多波段多极化成像雷达图象识别森林类型效果分析

要陕西省宜川县三北防护林地区获取的航天飞机成像雷达ＳＩＲ－Ｃ／Ｘ－ＳＡＲ数据为基础,进行了森林类型的识别与分类,区分出针叶林、阔叶林和混交林３种森林类型,并通过从ＳＩＲ－Ｃ／Ｘ－ＳＡＲ数据中提取这３种类型森林的后向散射系数,分析了多波段、多极化雷达在森林类型识别中的作用。     

成像雷达遥感的生态学应用

成像雷达(SAR)遥感具有独特的全天时、全天候及对地物的穿透性与形态探测能力。尤其是近年来新型成像雷达遥感(极化、干涉)及数据处理技术的发展,从SAR遥感影像上获得的地表信息越来越多,这些为进一步推进成像雷达遥感数据在生态学中的应用提供了基础。总结了成像雷达遥感在陆地生态环境系统中的4类典型应用:①土地覆盖分类与植被制图;②估计森林生物量;③洪泛区与湿地监测;④其它生态环境变化过程的探测。最后分析了不同生态学应用中SAR系统参数的最优配置,这对于我国开展成像雷达遥感在陆地生态环境监测中的应用具有非常重要的参考意义。     

雷达遥感的地质学应用及其进展

合成孔径雷达(SAR)遥感以其独有的全天时、全天候观测能力和对地表的穿透性及形态探测能力,特别是现在新型成像雷达技术的发展,使之在地质学应用中具有独特的优势。结合SAR应用技术的发展阶段,即由单波段单极化到多波段多极化,再发展到现在极化测量和干涉测量阶段,综述了成像雷达遥感在地学中的应用,特别是对新型成像雷达技术(极化雷达、干涉雷达)的地学应用作了介绍。     

超宽带穿墙雷达三维快速成像算法

超宽带穿墙雷达是一种能够隔墙探测和定位的新型实时成像雷达系统,在城区巷战,反恐斗争和人质救援中应用非常广泛.穿墙雷达对成像的实时性要求非常高,传统的成像算法已不能满足计算时间上的要求.本文将基于逆边界散射变换的快速成像算法应用到穿墙雷达系统中,利用FDTD数值仿真验证了对隐藏在墙后目标的三维成像结果,并分析了墙壁对目标定位误差的影响.研究表明,与传统的后向投影成像算法相比,基于逆边界散射变换的成像算法计算效率高,实时性好,且能对目标边界形状清晰成像.     

雷达高度计在海冰厚度探测中的研究进展

海冰是全球变化的指示剂,海冰厚度是海冰最难探测的参数之一。文章对现有的海冰厚度探测方式以及近年来相关研究进行了全面回顾和分析,对卫星雷达高度计、合成孔径雷达反演冰厚所存在的问题进行了总结;重点分析了星下点观测模式的雷达高度计以及雷达干涉测量技术在海冰厚度探测中的应用,分析了三维成像雷达成像高度计在海冰探测中的潜力,以及三维成像雷达成像高度计在海冰厚度探测机理及数据处理方面存在的问题。     

复杂运动目标ISAR成像技术进展与展望

逆合成孔径雷达成像是宽带雷达实现目标信 息获取和精细描述的重要途径,但在成像过程中存在复杂运动导致散焦和成像分辨率不高两 个难题。针对目标复杂运动导致成像散焦问题,本文总结了微动对雷达波调制、进动目标成 像、 三维运动成像、分离部件成像等技术现状;针对成像分辨率不高问题,分析了多频段宽带回 波相参配准、融合成像等技术进展。本文介绍了雷达成像新技术,为解决上述两个难题带来 新的思路和途径,其中,雷达关联成像不依赖于雷达与目标的相对运动,可避免成像的复杂 运动补偿;太赫兹雷达成像容易实现大带宽和窄波束,获得目标精细雷达像。最后对新兴技 术的难点和发展趋势进行了展望。     

一种结合ARMA的高精度穿墙雷达成像算法

雷达穿墙检测是一项新颖的探测技术,简要介绍了一种基于连续波的双频多普勒雷达以及应用这种雷达检测障碍物后运动目标的基本成像算法。分析了这种算法的主要特点,并针对其主要的缺陷,提出了一种新颖的ARMA（Auto-Regressive Moving Average）修正模型,成功改善了多普勒雷达中普遍存在的低分辨率和频谱混叠问题。对传统算法和基于ARMA模型算法的成像进行了仿真,成像结果验证了改进算法的先进性和有效性。     

