极化干涉SAR相干最优理论及其验证分析

极化干涉SAR树高反演是当前SAR研究领域的一个重要方向。相干最优化是在各种散射机制中寻求最优的散射机制,对于极化干涉SAR,它不仅可以改善不同极化通道之间的相干系数,还能改善地物分类和垂直结构参数估计。首先详细分析极化干涉SAR反演树高的相干最优化理论基础,然后利用仿真数据从不同极化通道对线极化、Pauli基极化和最优极化进行了试验,从定性和定量进行对比分析,研究结果进一步验证相干最优分解方法可以提高干涉相干系数,并获得更好的干涉图,从而有利于提高树高反演的精度。     

水田SAR后向散射强度及干涉相干特性研究

利用2007-2011年获取的ALOS PALSAR HH极化方式的FBS及FBD共21景数据, 结合实地1:500土地利用分类图, 对苏南无锡惠山试验区进行了水稻田后向散射强度的时间特性试验及干涉相干特性分析。试验结果表明:该区水稻田后向散射强度随生长周期的变化而变化, 且与各生长阶段的灌溉需水规律的变化一致, 需水量越大, 后向散射强度值越小。干涉相干特性试验表明:同期干涉相干系数略高于非同期干涉相干系数, 且在同期干涉试验中, 时间基线相同的情况下, 非水稻种植期的干涉相干系数达到最高, 其值明显高于水稻种植期的相干系数, 该试验结果对苏南地表沉降监测SAR影像干涉对的选取及地物变化检测具有重要的参考价值。     

多时相双极化合成孔径雷达干涉测量土地覆盖分类方法

综合采用时相、极化和干涉3种维度的SAR数据进行土地覆盖分类。以黑龙江省逊克县多时相ALOS PALSAR数据覆盖区为研究区,利用不同时相极化SAR、干涉SAR信号对地物特征的敏感性,结合后向散射强度和干涉相干的时变特征进行地物解译,发展了基于多时相、多极化、干涉SAR数据的SVM土地覆盖分类方法。研究结果表明,引入双极化SAR中不同极化(HH-HV)间的相干系数,并结合所选择的时相特征、极化特征以及干涉相干特征进行分类,可解决双极化SAR影像中林地与城市及建设用地的混分问题,得到更高精度的土地覆盖分类结果。     

基于Freeman散射熵和各向异性度的极化SAR影像分类算法研究

极化SAR影像中阴影、水体和裸露的耕地3种地物类型有非常相似的极化散射特性,常规基于非相干分解的分类方法难以将其有效地区分。对此,本文引入基于Freeman分解的散射熵Hf和各向异性度Af两个特征参数,并将其用于极化SAR影像分类。首先利用Hf和Af参数将阴影和水体提取出来,然后将其他地物按散射机制分为3大类,并对每一类再次利用Hf和Af参数进行细分,最后通过基于Wishart分布的聚类和迭代分类,得到最终的分类结果。通过利用Radarsat-2在河南登封获取的全极化SAR数据进行试验,表明该算法执行效率高,能够有效地区分阴影、水体和裸露的耕地,并且对其他地物类型也有很好的分类效果。     

自交叉双边滤波的极化SAR数据相干斑抑制

相干斑抑制是极化SAR数据预处理的关键步骤。双边滤波是一种空域和值域滤波相结合的优秀边缘保持滤波算法。针对双边滤波在抑制极化SAR数据相干斑的不足,该文将改进的交叉双边滤波引入到极化SAR数据降噪领域,加入散射机制测度来扩展原权重核,根据SPAN图像的局域变差系数自动调整空间方差系数,利用参考图像来度量灰度值和散射机制相似性。实验结果表明:本文方法较经典滤波算法有更强的噪声平滑能力和更好的细节信息保持能力,在保持原数据极化散射信息方面也表现出良好的性能,这为基于极化SAR数据的后续应用提供了支持。     

