小波离散点插值处理在综合孔径微波辐射计中的应用

土壤湿度和海水盐度是影响全球气候和水体循环的重要因素,但是对土壤湿度和海水盐度的测量都需要较低的微波测量频率,这时天线孔径和空间分辨率的矛盾就更加突出,而应用综合孔径微波辐射计就是在减小天线孔径和重量的前提下提高空间分辨率的一项新兴的被动微波遥感技术。综合孔径微波辐射计测量的是视场范围内亮温分布对于天线阵中不同基线的干涉天线单元之间的可见度函数分量,根据测得的可见度函数可以反演得到亮温图像。在综合孔径微波辐射计设计中,天线阵列设计和成像反演算法是两项关键技术。提出一种基于小波变换的离散点插值方法,它对二维综合孔径微波辐射计的Fourier成像反演算法进行了修正处理。     

一种一维综合孔径微波辐射计的定标方法

综合孔径微波辐射计是被动微波遥感发展的新方向。综合孔径技术利用了以小口径天线阵列合成大的观测口径的技术，解决了在较低频率时天线物理口径要足够大才能得到期望的空间分辨率的严重缺陷。土壤湿度和海水盐度是影响全球气候和水气循环的重要因素。这些参数一般是在L波段范围观测得到，综合孔径辐射计就是减少天线孔径和重量，并最终可以观测反演出这两个参数的一项新兴技术。综合孔径辐射计不同于全功率辐射计，它测量的是视场亮温分布对于天线阵中不同基线长度的可视度函数分量。它的系统主体是稀疏天线阵和多通道相关接收机。在实际应用中，要得到土壤湿度等参数的反演，较高的系统亮温分辨率以及亮温与测量量之间的准确对应是至关重要的，这即是定标工作要完成的任务。定标直接影响微波辐射图解译和判读的准确度，是实现定量化微波遥感的前提。针对一雏综合孔径辐射计系统，给出了一种定标方案。其中分析了天线阵以及多通道相关接收机部分的定标，由得到的矩阵形式的空间频率响应信息推出了亮温图像的反演公式。     

干涉式被动微波成像技术

近年来,大气、海洋和陆地土壤水分遥感的应用对被动微波遥感器的空间分辨率提出了越来越高的要求。但是传统的微波辐射成像技术在分辨率达到一定高度时,由于波束在每个分辨像元的驻留时间越来越短,以致对辐射亮温的测量分辨率产生了影响,这就使得采用传统扫描成像技术来提高空间分辨率的技术路径成为不可能。文章较为详细地综述了近年来发展起来的一种新的微波辐射成像技术--干涉式被动微波成像技术（或称为综合孔径微波辐射成像技术）。在阐述其原理时,采用了新颖的切入点,从被观测目标的空间频谱特征和如何对其进行采样入手,试图从另一个角度揭示干涉式辐射成像技术的基本原理,并使其更加系统化。在介绍实际的成像系统时,将重点放在二维成像系统上,其中时钟扫描成像方式和伪极网格图像反演方法是作者最新的研究成果。     

基于变密度稀疏采样的微波辐射干涉测量反演成像方法

针对微波辐射干涉测量在频域中进行测量值具有低频信息较少、高频信息丰富,且低频信息和高频信息分布比较集中的特点,提出了一种基于变密度稀疏采样的微波辐射干涉测量方法.该方法将测量的频域信息进行分块,根据不同块所包含信息量的不同,利用变密度进行稀疏采样.考虑微波辐射图像本身具有的梯度稀疏性和局部光滑性特征,建立全变差正则化约束的成像模型,并采用交替迭代算法进行微波辐射图像最优重构.仿真和实验结果表明,结合变密度稀疏采样方法和交替迭代重构算法能够快速、准确地反演高分辨率微波辐射图像;在总采样率相同的情况下,该方法能够大幅度提高反演图像的分辨率,且对低采样率情况效果显著.     

