Agricultural crop condition monitoring using airborne C-band synthetic aperture radar in southern Alberta

Applications of airborne C-band synthetic aperture radar imagery for determining variations in agricultural crop characteristics were investigated at a test site in southern Alberta, Canada. Synthetic aperture radar (SAR) imagery and ground-based crop characteristics data were acquired on 19–20 July 1994 for wheat, canola, beans, peas, and wheat + alfalfa cultivated under a variety of irrigation conditions. The results indicate that the statistically significant relationships that were derived between the ground-based data and SAR imagery are a function of crop type, crop condition parameter, and image processing procedures, and that crop characteristics such as leaf area index and plant height are negatively correlated with radar backscatter.     

Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data

During recent years, synthetic aperture radar (SAR) data have been increasingly used for flood mapping. New radar satellites especially, such as TerraSAR-X, Radarsat-2 and COSMO-SkyMed, provide high-resolution data with high potential for fast and reliable detection of inundated areas. This article compares three simple approaches to derive water areas from SAR data in relation to the German–Vietnamese project, Water-related Information System for the Sustainable Development of the Mekong Delta (WISDOM). Two methods are pixel based and use histogram-based grey-level thresholds, as well as a homogeneity criterion for classification. The third approach is object based and applies characteristic attributes of water objects such as grey value, texture and relations to neighbouring objects. Further discussed are the influence of a variation of the thresholds and the challenges to validate water masks derived from active remote-sensing data. We implemented one of the introduced approaches for surface water derivation in a water mask processor for automatic water mask calculation from radar satellite imagery (WaMaPro). This fully automatic processing chain was developed to process TerraSAR-X and Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) imagery in order to meet the demands for automatic flood monitoring.     

Processing synthetic aperture radar data†

Abstract

Basic operational principles of synthetic aperture radar systems are reviewed, with an emphasis on the data processing requirements. The techniques of image formation for these systems are covered, using either optical or digital techniques. Examples of imagery are shown to illustrate the data processing results. SAR processing capabilities as they exist worldwide are summarized.     

雷达图像特征信息提取及多源数据复合处理的地质应用研究

以湖南香花岭地区机载侧视雷达图像资料为基础，针对雷达图像特征，综合地球物探、地球化学异常数据，对工作区的成矿地质条件进行综合分析，形成了工作区多源地学数据集。在对该数据集进行多元统计的判别分析以后，建立了湖南香花岭地区锡、钨矿的找矿预测模型，得到综合成矿预测得分图，提出了预测远景区并进行了综合评价。总结出的一整套实用方法和技术将对雷达图像的地质应用研究及雷达地质的发展起着重要作用     

Detecting and quantifying mountain permafrost creep from in situ inventory,space-borne radar interferometry and airborne digital photogrammetry

In this paper three different techniques for detecting and quantifying mountain permafrost creep are compared: (1) rock glacier inventory and characterization from in situ indicators, (2) space-borne radar interferometry, and (3) digital processing of repeated airborne imagery. The specific characteristics of the three methods and their complementarity are investigated for the Fletschhorn mountain range in the Simplon/Saas valley region, Swiss Alps. We found that radar interferometry is suitable to quantify the degree of activity and the order of surface velocity of rock glaciers over large areas in one process, with the possibility to also detect very small movements of inactive and relict rock glaciers. On the other hand, aero-photogrammetry represents a valuable base for additional interpretation of the three-dimensional surface flow field (including speed, direction and change in thickness) of the most active rock glaciers. Results from radar interferometry can also form the basis for further detailed in situ investigations.     

Changing landscapes: Monitoring Environmentally Sensitive Areas using satellite imagery

A Map Updating System, which uses visual interpretation of satellite imagery to update vectorized maps of land cover, has been developed to complement existing methods using aerial photography. The use of satellite imagery in a Geographic Information System allowed information from a digitized 1987 map of land cover to be used to speed up the identification of changes in land cover between arable and grassland in the Broads Environmentally Sensitive Area. Both optical and radar imagery have been successfully used to discriminate arable land from grassland. ERS radar imagery proved valuable in constructing a time series of images to distinguish between permanent and temporary grassland.     

Comparison and integration of spaceborne optical and radar data for mapping in Sudan

The purpose of this study was to determine how different procedures and data, such as multiple wavelengths of radar imagery and radar texture measures, independently and in combination with optical imagery influence land-cover/use classification accuracies for a study site in Sudan. Radarsat-2 C-band and phased array L-band synthetic aperture radar (PALSAR) L-band quad-polarized radar were registered with ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) optical data. Spectral signatures were obtained for multiple landscape features, classified using a maximum-likelihood decision rule, and thematic accuracies were obtained using separate validation data. There were surprising differences between the thematic accuracies of the two radar data sets, with Radarsat-2 only having a 51% accuracy and PALSAR 73%. In contrast, the optical ASTER overall accuracy was 81%. Combining the original radar and a variance texture measure increased the Radarsat-2 to 78% and PALSAR to 80%, whereas the two original radar bands together had an accuracy of 87%. Sensor fusion of optical and radar obtained an accuracy of 93%. Based on these results, the use of multiwavelength quad-polarized radar imagery combined or integrated with optical imagery has great potential in improving the accuracy of land-cover/use classifications. In tropical and high-latitude regions of the world, where persistent cloud cover hinders the use of optical satellite systems, land management programmes may find this research promising.     