联合时间序列相干性和后向散射系数的黄河三角洲湿地分类

针对后向散射系数难以完成高精度湿地分类问题,本文以16景VH极化的Sentinel-1A影像为数据源,构建了一种联合时间序列相干性和后向散射系数的分类方法。通过对长时间序列的后向散射系数和相干性分析,选择互花米草易与其他地物混淆的3个时相(6月27日(R)、11月18日(G)、11月30日(B))的后向散射系数图为合成数据源,引入11月18—30日相干图代替11月30日后向散射系数图。采用SVM和随机森林分类器,探究相干性引入前后黄河三角洲湿地分类精度变化。研究表明,相干性引入后,SVM和随机森林分类结果的总体精度分别提升了3.07%和3.85%,互花米草的分类精度分别提升了9.39%和11.42%。     

多时相全极化SAR数据融合方法与土地覆盖分类研究

结合多时相和全极化SAR数据在地表覆盖分类中的优势,通过融合多时相全极化SAR数据,降低雷达图像上斑点噪声的影响,开展土地覆盖分类研究。以贵州扎佐林场大约12 km×17 km的区域为研究区,使用6个不同时期的RADARSAT-2全极化数据进行地表覆盖分类研究。研究结果表明:不同地物的后向散射机制有很大区别,且对应的后向散射系数随时间的变化规律也各不相同;该方法能有效区分人工建筑、森林、农田和水体等地物,斑点噪声得到有效抑制,图像质量,特别是视觉效果大为改善。     

PolSAR数据在矿山遥感监测中的可行性研究

随着极化合成孔径雷达系统的发展,Pol SAR数据在各个领域得到了广泛的应用。本文研究了Pol SAR数据在矿山监测领域的可行性。首先对Pol SAR数据进行滤波去噪等预处理;然后介绍了适合矿山地物分类的Cloude特征向量分解和Freeman分解方法,在极化分解的基础上采用了一种结合散射熵和Freeman分解的Wishart分类方法进行分类,最终得到矿山监测地物的分类图,并通过人工解译的方式对分类后的图像信息进行归类并建立数据库,得到矿山地区的地物分类图。以机载Pol SAR数据为例,得到了较好的实验结果。     

ERS-1散射计数据用于全球陆地监测

该文介绍了ＥＲＳ－１（欧洲资源卫星１号）ＷＳＣ（风散射计）数据结构。描述了全球雷达后向散射系数（σ°）图的成图方法。展示池中国第一幅全球雷达后向散射系数分布图。重点分析了ＷＳＣ数据用于全球陆地监测的能力，并对全球典型地物地雷达后向散射系数进行了统计。研究结果表明：１．全球雷达后向散射系数侧重反映了全球值被图和地形图的叠合信息；２．ＷＳＣ数据能够以区域和全球尺度分区分６类主要的地表覆盖类型。它们是：     

Yamaguchi分解Notch滤波的全极化SAR图像舰船目标检测

极化合成孔径雷达数据蕴含了丰富的地物极化散射信息,已广泛应用于海上舰船目标检测研究。针对极化相干矩阵无法直接用于分析特定散射体物理特性的缺陷,利用Yamaguchi极化分解改进了极化Notch滤波器。将基于模型的极化分解方法引入Notch滤波器,利用表面散射、二次散射、体散射和螺旋体散射等散射机制的能量构造散射矢量代替极化相干散射矢量,并加入功率能量因子,构造新的极化SAR图像Notch滤波器。Radarsat-2全极化SAR图像实验结果表明,改进算法有效增强了舰船目标与海杂波背景间的对比度,检测性能优越。     

极化雷达目标分解方法用于岩性分类

雷达遥感中地表不同岩石类别的后向散射一般判别不大,因此以散射幅度为主要探测因子常规雷达遥感数据不利于岩性分类。极化雷达以散射矩阵或Ｓｔｏｋｅｓ短阵的形式,记录了更多的地物回波信息。信息源的增多,有利于提高岩性分类的精度。但是,由于不同极化状态回波信号之间的关性,极化数据不可避免地产生数据冗余,反而增大了岩性分类的误差。     

