混沌蜂群优化的NSST域多光谱与全色图像融合

为有效融合多光谱图像的光谱信息和全色图像的空间细节信息,提出了一种基于混沌蜂群优化和改进脉冲耦合神经网络(PCNN)的非下采样Shearlet变换(NSST)域图像融合方法。首先对多光谱图像进行Intensity-HueSaturation(IHS)变换,全色图像的直方图按照多光谱图像亮度分量的直方图进行匹配;然后分别对多光谱图像的亮度分量和新全色图像进行NSST变换,对低频分量使用改进加权融合算法进行融合,以互信息作为适应度函数,利用混沌蜂群算法找到最优加权系数。对高频分量采用改进脉冲耦合神经网络(PCNN)方法进行融合,再经NSST逆变换和IHS逆变换得到融合图像。本文方法在主观视觉效果和信息熵、光谱扭曲度等客观定量评价指标上优于基于IHS变换、基于非下采样Contourlet变换(NSCT)和非负矩阵分解(NMF)、基于NSCT和PCNN等5种融合方法。本文方法在提升图像空间分辨率的同时,有效地保留了光谱信息。     

Mapping natural and urban environments using airborne multi-sensor ADS40–MIVIS–LiDAR synergies

The recent and forthcoming availability of high spatial resolution imagery from satellite and airborne sensors offers the possibility to generate an increasing number of remote sensing products and opens new promising opportunities for multi-sensor classification. Data fusion strategies, applied to modern airborne Earth observation systems, including hyperspectral MIVIS, color-infrared ADS40, and LiDAR sensors, are explored in this paper for fine-scale mapping of heterogeneous urban/rural landscapes. An over 1000-element array of supervised classification results is generated by varying the underlying classification algorithm (Maximum Likelihood/Spectral Angle Mapper/Spectral Information Divergence), the remote sensing data stack (different multi-sensor data combination), and the set of hyperspectral channels used for classification (feature selection). The analysis focuses on the identification of the best performing data fusion configuration and investigates sensor-derived marginal improvements. Numerical experiments, performed on a 20-km stretch of the Marecchia River (Italy), allow for a quantification of the synergies of multi-sensor airborne data. The use of Maximum Likelihood and of the feature space including ADS40, LiDAR derived normalized digital surface, texture layers, and 24 MIVIS bands represents the scheme that maximizes the classification accuracy on the test set. The best classification provides high accuracy (92.57% overall accuracy) and demonstrates the potential of the proposed approach to define the optimized data fusion and to capture the high spatial variability of natural and human-dominated environments. Significant inter-class differences in the identification schemes are also found by indicating possible sub-optimal solutions for landscape-driven mapping, such as mixed forest, floodplain, urban, and agricultural zones.     

A semi-ellipsoid-model based fuzzy classifier to map grassland in Inner Mongolia,China

Remote sensing techniques offer effective means for mapping plant communities. However, mapping grassland with fine vegetative classes over large areas has been challenging for either the coarse resolutions of remotely sensed images or the high costs of acquiring images with high-resolutions. An improved hybrid-fuzzy-classifier (HFC) derived from a semi-ellipsoid-model (SEM) is developed in this paper to achieve higher accuracy for classifying grasslands with Landsat images. The Xilin River Basin, Inner Mongolia, China, is chosen as the study area, because an acceptable volume of ground truthing data was previously collected by multiple research communities. The accuracy assessment is based on the comparison of the classification outcomes from four types of image sets: (1) Landsat ETM+ August 14, 2004, (2) Landsat TM August 12, 2009, (3) the fused images of ETM+ with CBERS, and (4) TM with CBERS, respectively, and by three classifiers, the proposed HFC-SEM, the tetragonal pyramid model (TPM) based HFC, and the support vector machine method. In all twelve classification experiments, the HFC-SEM classifier had the best overall accuracy statistics. This finding indicates that the medium resolution Landsat images can be used to map grassland vegetation with good vegetative detail when the proper classifier is applied.     

