A new Likelihood Ratio for supervised classification of fully polarimetric SAR data: An application for sea ice type mapping

One of the potential applications of polarimetric Synthetic Aperture Radar (SAR) data is the classification of land cover, such as forest canopies, vegetation, sea ice types, and urban areas. In contrast to single or dual polarized SAR systems, full polarimetric SAR systems provide more information about the physical and geometrical properties of the imaged area. This paper proposes a new Bayes risk function which can be minimized to obtain a Likelihood Ratio (LR) for the supervised classification of polarimetric SAR data. The derived Bayes risk function is based on the complex Wishart distribution. Furthermore, a new spatial criterion is incorporated with the LR classification process to produce more homogeneous classes. The application for Arctic sea ice mapping shows that the LR and the proposed spatial criterion are able to provide promising classification results. Comparison with classification results based on the Wishart classifier, the Wishart Likelihood Ratio Test Statistic (WLRTS) proposed by Conradsen et al. (2003) and the Expectation Maximization with Probabilistic Label Relaxation (EMPLR) algorithm are presented. High overall classification accuracy of selected study areas which reaches 97.8% using the LR is obtained. Combining the derived spatial criterion with the LR can improve the overall classification accuracy to reach 99.9%. In this study, fully polarimetric C-band RADARSAT-2 data collected over Franklin Bay, Canadian Arctic, is used.     

A data assimilation approach to discharge estimation from space

River discharge is currently monitored by a diminishing network of gauges, which provide a spatially incomplete picture of global discharges. This study assimilated water level information derived from a fused satellite Synthetic Aperture Radar (SAR) image and digital terrain model (DTM) with simulations from a coupled hydrological and hydrodynamic model to estimate discharge in an un‐gauged basin scenario. Assimilating water level measurements led to a 79% reduction in ensemble discharge uncertainty over the coupled hydrological hydrodynamic model alone. Measurement bias was evident, but the method still provided a means of improving estimates of discharge for high flows. The study demonstrates the potential of currently available synthetic aperture radar imagery to reduce discharge uncertainty in un‐gauged basins when combined with model simulations in a data assimilation framework, where sufficient topographic data are available. The work is timely because in the near future the launch of satellite radar missions will lead to a significant increase in the volume of data available for space‐borne discharge estimation. Copyright © 2009 John Wiley & Sons, Ltd.     

地震监测SAR卫星需求分析与轨道设计

通过对SAR卫星在地震监测任务中的需求分析。确定者这种卫星应该具备干涉和大视角两种工作模式。并利用太阳同步准回归轨道设计方法初步进行了地震监测SAR卫星的轨道设计。     

Wave Height Estimation from SAR Imagery

This paper presents a method developed for estimating wave height from synthetic aperture radar (SAR) imagery without prior assumption of noise distribution. It is based on two-dimenslonal ocean wave spectra retrieved from fully calibrated SAR images. Wen‘s spectrum was used as first-guess wave spectrum in the retrieval process. Comparison of the estimated wave height obtained by this method from two ERS-1 SAR subimages dated 23 July 1994 with in-situ measurements showed that the method works well.     

参照标准点探测PS法

提出参照标准点探测PS法。既考虑PS的强散射特性,又顾及其稳定性,且初选时振幅阈值的设置参照每景影像中PS标准点的振幅值,精选时振幅离差阈值的设置也参照这些PS标准点的振幅离差值,都不需要人工设定。选取澳门半岛为实验区,以18景TerraSAR-X为数据源,进行PS探测实验。结果表明,相比于传统的PS探测方法,本方法能更快速、有效、可靠地探测出SAR影像中的PS。     

基于Storm的地理编码引擎

近年来,随着Web 2.0和具有位置感知能力的移动计算设备的普及应用,带来了大量含有时空语义的地理大数据。在这个背景下,以地图厂商人工方式和半自动方式更新地名地址库为基础的传统地理编码服务,已难以满足新的应用需求。本文提出一种地理大数据驱动的自适应地理编码引擎的构建思路和方法,通过引入实时计算和流式计算平台Storm,实现对网络中的多源地理大数据的爬取与实时处理,加速地名地址库及相关资源的生成与更新过程,并给出了相适应的地理编码匹配方法。在实时流式计算框架基础上,通过JTS Topology Suite实现流式并行的空间操作,设计并实现了基于Storm的地理编码引擎原型系统,满足多源地理大数据的高效处理和地理编码要求。实验结果表明,该引擎通过实时流式处理可加速地址库的扩充与更新过程,并且利用地址库持续更新的方法,提升了地理编码的匹配率和定位准确度。     

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Analysis of multi-dimensional SAR for determining the thickness of thin sea ice in the Bohai Sea

Flat thin ice (<30 cm thick) is a common ice type in the Bohai Sea, China. Ice thickness detection is important to offshore exploration and marine transport in winter. Synthetic aperture radar (SAR) can be used to acquire sea ice data in all weather conditions, and it is a useful tool for monitoring sea ice conditions. In this paper, we combine a multi-layered sea ice electromagnetic (EM) scattering model with a sea ice thermodynamic model to assess the determination of the thickness of flat thin ice in the Bohai Sea using SAR at different frequencies, polarization, and incidence angles. Our modeling studies suggest that co-polarization backscattering coefficients and the co-polarized ratio can be used to retrieve the thickness of flat thin ice from C- and X-band SAR, while the co-polarized correlation coefficient can be used to retrieve flat thin ice thickness from L-, C-, and X-band SAR. Importantly, small or moderate incidence angles should be chosen to avoid the effect of speckle noise.     

多尺度相关性分析的机载双极化InSAR轨道误差校正方法

在高分辨率机载干涉SAR成像处理过程中,由于载机飞行过程中偏离理想轨迹,需要高精度的惯导系统和GPS系统记录载机的运动轨迹并进行运动补偿.然而,由于目前传感器导航精度的限制,在完成运动补偿处理后仍然存在轨道误差,从而影响干涉相位的精度,本文提出了一种机载双极化InSAR轨道误差去除方法.该方法利用小波多尺度分析对不同极...     

基于机理的合成孔径雷达系统成像误差理论分析

目前在遥感和GIS中,关于误差和不确定性研究的主要方法有两种: 概率统计和机理模拟。本文论述了二者的特点,详细讨论了影响合成孔径雷达成像的诸多因素,提出利用机理的方法分析SAR系统成像误差。本方法是从合成孔径雷达成像机理的研究出发,利用控制论的观点来解释、分析影响合成孔径雷达成像的诸多误差因素并建立误差传播模型。这不仅对研制合成孔径雷达的设计具有指导意义,而且对遥感数据的质量控制也有重要意义。