Evaluating SAR polarization modes at L-band for forest classification purposes in Eastern Amazon,Brazil

Single, interferometric dual, and quad-polarization mode data were evaluated for the characterization and classification of seven land use classes in an area with shifting cultivation practices located in the Eastern Amazon (Brazil). The Advanced Land-Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data were acquired during a six month interval. A clear-sky Landsat-5/TM image acquired at the same period was used as additional ground reference and as ancillary input data in the classification scheme. We evaluated backscattering intensity, polarimetric features, interferometric coherence and texture parameters for classification purposes using support vector machines (SVM) and feature selection. Results showed that the forest classes were characterized by low temporal backscattering intensity variability, low coherence and high entropy. Quad polarization mode performed better than dual and single polarizations but overall accuracies remain low and were affected by precipitation events on the date and prior SAR date acquisition. Misclassifications were reduced by integrating Landsat data and an overall accuracy of 85% was attained. The integration of Landsat to both quad and dual polarization modes showed similarity at the 5% significance level. SVM was not affected by SAR dimensionality and feature selection technique reveals that co-polarized channels as well as SAR derived parameters such as Alpha-Entropy decomposition were important ranked features after Landsat’ near-infrared and green bands. We show that in absence of Landsat data, polarimetric features extracted from quad-polarization L-band increase classification accuracies when compared to single and dual polarization alone. We argue that the joint analysis of SAR and their derived parameters with optical data performs even better and thus encourage the further development of joint techniques under the Reducing Emissions from Deforestation and Degradation (REDD) mechanism.     

Coherence optimization using the polarization state conformation in PolInSAR

A new coherence optimization algorithm is proposed for polarimetric synthetic aperture radar (SAR) interferometry applications by using the polarization state conformation algorithm based on the polarimetric basis transformation along with the polarization signatures. Through application of this algorithm, the resemblance between the scattering mechanisms of the same target in the repeat-pass polarimetric SAR (POLSAR) images is maximized. Then, coherence maps between the repeat-pass POLSAR images, before and after application of the algorithm, are generated. The coherences obtained by this method represent the best coherences or optimized coherences between the POLSAR images. The effects predicted by the theory are confirmed by the POLSAR data acquired by the Jet Propulsion Laboratory Spaceborne Imaging Radar mission.     

利用矩阵值域的极化干涉SAR相干性的研究

极化SAR干涉测量 (PolInSAR)的相干性是反演植被参数的重要信息来源,极化空间中不同极化状态对应的相干性分布与复极化相干矩阵的值域相关。本文讨论了不同结构的复极化相干矩阵值域的特点,分析了不同极化状态下干涉相干性与复极化相干矩阵的值域的关系。利用模拟数据和真实全极化SAR数据分析了滑动窗口大小和不同散射体对复极化干涉矩阵值域的影响,以及复极化干涉矩阵的结构对极化干涉SAR相干性分布空间的影响,有助于更加准确地获取极化干涉SAR最优极化基和估计最优相干性。     

基于ESPRIT算法的极化干涉SAR植被高度反演研究

由于存在着去相干分量,利用ESPRIT(旋转不变技术)算法对植被区域的极化干涉SAR(PollnSAR)数据进行反演的结果有较严重偏差.针对这一问题,结合极化干涉相干最优理论及其物理散射机制,引入新的散射矢量——相干最优化散射矢量,提出一种改进的基于ESPRIT的植被高度反算法.最后,利用欧空局(ESA)提供的模拟L波...     

