基于SAR数据的干旱区冲积扇地表粗糙度反演

干旱区冲积扇的表面特征是研究古气候、古环境变化的重要途径,冲积扇扇面粗糙度是反映冲积扇演化的重要指示。合成孔径雷达(SAR)已成为冲积扇扇面特征参数提取的有力工具。然而,现有的地表粗糙度反演模型在干旱区难以得到较好的反演结果。利用Radarsat-2极化数据,开展了疏勒河冲积扇地表粗糙度反演研究,在OH模型的基础上,提出了更加适合干旱区地表粗糙度反演的新方法。经过实测数据的验证,新方法能够解决原有模型在干旱区粗糙度反演结果偏大的问题,并取得了较好的反演结果。该模型的提出为干旱区冲积扇表面参数提取提供了可靠的工具。     

Sensitivity of SAR data to post-fire forest regrowth in Mediterranean and boreal forests

Disturbed forests may need decades to reach a mature stage and optically-based vegetation indices are usually poorly suited for monitoring purposes due to the rapid saturation of the signal with increasing canopy cover. Spaceborne synthetic aperture radar (SAR) data provide an alternate monitoring approach since the backscattered microwave energy is sensitive to the vegetation structure. Images from two regions in Spain and Alaska were used to analyze SAR metrics (cross-polarized backscatter and co-polarized interferometric coherence) from regrowing forests previously affected by fire. TerraSAR-X X-band backscatter showed the lowest sensitivity to forest regrowth, with the average backscatter increasing by 1-2 dB between the most recent fire scar and the unburned forest. Increased sensitivity (around 3-4 dB) was observed for C-band Envisat Advanced Synthetic Aperture (ASAR) backscatter. The Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) L-band backscatter presented the highest dynamic range from unburned to recently burned forests (approximately 8 dB). The interferometric coherence showed low sensitivity to forest regrowth at all SAR frequencies. For Mediterranean forests, five phases of forest regrowth were discerned whereas for boreal forest, up to four different regrowth phases could be discerned with L-band SAR data. In comparison, the Normalized Difference Vegetation Index (NDVI) provided reliable differentiation only for the most recent development stages. The results obtained were consistent in both environments.     

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.     

高性能SAR成像程序的并行粒度选择技术

高性能并行仿真程序必须采用针对性的并行优化技术以获得高效的性能加速。而根据程序特性,确定适合的并行粒度是设计并行优化技术的基础。因此,以典型合成孔径雷达(SAR)成像程序--距离-多普勒(RD)程序为代表,研究了高性能SAR成像程序的并行粒度选择技术,重点针对并行粒度的基本准则和RD算法的并行粒度选择方法进行了研究。实验结果证明,经过并行粒度优化选择的SAR成像程序能够获得显著的性能提升,并且具有良好的可扩展性。     

SAR与可见光图象匹配方法的研究

由于SAR和可见光成象传感器图象的物理特征不同,因此进行匹配难度很大。本文在深入分析了这两种传感器图象物理特征的基础上,提出了一种图象匹配的新方法。该方法运用形态学滤波方法去除SAR图象斑点噪声,利用图象的边缘特征进行匹配,并采用多分辨率分级搜索技术减少计算量,在对实验结果进行分析的基础上提出了搜索真实匹配位置的原则,大大提高了匹配的准确率。通过实验对SAR图象斑点噪声滤波和SAR与可见光图象匹配算法进行了验证,实验证明本算法的SAR图象去噪效果明显优于传统的Frost方法,图象匹配精度高,稳定性好。     

基于模糊理论的SAR图像分割算法研究与实现

SAR图像存在强烈的相干斑噪声,传统方法不能很好对其分割。文章基于模糊理论,通过选择图像特征,构造模糊集,借助最大隶属度原则进行了SAR图像分割算法的设计,并借助SAR图像分别进行了参数调节和窗口选择的实验,获得了满意的分割结果。实验结果表明,该算法对于SAR图像分割可行有效。     

SAR图像自动目标识别系统研究与设计*

研究了SAR图像自动目标识别系统。通过分析国际上先进的SAR自动目标识别系统的组成、算法实现、流程设计和所得到的结果,设计出SAR自动目标识别系统结构和识别流程图。系统结构主要由SAR图像特性库、特征库和模型库组成。识别流程在系统结构的基础上,分为检测、辨识和分类三步。最后给出了SAR图像自动目标识别系统的主要评价原则。     

一种低比特率下的快速SAR图像压缩方法*

提出了一种低比特率的快速压缩方法,通过基于小波的降采样增强像素间的相关,继而使用小波包分解降采样后的图像并使用基于零块的方法对系数进行编码。实验结果表明用该方法进行压缩能够得到优于JPEG2000的主观效果,对边缘保持更好,并且计算量小,适合实时实现。     

Supervised polarimetric SAR classification method based on Fisher linear discriminant

A supervised polarimetric SAR land cover classification method was proposed based on the Fisher linear discriminant.The feature parameters used in this classification method could be selected flexibly according to land covers to be classified.Polarimetric and texture feature parameters extracted from co-registered multifrequency and multi-temporal polarimetric SAR data could be combined together for classification use,without consideration of the dimension difference of each feature parameter and the joint probability density function of those parameters.Experimental result with AGRSAR L/C-band full polarimetric SAR data showed that a total classification accuracy of 94.33% was achieved by combining the polarimetric with texture feature parameters extracted from L/C dual band SAR data,demonstrating the effectiveness of this method.     

星载多通道SAR的模糊特性研究

为解决星载SAR的高分辨率和宽测绘带之间存在的矛盾,引入了多通道模式．基于多通道SAR的工作原理,深入分析了多通道SAR模糊信号的来源;从子带滤波和多通道重构的角度,提出多通道SAR的方位模糊计算方法,最后给出了仿真实例．仿真结果表明,该方法能够有效应用于星载多通道SAR设计中,指导选择合适的脉冲重复频率,以降低方位模糊和距离模糊．