基于人工神经网络和AMSR2多频微波亮温的北疆地区雪深反演

被动微波遥感数据是进行积雪深度反演的重要资料,不同频率微波信号对积雪响应不同。利用人工神经网络(Artificial Neural Network,ANN)方法结合新疆北部地区积雪观测资料建立AMSR2(Advanced Microwave Scanning Radiometer 2)微波亮温(Brightness Temperature,TB)、地理位置、地形因子与雪深的隐含关系,以实现通过亮温、地理位置、地形因子估算北疆地区积雪深度,并分析微波极化方式、位置以及地形的不同组合方式对雪深反演效果的影响。实验结果表明:水平极化对雪深反演的影响大于垂直极化,纬度对雪深的影响大于经度,地表粗糙度和坡向对雪深的影响大于高程和坡度,并且位置和地形因子对雪深影响作用相当。最终通过4种优选模型的误差空间分布对比发现,综合亮温、经纬度、坡度、坡向的ANN输入模型能够较好的反映北疆地区积雪分布状况,训练集的站点平均误差在-7~6cm之间,该组合模型作为神经网络的输入能够较为合理地获取北疆地区雪深模拟值。     

多波段微波辐射计反演大气温湿廓线性能分析

为了为星载、机载以及地基微波大气温湿廓线探测仪通道的设置、大气参数反演指标的论证、反演算法的开发以及反演产品的质量评定提供参考依据,基于快速辐射传输模式(RTTOV10)和大气参数廓线库,建立了基于神经网络的微波大气温湿廓线反演性能分析方法,分析了反演方法、通道选择、亮温观测误差和地表比辐射率等因素对大气温湿廓线反演性能的影响。模拟试验分析表明:1神经网络反演算法显著优于线性统计回归反演算法,特别是对亮温观测噪声的敏感性相对较弱;2183.31GHz附近的水汽探测通道能够为大气温度廓线反演提供一定的信息;118.75GHz附近的温度探测通道对整个大气的温度反演均有明显影响,在200hPa附近误差的影响量达0.4K;350~60GHz和118.75GHz附近的温度探测通道对基于183.31GHz附近通道的湿度廓线反演具有重要影响,而且存在一定的互补性;4微波亮温观测误差以及地表比辐射率假定对大气温湿廓线反演有着显著影响。     

干涉式微波辐射计成像算法以及天线阵采样方案分析

对旋转扫描干涉式微波辐射计成像算法进行研究,提出了基于伪极网格傅立叶变换的成像算法,利用一维插值以及一维FFT即可实现图像重建,既提高了成像精度,又保证了成像速度。以3种8单元天线排列方案阵为例,利用本文方法和基于线性插值的笛卡儿网格方法分别进行成像模拟,模拟结果验证了本文方法的优越性,同时表明天线阵排列方案对成像结果影响很大,天线阵基线均匀性越好,数量越多,成像质量就越好。直线阵无法实现基线数量与均匀性的统一,而平面阵可以在较好均匀性情况下实现最多的基线数,是天线阵排列的首选方案。     

综合孔径微波辐射计及其反演成像

介绍了综合孔径微波辐射计的基本思想,以及对地观测的综合孔径微波辐射计与综合孔径射电天文望远镜的区别。给出了综合孔径微波辐射计任意基线干涉测量的可见度函数与基线参数的关系,推导了可见度函数的一般表达式和综合孔径微波辐射计亮度温度反演的一般函数空间表示。给出了亮度温度反演的数值算法和算例。     