浅谈噪声雷达原理及其实践应用

本文分析了噪声雷达的工作原理并阐述了所具有的优点,并从现代常用的超宽频随机噪声成像雷达和伪随机码连续波雷达为例,浅谈了噪声雷达在军用和民用领域中的实践应用。     

新体制民用雷达与实时信息处理研究

随着天线制造技术、超宽带技术、合成孔径技术和信息处理技术的发展,雷达的体积不断减小,探测精度和成像分辨率大大提升,雷达开始在民用领域中活跃起来,尤其是应用于穿透成像、微波遥感成像、滑坡监测和机场异物检测等领域的民用雷达发展十分迅速。为了让民用雷达在复杂的自然环境中具有更高、更稳定的性能,雷达信息处理技术一直在不断创新。本文介绍了民用雷达的新趋势和新技术,以及探墙雷达、微型SAR、边坡雷达和异物检测（Foreign object debris,FOD)雷达实时信息处理的关键问题和解决方案。     

ISAR成像中MTRC校正的新插值算法

研究雷达成像优化问题,在雷达成像中,双线性插值方法是一种常用、而且高效的校正散射点越分辨单元走动(MTRC)现象的插值算法,但由于其插值精度不高,会产生图像模糊的问题。为解决上述问题,采用目标回雷达波数据结构,并借鉴双线性插值方法进行仿真试验,提高了插值的精度,改善最终成像质量。通过对不同目标进行成像,验证所推导出的新的插值格式,不仅对越分辨单元走动现象有良好的校正能力,而且与应用双线性插值方法所取得的雷达成像仿真结果相比较,使用所推的双线插值格式所得结果更加清晰。针对复杂目标的仿真结果表明,改进插值格式能很好地适用于复杂目标的成像计算,具有较好的实用性。     

COSMO雷达数据在多云多雾地区土地变化监测研究

近年来新型成像雷达遥感(极化、干涉)及数据处理技术的发展,SAR遥感影像上获得的地表信息越来越多,如何利用雷达信息探测土地变化成为研究的新课题。但是雷达影像不同于光学影像,目前雷达数据解译仍存在着一些困难。本文主要针对多云多雾地区雷达数据土地变化监测,以四川成都地区COSMO数据为例,利用雷达相干影像,后向散射强度,强度比值影像,提出一种新的雷达处理手段,减少了雷达数据土地变化监测的工作量,提高工作效率。     

Using multi-source data from lidar,radar, imaging spectroscopy,and national forest inventories to simulate forest carbon fluxes

Terrestrial biosphere carbon dynamics are the most uncertain elements of the global carbon budget. Carbon stocks estimated using spatially extensive remote sensing are crucial in reducing this uncertainty, and using these stocks as initial conditions to biosphere models can improve carbon flux predictions beyond the site level. Yet remote-sensing data are not always consistently available for large regions, so methods assessing carbon uncertainty using data sources in one location may not be transferable to another. This study assesses the use of multiple-source data from lidar, radar, imaging spectroscopy, and national forest inventories to derive forest structure and composition necessary to initialise the Ecosystem Demography model (ED2), and hence evaluate short-term carbon flux uncertainty over Harvard Forest, Massachusetts. ED2 was initialized using forest structure and composition derived from lidar and national forest inventories, radar and national forest inventories, lidar and imaging spectroscopy, and radar and imaging spectroscopy resulting in net ecosystem productivity uncertainty of 26.3%, 41.9%, 19.6%, and 20.2%, respectively, compared to ground-based forest inventory initializations. This study uniquely offers a multitude of methods to estimate forest ecosystem state, with resulting carbon uncertainties, transferable to regions with potentially different data availability. Furthermore, in preparation for satellite radar, lidar, and imaging spectrometer, this study highlights the importance of combining techniques deriving forest structure and composition at different scales, binding regional to potentially global carbon-fluxes with remote sensing, reducing this uncertainty source in global climate models.     

Coupling SAR data with forest growth models

Recent studies have shown the potential of remote sensing data at optical wavelengths to provide spatially referenced input data for process-based forest growth models. In comparison, radar remote sensing remains underexploited, despite having numerous advantages. This letter assesses the potential of radar remote sensing data to estimate tree biomass and various structural features. It is concluded that there exists a wide range of strategies for coupling radar remote sensing with forest growth models.     

Research Progress of Remote Sensing Classification and Change Monitoring on Forest Types

Forest is one of the main vegetation type in the terrestrial ecosystem,and using remote sensing technology on discriminating and change monitoring forest types are of great significance importance for the global carbon cycle study and sustainable development of forest resources.This article reviewed the classical remotely sensed classification methods forest remote sensing classification methods,including pixel-based,object-oriented,red-edge spectral information based and deep learning methods,separately.We also introduced the details and individual advantages of these methods in the some specific applications.Finally,the limitations of the current study on forest remote sensing classification and change monitoring on forest types were indicated in order to provide reference for the dynamic supervision of forest resources under the new situation.     