机载LiDAR和高光谱融合实现温带天然林树种识别

将机载LiDAR(Light Detection and Ranging)与高光谱CASI(Compact Airborne Spectrographic Imager)数据融合,充分利用垂直结构信息和光谱信息进行温带森林树种分类,并与仅用高光谱数据的分类结果相比较,评估融合数据的树种分类能力。结合样地实测数据,首先用LiDAR获得的3维垂直结构信息对CASI影像上的林间空隙进行掩膜,提取林木冠层子集;然后对冠层子集分层掩膜,利用光谱曲线的一阶微分及曲线匹配技术,实现各树种训练样本的自动提取;利用SVM分类器对两种数据分类并比较精度。结果表明,融合数据的树种分类总体精度和Kappa系数(83.88%,0.80)优于仅使用CASI数据(76.71%、0.71),优势树种的制图精度为78.43%—89.22%,用户精度为75.15%—95.65%,整体也优于仅使用CASI的制图精度(68.51%—84.69%)和用户精度(63.34%—95.45%)。结果表明,机载LiDAR与CASI基于像元的融合对温带森林树种识别的精度较仅高光谱数据有较大提高。     

基于超分辨率重建的多时相MODIS与Landsat反射率融合方法

提出一种基于超分辨率重建的MODIS与Landsat反射率图像融合方法,以STARFM算法与超分辨率重建为基础,使用观测的MODIS和Landsat地表反射率图像预测给定时刻的Landsat合成反射率图像。该方法利用基于稀疏表示的超分辨率重建方法对MODIS图像进行分辨率增强,实验结果表明这一操作能够增加原MODIS图像的空间细节,有助于提高STARFM算法的预测精度;另一方面,考虑输入两个基时刻图像相差较大时原STARFM算法预测的反射率会存在"时间平滑"的问题,限制每次只使用一个基时刻MODIS和Landsat图像对进行STARFM预测,使用逐图像块选择策略,从由两个基时刻图像分别进行预测得到的两组预测图像中选择最优的预测,同样得到了优于STARFM算法的预测结果。     

考虑多普勒参数的SAR影像距离——共面方程及其定位

通过对合成孔径雷达（SAR）影像成像几何机理的分析,以传感器位置和姿态作为定向参数,构建了考虑多普勒参数的SAR影像距离—共面方程及基于该方程的SAR影像空中三角测量模型。以登封地区99景0.5 m分辨率机载非0多普勒SAR影像为试验数据,进行了单片定向、无控制点的自由网平差、稀少控制点的区域网平差及带有自检校参数的区域网平差等系列试验。与R-D模型相比,本文模型减少了定向参数的数目,实现了SAR影像与POS数据的联合平差处理,建立的带有自检校参数的SAR影像区域网平差模型,对提高稀少或无地面控制点条件下未进行（或低精度）几何定标SAR影像的区域网平差精度起到了显著作用。较少控制点条件下模型满足了丘陵和山区1∶5000/1∶10000航摄测图对空中三角测量精度的要求。     

北斗与GPS组合伪距单点定位精度分析

主要介绍了北斗与G PS组合单点定位时的原理和模型,根据自编程序,利用同济大学TJA站的数据进行计算及分析比较GPS伪距、北斗伪距、北斗/GPS组合伪距单点定位的精度。结果表明：北斗伪距单点定位的精度平面方向上达到3m以内,高程方向优于8 m ,满足普通导航定位的需求。北斗/G PS组合伪距单点定位精度明显优于单系统。在单系统观测条件差时,组合系统可以减少对单一系统的依赖性,而且还可以增强卫星定位的稳定性和可靠性。     

GPS／BeiDou 组合单点定位的定权与结果分析

利用Helmert方差分量估计方法为GPS/BeiDou组合单点定位不同系统观测值定权,获得了合理的权值。分析了开阔环境和遮挡环境两种情况下的动态GPS/BeiDou组合单点定位的精度。结果表明：在静态观测和动态观测中,组合单点定位与单独G PS单点定位相比精度有显著提高。     

天绘一号卫星无地面控制点摄影测量关键技术及其发展历程

本文介绍了天绘一号卫星立项前的预先研究和试验工作、摄影测量研究历程框架以及关键的学术思想。EFP多功能光束法平差结果表明:无地面控制点目标定位精度达到工程技术指标,并与美国SRTM相对精度相当。此外,笔者还简要地介绍了国外主要传输型卫星无地面控制点摄影测量历程。     

异源高分辨率光学卫星遥感影像联合定位

本文提出一种基于RFM和投影射线法的异源高分辨率光学卫星遥感影像定位算法:首先利用高分辨率遥感影像RFM正解模型迭代计算其反解模型;接着通过RFM正反解模型构建投影射线,最后采用迭代求取射线交点的方法确定地面三维实际坐标。实验对比结果表明,联合具有一定重叠度的异源高分辨率光学卫星遥感影像进行立体定位具有较强的可行性,并可达到一定的精度。