积雪微波辐射亮温对积雪参数的敏感性分析——以多层积雪微波辐射模型为例

积雪的被动微波辐射亮温信号十分复杂,深度、温度、粒径、密度、液态水含量,以及下垫面的状况都会不同程度的影响积雪层的亮度温度。本文利用多层积雪微波辐射模型（MEMLS）分别针对各个输入参数对模拟亮温的影响进行了分析,发现粒径是敏感性最高的模型参数,湿度、深度、密度、积雪温度次之。模型模拟结果表明,当雪深小于50cm时,雪深可以近似地表示为19和37GHz的亮温差的线性函数 |当雪深大于50cm后,随着雪深的增加,亮温差增加幅度变小,趋向于饱和。在建立积雪深度反演公式时,粒径和密度会影响公式的拟合系数。把一定区域内积雪粒径和密度看作是相同的值,这可能是造成被动微波遥感反演雪深和雪水当量误差的原因之一。被动微波无法反演湿雪的雪深和雪水当量,但可以有效识别干雪和湿雪,为水文模拟以及农业灌溉提供科学的依据和信息。积雪温度对积雪辐射亮温影响较小,而且在对积雪深度进行反演时,两个频率亮温值相减,温度的影响也被降到了最低。     

L波段微波辐射计冷空定标源亮温分析

欧空局SMOS任务的塔克拉玛干沙漠地面定标实验,采用L波段微波辐射计测量塔克拉玛干沙漠的土壤亮温。此辐射计的定标采用冷空作为低温定标源。由于冷空的情况比较复杂,在L波段,其亮温受到诸多因素的影响。根据日地关系、太阳与天线的位置关系,建立太阳辐射模型,得出了任意时刻太阳进入天线视场范围内的影响亮温值,并简要分析了月球、其它星球、中性氢原子分立谱发射和热源连续谱发射对辐射亮温的影响,为选取冷空作为低温定标源提供了理论及数据参考。     

Spatial information of high-frequency brightness temperatures for passive microwave rainfall retrievals

The effect of rainfall inhomogeneity within the sensor field of view (FOV) affects significantly the accuracy of rainfall retrievals causing the so-called beam-filling error. Observational analyses of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) data suggest that the beam-filling error can be classified in terms of the mean rain rate and the rainfall inhomogeneity parameter or coefficient of variation (CV_R, standard deviation divided by mean). The dependence of the beam-filling error on the rain rate and CV_R has been confirmed quantitatively using a single channel at 19.4 GHz. It is also found significantly different beam-filling errors for the two different regions, the East and West Pacific, where the spatial and vertical distributions of rainfalls are different. It is also observed that the vertical distribution of rainfall is related to the spatial variability of rainfall (CV_R) and similarly to the spatial variability of TMI 85.5 GHz brightness temperature (CV _Tb). Based on these findings, this study exploits the CV _Tb to correct the beam-filling error in a direct inversion from a rainfall (R) and brightness temperature (T _b) curve at a single frequency, and to reduce the retrieval error in the context of a Bayesian-type inversion method for multi-frequency rainfall retrievals. Both the experiments suggest that the spatial variability of the high-frequency radiometer data appears to contain useful information for retrievals.     

Quantifying the uncertainty in passive microwave snow water equivalent observations

Passive microwave sensors (PM) onboard satellites have the capability to provide global snow observations which are not affected by cloudiness and night condition (except when precipitating events are occurring). Furthermore, they provide information on snow mass, i.e., snow water equivalent (SWE), which is critically important for hydrological modeling and water resource management. However, the errors associated with the passive microwave measurements of SWE are well known but have not been adequately quantified thus far. Understanding these errors is important for correct interpretation of remotely sensed SWE and successful assimilation of such observations into numerical models.This study uses a novel approach to quantify these errors by taking into account various factors that impact passive microwave responses from snow in various climatic/geographic regions. Among these factors are vegetation cover (particularly forest cover), snow morphology (crystal size), and errors related to brightness temperature calibration. A time-evolving retrieval algorithm that considers the evolution of snow crystals is formulated. An error model is developed based on the standard error estimation theory. This new algorithm and error estimation method is applied to the passive microwave data from Special Sensor Microwave/Imager (SSM/I) during the 1990-1991 snow season to produce annotated error maps for North America. The algorithm has been validated for seven snow seasons (from 1988 to 1995) in taiga, tundra, alpine, prairie, and maritime regions of Canada using in situ SWE data from the Meteorological Service of Canada (MSC) and satellite passive microwave observations. An ongoing study is applying this methodology to passive microwave measurements from Scanning Multichannel Microwave Radiometer (SMMR); future study will further refine and extend the analysis globally, and produce an improved SWE dataset of more than 25 years in length by combining SSMR and SSM/I measurements.     