Sparse microwave imaging:Principles and applications

This paper provides principles and applications of the sparse microwave imaging theory and technology.Synthetic aperture radar(SAR) is an important method of modern remote sensing.During decades microwave imaging technology has achieved remarkable progress in the system performance of microwave imaging technology,and at the same time encountered increasing complexity in system implementation.The sparse microwave imaging introduces the sparse signal processing theory to radar imaging to obtain new theory,new system and new methodology of microwave imaging.Based on classical SAR imaging model and fundamental theories of sparse signal processing,we can derive the model of sparse microwave imaging,which is a sparse measurement and recovery problem and can be solved with various algorithms.There exist several fundamental points that must be considered in the efforts of applying sparse signal processing to radar imaging,including sparse representation,measurement matrix construction,unambiguity reconstruction and performance evaluation.Based on these considerations,the sparse signal processing could be successfully applied to radar imaging,and achieve benefits in several aspects,including improvement of image quality,reduction of data amount for sparse scene and enhancement of system performance.The sparse signal processing has also been applied in several specific radar imaging applications.     

The role of remote sensing on studying mangrove forest extent change

ABSTRACT

Given the ability of effectively observing vegetation at a variety of spatial and temporal scales, remote sensing has been widely used to monitor and understand the change of mangrove forest extent. Yet, a systematic cognition of the unique contribution of remote sensing on studying mangrove forest extent dynamic is lacking, which prevents us from clearly identifying and further overcoming current deficiencies of studying the change of mangrove forest extent with remote sensing. Therefore, in this review, we summarized remotely sensed extraction methods and data related to mangrove forest extent change monitoring and all discoveries from mangrove forest extent change observation and driver analysis. We found that mangrove forest in most of the study areas has declined over the past decades, and the loss rate is accelerating. We also found that remote sensing plays an important role in revealing how the extent of mangrove forest responds to climate change and human impacts. In addition, we identified several recurring limitations from previous studies. This review highlights the role of remote sensing on studying mangrove forest extent change, and is helpful to make better use of remotely sensed data in this field.     

干涉雷达在地学研究中的应用

近年来干涉雷达(Interferometric SAR,INSAR)已经成为合成孔径雷达(SAR)对地观测技术研究的前沿热点之一,其应用领域正在迅速扩大。首先介绍了INSAR的基本原理,接着通过许多实例总结了INSAR在地学研究中的应用,最后,对未来星载INSAR系统作了介绍,并对我国INSAR的发展提出了建议。     

Radar backscattering of forest stands†

Research on the potential applications of microwave remote sensing in agriculture is conducted in the Netherlands by the ROVE team. Since active microwave remote sensing, featuring its all-weather capability, also seems to be a promising tool for forest classification, especially on a global scale, the Wageningen Agricultural University started a new working group in co-operation with the ROVE team in order to explore this field of application.

Results of four X-band SLAR flights have been analysed. The digital radar images obtained are accurately corrected both geometrically and radiometrically and indicate gamma values instead of arbitrary grey tones.

Radar signatures, showing seasonal and angular effects, of 16 classes of forest stands have been derived from the images. Special attention has been paid to the statistical properties of the radar signatures and their impact on classification accuracy. Several interesting phenomena have been observed indicating effects of vegetation structure on radar backscattering.

One of the test areas is a young forest in the Oost-Flevoland polder featuring a substantial variety of species; parcels are relatively large, rectangular in shape, homogeneous in structure and age and with pure species stands making this an ideal test site. Another test area is an old forest located at the Veluwe. Much variation in age is present here, which made it possible to determine relationships between tree age and radar backscatter for several coniferous tree species.

The initial work as presented here clearly demonstrates the appropriateness of X-band ability in the classification of (Dutch) forests. Theoretical considerations suggest that a multitemporal approach is likely to give the most accurate results of tree-type classification. A classification simulation yielded overall error fractions ranging from 10 to 16 per cent at the Oost-Flevoland polder test area and 14 to 28 per cent at the Veluwe test area. This can be demonstrated in multitemporal radar images as well as in actual classified images.     

红树林雷达遥感研究进展

红树林是海岸带生态系统中重要的植物群落,具有较高的社会、生态和经济价值。遥感技术的发展为红树林监测提供了一种高效便利的手段。雷达遥感由于具有穿透性好、不受云雨影响的特点,在红树林分布地区具有得天独厚的优势。对近几十年来雷达遥感在红树林监测方面的研究进行了回顾,着重分析了红树林散射机制、红树林分类与识别和红树林生物物理参数反演这3个方面的研究进展,对各类方法进行了总结和对比,最后针对存在的问题提出了未来可以改进的方向。