Assessing integration of intensity,polarimetric scattering,interferometric coherence and spatial texture metrics in PALSAR-derived land cover classification

Synthetic aperture radar (SAR) is an important alternative to optical remote sensing due to its ability to acquire data regardless of weather conditions and day/night cycle. The Phased Array type L-band SAR (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) provided new opportunities for vegetation and land cover mapping. Most previous studies employing PALSAR investigated the use of one or two feature types (e.g. intensity, coherence); however, little effort has been devoted to assessing the simultaneous integration of multiple types of features. In this study, we bridged this gap by evaluating the potential of using numerous metrics expressing four feature types: intensity, polarimetric scattering, interferometric coherence and spatial texture. Our case study was conducted in Central New York State, USA using multitemporal PALSAR imagery from 2010. The land cover classification implemented an ensemble learning algorithm, namely random forest. Accuracies of each classified map produced from different combinations of features were assessed on a pixel-by-pixel basis using validation data obtained from a stratified random sample. Among the different combinations of feature types evaluated, intensity was the most indispensable because intensity was included in all of the highest accuracy scenarios. However, relative to using only intensity metrics, combining all four feature types increased overall accuracy by 7%. Producer’s and user’s accuracies of the four vegetation classes improved considerably for the best performing combination of features when compared to classifications using only a single feature type.     

Classification of dual- and single polarized SAR images by incorporating visual features

Fully and partially polarimetric SAR data in combination with textural features have been used extensively for terrain classification. However, there is another type of visual feature that has so far been neglected from polarimetric SAR classification: Color. It is a common practice to visualize polarimetric SAR data by color coding methods and thus it is possible to extract powerful color features from such pseudo color images so as to gather additional crucial information for an improved terrain classification. In this paper, we investigate the application of several individual visual features over different pseudo color generated images along with the traditional SAR and texture features for a novel supervised classification application of dual- and single-polarized SAR data. We then draw the focus on evaluating the effects of the applied pseudo coloring methods on the classification performance. An extensive set of experiments show that individual visual features or their combination with traditional SAR features introduce a new level of discrimination and provide noteworthy improvement of classification accuracies within the application of land use and land cover classification for dual- and single-pol image data.     

The inversion method study of sub-canopy soil moisture estimation using repeat pass SIR-C PolInSAR data

From repeat pass SIR-C L band polarimetric SAR interferometric data and fully maximum likelihood inversion decomposition model of PolInSAR, a method for sub-canopy soil moisture estimation using repeat pass SIR-C PolInSAR data is proposed. At the same time, the potential and validity of fully maximum likelihood inversion decomposition model of PolInSAR for sub-canopy soil moisture inversion is investigated. Firstly, from the random oriented volume over ground two layer coherent scattering model and the statistical characteristics of Pol-InSAR coherency matrix, the fully maximum likelihood inversion decomposition model is used to reconstruct or recover the surface polarimetric coherency matrix with volume scattering components significantly removed; then, from recovered surface polarimetric coherency matrix, co-HH, VV and cross-HV polarization backscattering coefficient are obtained, and the sub-canopy soil moisture are inverted from Oh and Dihedral scattering model. At last, Compared the inversion result with the field measurement and the climate data of hetan region from 1951 to 2006, the preliminary result indicates that the proposed method based on fully maximum likelihood inversion decomposition model has enough high inversion accuracy, if the new spaceborne or airborne polarimetric SAR interferometric data with synchronously spaceborne or airborne-ground measurement will be acquired, the validity and accuracy of proposed inversion method will be further investigated and validated.     

基于MODIS影像的森林火灾火线检测方法

结合归一化火灾差异指数NDBR(normalized difference burn ratio)和MODIS多波段影像梯度边缘分析手段检测火线, 应用B样条函数拟合火线并确定火势蔓延方向。为对比验证, 基于火线的Kriging插值实现火灾外推预测, 与30min后的火灾参考数据目视对比与统计：火线的预测变化与参考影像基本保持一致, 火灾外推影像的均值和熵约为参考影像的86%和81%, 火迹地检测的Kappa系数达80.2%。试验表明, 提出的森林火线特征自动检测方法在动态火灾监测中是可行、有效的。