低空导航吊舱信号处理系统设计

信号处理系统是低空导舱的核心系统,该系统是一个由ＴＩ公司ＴＭＳ３２０Ｃ３０ＤＳＰ构成的紧耦合系统,完成地面跟随雷达的测距、测角、回收重点２,红外图象处理,伺县控制和与飞机航空电子 线系统通信等任务,实现了飞机超低空飞行中地形跟随功能和红外夜视能力。详细介绍导航吊舱信号处理系统的系统结构和有关信号处理模块的设计。     

Multi-look processing of synthetic aperture radar data from dynamic ocean surfaces

The basic theory is described of multi-look processing of synthetic aperture radar (SAR) data from dynamic ocean surfaces. The principal effects inherent to multi-look SAR on the impulse response function are first illustrated using a moving point target model. The theory is then extended to the multi-look imagery of diffusely scattering ocean surface waves. Finally, the use of a sliding synthetic aperture is suggested to investigate the short life-times of small scale surface waves that are considered to be the predominant scatterers on the ocean surface.     

Crop type classification using a combination of optical and radar remote sensing data: a review

Reliable and accurate crop classification maps are an important data source for agricultural monitoring and food security assessment studies. For many years, crop type classification and monitoring were focused on single-source optical satellite data classification. With advancements in sensor technologies and processing capabilities, the potential of multi-source satellite imagery has gained increasing attention. The combination of optical and radar data is particularly promising in the context of crop type classification as it allows explaining the advantages of both sensor types with respect to e.g. vegetation structure and biochemical properties. This review article gives a comprehensive overview of studies on crop type classification using optical and radar data fusion approaches. A structured review of fusion approaches, classification strategies and potential for mapping specific crop types is provided. Finally, the partially untapped potential of radar-optical fusion approaches, research gaps and challenges for upcoming future studies are highlighted and discussed.     

Mapping arctic landfast ice extent using L-band synthetic aperture radar interferometry

In recent years methods have been developed to extract the seaward landfast ice edge from series of remote sensing images, with most of them relying on incoherent change detection in optical, infrared, or radar amplitude imagery. While such approaches provide valuable results, some still lack the required level of robustness and all lack the ability to fully automate the detection and mapping of landfast ice over large areas and long time spans. This paper introduces an alternative approach to mapping landfast ice extent that is based on coherent processing of interferometric L-band Synthetic Aperture Radar (SAR) data. The approach is based on a combined interpretation of interferometric phase pattern and interferometric coherence images to extract the extent and stability of landfast ice. Due to the low complexity of the base imagery used for landfast ice extraction, significant improvements in automation and reduction of required manual interactions by operators can be achieved. A performance analysis shows that L-band interferometric SAR (InSAR) data enable the mapping of landfast ice with high robustness and accuracy for a wide range of environmental conditions.     

Comparison of Selected Features for Target Detection in Synthetic Aperture Radar Imagery

Cooke, Tristrom, Redding, Nicholas J., Schroeder, Jim, and Zhang, Jingxin, Comparison of Selected Features for Target Detection in Synthetic Aperture Radar Imagery, Digital Signal Processing10 (2000), 286–296.Several methods are available that capture the statistics of radar imagery. The best features, in the sense of man-made target discrimination, are expected to be different for different types of natural background and for different objects of interest such as vehicles. We demonstrate that discrimination of natural background and man-made objects using low resolution synthetic aperture radar imagery is possible using singular value decomposition; several other simple features are also used to augment the feature vector. We use a subset of eigenvectors as features for target discrimination. The optimal set of features used to classify a region as “background clutter only” or “target region” is automatically chosen by a standard suboptimal feature selection algorithm.     

Urban land use classification using synthetic aperture radar

Abstract

This paper presents several approaches to the use of radar imagery for land use classification of urban and near-urban areas. The use of L(HH) (L band, horizontal transmit and horizontal receive) data is emphasized because it is these types of data obtained by Seasat-A (and in November 1981 by Shuttle radar) which are most generally available. For urban area studies using imaging radar the effect of processing in an off-zero doppler (‘squint’) mode, the presence of large diffuse scatters and the possibility of height measurements are discussed. Each approach provides information and also requires supporting ground truth which are unique to radar remote sensing. For some areas the coupling of data from the microwave portion of the spectrum to the data available in the visible and near visible realms may improve the classification of urban and near-urban land use. However, the radar data are not without their own limitations which may be imposed by either the system or the nature of the imaged scene. A proper knowledge of these limitations can permit us to turn a perceived defect into a decided advantage. The metropolitan area of Los Angeles provides the geographic background for this study.     

