空间几何变换关系,实现图像匹配并获取定位点经纬坐标。算法性能评价实验表明,本文算法不仅相对于SIFT、SURF、ORB算法,对各种变换具有更好的鲁棒性,而且相对于改进的SIFT、SURF算法处理时间有更大程度的缩短,算法定位误差在0.8个像素内,尺度误差在0.02倍内,旋转角度误差在0.04°内。基于算法进行外场飞行实验,实验证明算法定位精度较高,可以适应地貌信息较少的环境,并能满足无人机视觉辅助导航的需求。展开更多
Large areas assessments of forest bioinass distribution are a challenge in heterogeneous landscapes, where variations in tree growth and species composition occur over short distances. In this study, we use statistica...Large areas assessments of forest bioinass distribution are a challenge in heterogeneous landscapes, where variations in tree growth and species composition occur over short distances. In this study, we use statistical and geospatial modeling on densely sample.d forest biomass data to analyze the relative importance of ecological and physiographic variables as determinants of spatial variation of forest biomass in the environmentally heterogeneous region of the Big Sur, California. We estimated biomass in 280 forest Plots （one plot per 2.85 km2） and meas- ured an array of ecological （vegetation community type, distance to edge, amount of surrounding non-forest vegetation, soil properties, fire history） and physiographic drivers （elevation, potential soil moisture and solar radiation, proximity to the coast） of tree growth at each plot location. Our geostatistical analyses revealed that biomass distribution is spatially structured and autocorrelated up to 3.1 kin. Regression tree （RT） models showed that both physiographic and ecological factors influenced bio- mass distribution. Across randomly selected sample densities （sample size 112 to 280）, ecological effects of vegetation community type and distance to forest edge, and physiographic effects of elevation, potential soil moisture and solar radiation were the most consistent predictors of biomass. Topographic moisture index and potential solar radiation had apositive effect on biomass, indicating the importance of topographically- mediated energy and moisture on plant growth and biomass accumula- tion. RT model explained 35% of the variation in biomass and spatially autocorrelated variation were retained in regession residuals. Regression kriging model, developed from RT combined with kriging of regression residuals, was used to map biomass across the Big Sur. This study dem- onstrates how statistical and geospatial modeling can be used to dis- criminate the relative importance of physiographic and ecologic effects on forest biomass and develop spatial models to predict and map biomass distribution across a heterogeneous landscape.展开更多
文摘针对传统局部不变特征的景象匹配算法冗余点多、实时性差、抗几何变换不突出的情况,提出基于CenSurE-star的无人机(UAV)景象匹配算法。首先采用Cen Sur E特征星型滤波器(CenSurE-star)提取基准图和实时图中的特征点,并生成FREAK二进制描述符;然后将汉明距离作为特征点的相似性判定度量,采用K近邻距离比值的方法提取匹配点对;最后利用基于RANSAC的定位模型得到空间几何变换关系,实现图像匹配并获取定位点经纬坐标。算法性能评价实验表明,本文算法不仅相对于SIFT、SURF、ORB算法,对各种变换具有更好的鲁棒性,而且相对于改进的SIFT、SURF算法处理时间有更大程度的缩短,算法定位误差在0.8个像素内,尺度误差在0.02倍内,旋转角度误差在0.04°内。基于算法进行外场飞行实验,实验证明算法定位精度较高,可以适应地貌信息较少的环境,并能满足无人机视觉辅助导航的需求。
基金financially supported by the National Science Foundation (EF-0622770 and EF-0622677)the USDA Forest Service–Pacific Southwest Research Stationthe Gordon & Betty Moore Foundation
文摘Large areas assessments of forest bioinass distribution are a challenge in heterogeneous landscapes, where variations in tree growth and species composition occur over short distances. In this study, we use statistical and geospatial modeling on densely sample.d forest biomass data to analyze the relative importance of ecological and physiographic variables as determinants of spatial variation of forest biomass in the environmentally heterogeneous region of the Big Sur, California. We estimated biomass in 280 forest Plots （one plot per 2.85 km2） and meas- ured an array of ecological （vegetation community type, distance to edge, amount of surrounding non-forest vegetation, soil properties, fire history） and physiographic drivers （elevation, potential soil moisture and solar radiation, proximity to the coast） of tree growth at each plot location. Our geostatistical analyses revealed that biomass distribution is spatially structured and autocorrelated up to 3.1 kin. Regression tree （RT） models showed that both physiographic and ecological factors influenced bio- mass distribution. Across randomly selected sample densities （sample size 112 to 280）, ecological effects of vegetation community type and distance to forest edge, and physiographic effects of elevation, potential soil moisture and solar radiation were the most consistent predictors of biomass. Topographic moisture index and potential solar radiation had apositive effect on biomass, indicating the importance of topographically- mediated energy and moisture on plant growth and biomass accumula- tion. RT model explained 35% of the variation in biomass and spatially autocorrelated variation were retained in regession residuals. Regression kriging model, developed from RT combined with kriging of regression residuals, was used to map biomass across the Big Sur. This study dem- onstrates how statistical and geospatial modeling can be used to dis- criminate the relative importance of physiographic and ecologic effects on forest biomass and develop spatial models to predict and map biomass distribution across a heterogeneous landscape.