Special sensor microwave imager data in field experiments: FIFE-1987

Estimation of regional soil moisture is of importance to the hydrological modelling community for validating the accuracy of model predictions and to the climate community for use in regional and global climate models. Soil moisture estimation is done in two ways, viz., estimation using models and estimation using satellite data. Both these methods have to be corroborated using in situ field data. The present paper examines the feasibility of using observations of brightness temperature from the Special Sensor Microwave Imager (SSM/I) for small scale/catchment scale studies. The simulations of the 19 and 37 GHz brightness temperatures are carried out for a period of 13 days between 6 and 21 August 1987. The comparisons between the simulated and the observed brightness temperatures are good and the errors can be explained based on the atmospheric and surface conditions. Sensitivity analysis of the brightness temperatures to leaf area index, soil moisture and soil temperatures shows interesting characteristics. The errors in the simulated brightness temperatures can be explained by variability in the input parameters. This study shows that such an approach holds promise for local and regional studies of the land surface.     

基于光谱相似性的高光谱图像超分辨率算法

光谱相似性是指高光谱图像中的大量像元具有相似光谱的性质.提出了一种基于光谱相似性的高光谱遥感图像超分辨率算法,利用遥感图像中广泛存在的结构自相似性提升图像的空间分辨率,利用高光谱图像的低维子空间性通过主成分分析降低光谱维数提高运算效率,利用具有相似光谱的像元构建光谱约束项保证重建图像光谱的准确性.该算法在将单波段图像超分辨率方法推广到处理具有数百、乃至上千波段的高光谱图像过程中,既保证了重建图像光谱的准确性,又具有较高的运算效率.实验表明,与双三次插值和基于稀疏表示与光谱正则化约束的高光谱图像超分辨率算法相比,该算法具有更高的空间分辨率提升能力和更好的光谱保真能力.     

Visualization Blackboard-visualizing 3D PET images

Characteristics of positron emission tomography (PET) images that limit human ability to accurately perceive the information the images contain are discussed. They are relatively low spatial resolution, a lack of apparent anatomical information and the expression of metabolic activity in terms of brightness levels (gray levels), which are not efficiently determined by the human visual system. These affect how clearly the 3-D structures contained in the reconstructed 3-D images can be seen. The use of pseudocolor to visualize different levels of activity expressed by brightness and shading to accentuate depth and shape information is described. To further enhance the brightness contrast of a surface with its neighboring areas, stereo and motion were used as depth cues     

A model for retrieving total sea ice concentration from a spaceborne dual-polarized passive microwave instrument operating near 90 GHz

Abstract

An algorithm has been developed for estimating total ice concentration from spaceborne high-frequency passive microwave instrumentation. The algorithm is intended for use with the coming Special Sensor Microwave/Imager (SSM/I) data giving a spatial resolution of 12 km. It is based on radiation physics and detailed millimetre wave surface signature measurements and can therefore be applied to other similar data. However, due to large effects on the signals caused by time varying atmospheric conditions and radiation properties of the ice, the algorithm is made self-adjusting. The atmospheric effects are implicitly treated as a smooth function of the ice concentration with tie points over open ocean and 100 per cent ice for each orbit. This means that the main errors are due to patches of heavy clouds and ice floes with atypical radiation properties. An error analysis indicates possible errors of the order of 5 percent for concentrations representative for the Arctic Basin, increasing with decreasing concentration.     