Multiscale Models for Target Detection and Background Discrimination in Synthetic Aperture Radar Imagery

Multiscale models can be used to capture the scale-dependent behavior of the statistics in radar imagery. This behavior is expected to be different for natural background compared to objects of interest such as vehicles. We demonstrate that multiscale autoregressive models can discriminate between samples of these two major classes extracted from 1.5-m-resolution radar imagery. We also show that it is possible to discriminate between two types of natural background in SAR imagery, “grassland” and “woodland,” using multiscale models. This latter result could be exploited in adaptive algorithms for automated target detection.     

Comparative analysis of backpropagation and the extended Kalmanfilter for training multilayer perceptrons

The relationship between backpropagation and extended Kalman filtering for training multilayer perceptrons is examined. These two techniques are compared theoretically and empirically using sensor imagery. Backpropagation is a technique from neural networks for assigning weights in a multilayer perceptron. An extended Kalman filter can also be used for this purpose. A brief review of the multilayer perceptron and these two training methods is provided. Then, it is shown that backpropagation is a degenerate form of the extended Kalman filter. The training rules are compared in two examples: an image classification problem using laser radar Doppler imagery and a target detection problem using absolute range images. In both examples, the backpropagation training algorithm is shown to be three orders of magnitude less costly than the extended Kalman filter algorithm in terms of a number of floating-point operations     

Object-based image analysis of optical and radar variables for wetland evaluation

Optical and radar imagery has been shown to be useful for classifying wetland types and surrounding non-wetland classes such as forest and agriculture. Throughout the literature, recommendations have been made that optical and radar image variables together should improve overall and individual class accuracies. object-based image analysis (OBIA) uses multiple data types to segment objects representing land cover entities that are subsequently classified. There are few studies that have utilized optical and polarimetric radar variables together in OBIA to map wetland classes. This research investigated the potential to combine WorldView-2 optical image variables with fully polarimetric Radarsat-2 image variables in OBIA classification of wetland type. With the addition of radar polarimetric variables, classification accuracy improved for the wetland classes of fen, bog, and swamp over the use of optical imagery alone; specifically the addition of Cloude–Pottier (CP) variables of entropy, anisotropy, and alpha angle improved the classification of fen, and the addition of horizontal transmit and horizontal receive (HH) and horizontal transmit and vertical receive (HV) backscatter intensity improved the classification of swamp.     

基于ActiveX控件的雷达动态信息系统的实现

简述了机载雷达记录信息的特点,提出了信息搜索及目标再现的优化算法,给出了对于多目标动态雷达信息的计算机分析与再现方法.利用ActiveX控件技术,实现了软件系统的可移植性,有效地解决了机载雷达告警记录信息处理的通用性问题,满足了不同层面应用的需要,提高了部队的战斗力.     

Multi-mode PolInSAR experiments using an airborne X-band SAR system in China

In this paper, we describe our polarimetric interferometric synthetic aperture radar (PolInSAR) experiments with high-resolution X-band data acquired by a multi-mode airborne SAR system over an area of Linshui in southern China. First, we introduce our latest multi-mode X-band airborne imaging radar system (Multi-Mode-XSAR), which integrates three operation modes of bistatic, ping-pong, and mixed. Then, the Multi-Mode-XSAR data set and the corresponding ground measurements in test areas are briefly described. Considering the characteristics of the Multi-Mode-XSAR imagery, a dual-baseline polarimetric interferometry (DPI) method is proposed in this article. The proposed method guarantees a high coherence on the full polarimetric data and combines the benefits of short and long baselines to facilitate the phase unwrapping and promote height sensitivity. Our PolInSAR experiment results demonstrate that the DPI method is capable of generating DSM with higher accuracy than other multi-baseline (MB) methods and the Multi-Mode-XSAR imagery has great potential in PolInSAR applications.     

雷达抗有源干扰技术现状与展望

雷达有源干扰为雷达目标检测、跟踪与识别带来了极大挑战,使得复杂电磁环境下雷达抗有源干扰技术研究迫在眉睫。雷达抗有源干扰技术囊括雷达信号与信息处理各个环节,是一项系统综合技术。本文从系统与体制层面、波形设计与接收机层面以及信号与数据处理层面层次化地综述了雷达抗有源干扰理论及其关键技术,着重介绍了代表性成果及其最新进展,评述了其中的公开问题,对研究中现存的难点进行了探讨。随后简述了现有雷达抗有源干扰效能评估方法及其不足。以此为基础,对现有部分方法存在的限制给出了可行的解决思路。最后本文展望了未来抗有源干扰的发展方向。     

高空间分辨率遥感影像分割方法研究综述

遥感影像分割是指把一幅影像划分为互不重叠的一组区域的过程,它要求得到的每个区域的内部具有某种一致性或相似性,而任意两个相邻的区域则不具有此种相似性。遥感影像分割是面向对象的遥感影像数据挖掘与应用中的一项关键技术,对于影像目标信息自动化提取与智能识别尤为重要,在面向对象的遥感影像处理工程中具有重要意义。本文对常见的高空间分辨率遥感影像分割方法与应用策略进行了分析,比较了各种分割方法的应用范围、优缺点及目前存在的改进措施。建立了面向对象的遥感影像分割方法的分类体系,最后指出了面向对象的遥感影像分割方法目前所存在的问题及应用前景。