基于FGHT的SAR影像形状检测算法     

JI Yuan  QIN Zhi-yuan  MAO Li 《测绘学院学报》2008,(1)

形状检测在目标识别中有着重要作用。广义Hough变换(GHT)具有很好的全局特性,是常用的目标形状检测算法。但由于SAR影像具有强噪声等特点,常用的广义Hough变换算法可能会产生定位不准确的问题,甚至会出现错检的情况。文中将GHT中投票不集中的区域看做"模糊投票点",构造了隶属函数,并据此设计了模糊广义Hough变换算法(FGHT),通过优化参考点的位置达到检测结果精确定位的目的。实验对比证明,该算法在强噪声情况下具有更好的鲁棒性。  相似文献   

热带人工林SAR散射组成及对遥感估测叶面积指数的影响     

高帅  牛铮 《地球科学进展》2008,23(9):982-989

基于RADARSAT SAR数据,利用MIMICS（ Michigan Microwave Canopy Scattering）模型模拟森林组分（冠层、树干层、地表）雷达后向散射,模拟研究表明在稀疏的人工林地区,地表层与森林冠层的直接散射是影响森林总的后向散射中最重要的两个因素。在同样的地表条件与森林环境假设下,阔叶林的模拟结果与影像的一致性要优于针叶林,针叶林由于受到地形起伏的影响,难以利用模型模拟森林的散射情况。同时,研究发现,利用森林郁闭度可以定量的表示森林冠层直接散射与总散射的相关关系,因而在一定的条件下得到冠层直接散射。最后,对该方法进行了简单的验证。、  相似文献   

一种高分辨率SAR影像道路提取方法     

刘振宇  余洁 《测绘科学》2017,42(11)

针对高分辨率SAR影像中细节信息损坏道路的面特征结构、影响道路提取,基于影像统计特征,给出一种结合区域生长和细节信息识别的道路提取方法。该方法通过区域生长提取呈现面特征的暗目标(道路框架),利用CFAR算法识别细节,通过形态学融合得到最终结果。为降低高分辨率影像区域异质性对提取结果影响,提出了一种自适应CFAR算法,相比之前算法可自适应删除干扰点;并引入有效表征影像统计的GA0分布。利用海南省陵水黎族自治县机载X波段高分辨率SAR数据的幅度影像进行实验,结果表明,该方法能有效提取呈面特征的道路,获得准确的道路宽度和中心线信息。  相似文献   

极化分解后多纹理特征的建筑物损毁评估     

陈启浩  聂宇靓  李林林  刘修国 《遥感学报》2017,21(6):955-965

准确地获知灾区的建筑物损毁程度能为抗震救灾和灾后重建提供决策依据。利用震后极化合成孔径雷达(SAR)数据,该文提出了一种综合利用极化分解后多纹理特征的震后建筑物损毁评估方法。首先,用Pauli分解的π/4偶次散射分量剔除非建筑区;其次,用Pauli分解的π/4偶次散射分量的方差特征、对比度特征和Pauli分解的奇次散射分量的对比度特征识别倒塌建筑物,并分别基于区块计算建筑物损毁指数;最后,综合3个纹理特征完成建筑物的损毁评估。采用玉树震后RADARSAT-2数据和东日本大地震后ALOS-1数据的实验验证了所提方法对建筑物损毁评估的有效性,该方法对玉树城区和日本石卷城区的重度、中度和轻度损毁建筑评估的总体精度分别为74.39%和80.26%。与其他方法的对比实验表明,该方法能减少取向角的影响,对存留有少数与方位向平行的完好建筑物的倒塌区、大取向角的完好建筑区的评估更为准确。  相似文献   

Remote detection of geological structures: an application to the Aravalli region,western India     

Debaditya Acharya  Alok Porwal  Avik Bhattacharya 《国际地球制图》2017,32(3):257-273

Optical and microwave remote sensing data are used in conjunction with a digital elevation model to map lineaments in the central parts of the Aravalli region, Rajasthan, western India. Lineament maps interpreted from each data-set are subsequently combined to derive a composite lineament map of the area. Rose plots are used to identify the prominent trends of the lineaments and compared with published structural map of the study area. Three major trends are identified, namely, the NE–SW, NNE–SSW and EW, which are interpreted to be, related to the DF1, DF2 and DF4 deformation phases identified by the previous workers through field studies. The lineaments are classified as fold axes or faults, and a total of 10-fold axes and 30 faults mapped in the area.  相似文献   

Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area     

Mohamed Barakat A. Gibril  Suzana A. Bakar  Kouame Yao  Mohammed Oludare Idrees 《国际地球制图》2017,32(7):735-748