Tracking of Antarctic tabular icebergs using passive microwave radiometry

Passive microwave images of Antarctica from the Special Sensor Microwave Imager (SSM/I) are used to track two giant tabular icebergs that originated from the Larsen ice shelf in 1986. Since microwave radiation is relatively insensitive to weather and unaffected by lighting conditions, the SSM/I instrument provides all-weather, year-round viewing. The icebergs are visible almost every day giving an ideal temporal resolution for tracking their motion. One of the icebergs was tracked until October 1988, six months after its last position noted in the Navy/NOAA Joint Ice Centre ice charts. The tracks of both icebergs reveal motion in sympathy with observed oceanographic currents and eddies.     

An efficient error estimation method for antenna distortions in space based radar

The phased array antenna plays an important role in determining the performance achievable in a space based radar system. However, the array position errors caused by the antenna distortions in the complex cosmic vacuum environment will distort the antenna pattern, which significantly degrades the performance of the radar systems. Most of the conventional estimation methods are proposed based on the random position errors without the consideration of the distortion character for spaceborne arrays, resulting in a large estimation error. In this paper, to address this issue, an efficient array error estimation method is proposed to compensate the antenna distortion errors. In the proposed method, the initial sensor positions can be obtained based on the measured results from the units distributed over the array surface. Then the distortion model parameters can be estimated by solving an optimization problem. Based on this, the sensor positions can be corrected with high accuracy at the lth iteration. In this way, the proposed method can improve the accuracy and the robustness of the estimation simultaneously. Several simulated results are presented to validate the proposed method for the position error estimation in a space based radar system.     

一种基于残差的遥感图像融合新方法

针对遥感图像融合问题,提出了一种基于残差的遥感图像融合新方法。该方法借助于主成分分析(principal component analysis,PCA),通过对多光谱图像的残差图像和全色图像的残差图像进行融合来恢复出多光谱图像的高分辨率残差图像,以实现多光谱图像和全色图像的融合。实验结果的主观视觉效果和客观统计参数分析都表明,新方法不仅较大地增强了融合图像的空间细节表现能力,而且很好地保留了多光谱图像的光谱信息,其性能优于现有的HIS(hue-intensity-saturation)变换融合方法、PCA融合方法和小波变换(wavelet transform,WT)融合方法。     

Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach

An approach is evaluated for the estimation of soil moisture at high resolution using satellite microwave and optical/infrared (IR) data. This approach can be applied to data acquired by the Visible/Infrared Imager Radiometer Sensor Suite (VIIRS) and a Conical Scanning Microwave Imager/Sounder (CMIS), planned for launch in the 2009–2010 time frame under the National Polar-Orbiting Operational Environmental Satellite System (NPOESS). The approach for soil moisture estimation involves two steps. In the first step, a passive microwave remote sensing technique is employed to estimate soil moisture at low resolution (～25?km). This involves use of a simplified radiative transfer model to invert dual-polarized microwave brightness temperature. In the second step, the microwave-derived low-resolution soil moisture is linked to the scene optical/IR parameters, such as Normalized Difference Vegetation Index (NDVI), surface albedo, and Land Surface Temperature (LST). The linking is based on the ‘Universal Triangle’ approach of relating land surface parameters to soil moisture. The optical/IR parameters are available at high resolution (～1?km) but are aggregated to the microwave resolution for the purpose of building the linkage model. The linkage model in conjunction with high-resolution NDVI, surface albedo and LST is then used to disaggregate microwave soil moisture into high-resolution soil moisture. The technique is applied to data from the Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) acquired for the Southern Great Plains (SGP-97) experiment conducted in Oklahoma in June–July 1997. An error budget analysis performed on the estimation procedure shows that the rms error in the estimation of soil moisture is of the order of 5%. Predicted soil moisture results at high resolution agree reasonably well with low resolution results in both magnitude and spatio-temporal patterns. The high resolution results are also compared with in situ (0–5?cm deep) point measurements. While the trends are similar, the soil moisture estimates in the two cases are different. Issues involving comparison of satellite derived soil moisture with in situ point measurements are also discussed.