In this study, we investigated the performance of different fusion and classification techniques for land cover mapping in Hilir Perak, Peninsula Malaysia using RADAR and Landsat-8 images in a predominantly agricultural area. The fusion methods used are Brovey Transform, Wavelet Transform, Ehlers and Layer Stacking and their results classified into seven different land cover classes which include (1) pixel-based classifiers (spectral angle mapper (SAM), maximum likelihood (ML), support vector machine (SVM)) and (2) Object-based (rule-based and standard nearest neighbour (NN)) classifiers. The result shows that pixel-based classification achieved maximum accuracy of the optical data classification using SVM in Landsat-8 with 74.96% accuracy compared to SAM and ML. For multisource data classification, the highest overall accuracy recorded for layer stacking (SVM) was 79.78%, Ehlers fusion (SVM) with 45.57%, Brovey fusion (SVM) with 63.70% and Wavelet fusion (SVM) 61.16%. And for object-based classifiers, the overall classification accuracy is 95.35% for rule-based and 76.33% for NN classifier, respectively. Based on the analysis of their performances, object-based and the rule-based classifiers produced the best classification accuracy from the fused images.  相似文献   

An Information fusion approach for PALSAR data to retrieve soil moisture     

Ankita Jain 《国际地球制图》2017,32(9):1017-1033

Estimation of vegetation covered soil moisture with satellite images is still a challenging task. Several models are available for soil moisture retrieval in which water cloud model (WCM) is most common. But, it requires an estimation of accurate vegetation parameterization. Thus, there is a need to develop such an approach for soil moisture retrieval which minimize these limitations. Therefore, this paper deals with the soil moisture retrieval using fully polarimetric SAR data by fusing the information from different bands. Various polarimetric indices and observables were critically analysed, and found that the index; SPAN (total scattered power) gives better information of vegetation cover as compared to other indices/observables. Based on this, WCM model has been modified using SPAN as parameter and soil moisture content were retrieved.  相似文献   

A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations     

《International Journal of Applied Earth Observation and Geoinformation》2017

Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific characteristics such as high resolution, weather and daylight independence, have become a valuable source of information for environment monitoring and management. The discrimination capability of observations acquired by these sensors can be used for land cover classification and mapping. The aim of this paper is to propose an optimized kernel-based C-means clustering algorithm for agriculture crop mapping from multi-temporal PolSAR data. Firstly, several polarimetric features are extracted from preprocessed data. These features are linear polarization intensities, and several statistical and physical based decompositions such as Cloude-Pottier, Freeman-Durden and Yamaguchi techniques. Then, the kernelized version of hard and fuzzy C-means clustering algorithms are applied to these polarimetric features in order to identify crop types. The kernel function, unlike the conventional partitioning clustering algorithms, simplifies the non-spherical and non-linearly patterns of data structure, to be clustered easily. In addition, in order to enhance the results, Particle Swarm Optimization (PSO) algorithm is used to tune the kernel parameters, cluster centers and to optimize features selection. The efficiency of this method was evaluated by using multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Manitoba, Canada, during June and July in 2012. The results demonstrate more accurate crop maps using the proposed method when compared to the classical approaches, (e.g. 12% improvement in general). In addition, when the optimization technique is used, greater improvement is observed in crop classification, e.g. 5% in overall. Furthermore, a strong relationship between Freeman-Durden volume scattering component, which is related to canopy structure, and phenological growth stages is observed.  相似文献   

Satellite radar interferometry for monitoring subsidence induced by longwall mining activity using Radarsat-2, Sentinel-1 and ALOS-2 data     

《International Journal of Applied Earth Observation and Geoinformation》2017

This paper describes the simulation and real data analysis results from the recently launched SAR satellites, ALOS-2, Sentinel-1 and Radarsat-2 for the purpose of monitoring subsidence induced by longwall mining activity using satellite synthetic aperture radar interferometry (InSAR). Because of the enhancement of orbit control (pairs with shorter perpendicular baseline) from the new satellite SAR systems, the mine subsidence detection is now mainly constrained by the phase discontinuities due to large deformation and temporal decorrelation noise.This paper investigates the performance of the three satellite missions with different imaging modes for mapping longwall mine subsidence. The results show that the three satellites perform better than their predecessors. The simulation results show that the Sentinel-1A/B constellation is capable of mapping rapid mine subsidence, especially the Sentinel-1A/B constellation with stripmap (SM) mode. Unfortunately, the Sentinel-1A/B SM data are not available in most cases and hence real data analysis cannot be conducted in this study. Despite the Sentinel-1A/B SM data, the simulation and real data analysis suggest that ALOS-2 is best suited for mapping mine subsidence amongst the three missions. Although not investigated in this study, the X-band satellites TerraSAR-X and COSMO-SkyMed with short temporal baseline and high spatial resolution can be comparable with the performance of the Radarsat-2 and Sentinel-1 C-band data over the dry surface with sparse vegetation.The potential of the recently launched satellites (e.g. ALOS-2 and Sentinel-1A/B) for mapping longwall mine subsidence is expected to be better than the results of this study, if the data acquired from the ideal acquisition modes are available.  相似文献   

高分辨率星载SAR在立体测图中的应用研究     

卢圣奇周军元倪峰彭昀肖聪 《地理空间信息》2016,(6):35-37

以兰州市以北地区为研究对象,选取COSMO-SKYMED和Terra SAR-X数据,采用Map Matrix摄影测量工作站进行数据采集,生产1∶50 000比例尺的DLG数据,通过全野外检查点来分析成果数据的精度,最终成果满足1∶50 000DLG的精度。由此说明,高分辩率星载SAR能应用于湖北西部1∶50 000比例尺DLG生产。  相似文献