共查询到19条相似文献，搜索用时 125 毫秒
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波形激光雷达传感器所获取的遥感数据几乎可以提供后向散射信号的全部特征,除了距离观测值,需要通过进一步分析接收的脉冲获取更多关于地表实体的物理特性,因此,这些相关的物理特性成为研究地表覆盖的新领域。通过总结利用波形信号处理技术进行地表覆盖解译工作的研究进展,开展了南极查尔斯王子山脉地区地表覆盖分类工作。首先,描述了GLAS的数据结构;其次,总结了相关的信号处理技术;然后,从实际应用角度讨论了方法的可用性;最后,以南极查尔斯王子山脉地区为例,实践了基于ICESat GLAS完整波形信号处理技术的地表覆盖分类方法。 相似文献
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土地覆盖制图：基于最优化遥感数据的支撑向量机分类 总被引：1，自引：0，他引：1
遥感数据具有在不同空间、光谱和时间尺度上获取地表测量信息的能力,使其成为获取土地覆盖信息的一个主要数据源。影像分类即把卫星影像上的相关像元划分给某类已知的土地覆盖类型的过程。支撑向量机（SVMs）是一种土地覆盖分类的新技术。三种常用的SVMs是：基于线性和多项式的SVM以及具有高斯核函数的SVM分类器,分类能否成功地应用有赖于其各自选择的最佳参数。但是海量的遥感数据使得这些参数的确定速度十分缓慢。本文研究了一种新的基于最优化遥感数据压缩技术的SVM分类方法。研究显示用于获取SVM参数的数据量能够在不影响土地覆盖的分类精度的前提下进行压缩。数据压缩成功的应用于多项式和高斯核函数的SVM分类,而线性SVM的分类精度却非常低。 相似文献
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全波形激光雷达后向散射回波,通过分解返回波形获取多种地物属性信息,在森林结构参数反演方面具有显著的优势,但是,当波形变形或者存在饱和度和前向散射时,高斯分量参数确定不准确以及有效波形起始位置不准确,降低波形分解精度。本文采用高斯混合模型对波形进行拟合,利用期望最大算法估计混合模型参数,抑制高斯分量初值敏感问题,特别是在大范围树高估算且要求一定精度的时候,以确定波形分解并且反演树高。本算法基于C++编程实现,实验结果表明,高斯混合模型能较好地拟合GLAS波形数据且对树高提取精度提升明显,该方法有着很好的有效性、稳定性和精确性。 相似文献
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针对大光斑激光雷达回波信号噪声影响森林冠顶高估测精度,且回波分析法判定回波位置受限于平坦地区的问题,利用高斯低通滤波和小波去噪两种方法对GLAS波形进行去噪处理,提出了结合均方根倍差法和回波分析法来判定回波位置的有效算法。经小波去噪后信号的信噪比23.360 704,均方根误差为0.000 233 3,经均方根倍差法和回波分析法相结合来判定回波位置估测的冠顶高结果与实测结果相关性系数r值为0.864,效果均优于高斯低通滤波去噪。基于GLAS回波数据实验结果表明:小波去噪较好地实现了对回波信号的去噪处理,均方根倍差法和回波分析法相结合,实现了对坡度相对较大地区的GLAS波形的回波开始位置和地面回波位置的准确判定,对森林冠顶高的精确估算具有重要意义。 相似文献
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林木空间格局对大光斑激光雷达波形的影响模拟 总被引：5，自引：1，他引：5
激光雷达是近年来国际上发展十分迅速的主动遥感技术，在森林参数的定量测量和反演上取得了成功应用。激光雷达具有与被动光学遥感不同的成像机理，对植被空间结构和地形的探测能力很强。大光斑激光雷达系统一般指光斑直径在8—70m、连续记录激光回波波形的激光雷达系统。由于大光斑连续回波的激光雷达的光斑尺寸通常大于林木冠幅，波形中往往包含了森林冠层和许多森林元素的信息而不仅仅是单株树的信息。对于搭载在ICESAT卫星上的GLAS而言，光斑直径为70m，因此光斑对应着一片森林，包括很多棵树，在GLAS的激光光斑内树木的空间分布会有一定变化。同时激光雷达发射的脉冲信号在激光光斑内的分布也不均匀，而是从中心到边缘呈递减的分布。因此树木空间分布模式的变化对波形会产生一定的影响。通过对几种典型的树木空间格局进行模拟（包括规则分布、均匀（随机）分布和集群分布），假定激光光斑内能量呈高斯分布，模拟了各种树木分布模式林分的激光雷达信号。从模拟结果可见，森林的空间分布模式对大光斑激光雷达波形有明显的影响，对于波形面积（AWAV）和波形半能量高度（HOME），规则分布〉随机分布〉团状分布。其中对于HOME而言，规则分布和随机分布十分接近，而对于AWAV而言，聚集中心的变动不太敏感。 相似文献
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基于30 m地表覆盖数据产品完成湿地精细化分类,能够更好地满足当前较高分辨率及较详尽全球湿地数据的应用需求。本文在深入分析湿地分类体系与细化方法的基础上,提出以湿地细化类别的定义、多元知识的分层分类、亚类数据精细化提取为主线的总体研究思路,制定了基于先验知识的对象系统筛选、基于森林数据的同位像元提取、基于最佳阈值的极大似然掩膜的主体分类方法,并应用于数据生产实践获得8个亚类信息。该方法克服了常规手段普遍存在的周期长、效率低等弊端,实现了全球较高分辨率湿地亚类数据的快速精确制图,总体分类精度达82.6%,对地理世情及其他地表覆盖研究具有借鉴意义。 相似文献
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介绍地学激光测高系统（ICESAT／GLAS）基本原理和ICESAT／GLAS的数据产品，对其中包含的误差和应采用的改正模型进行分析。并对冰盖、海冰、陆地以及海面等不同地表特征下波形的处理算法进行研究，包括表面特征参数的提取算法，如表面倾斜度算法。同时，研究脚点的定位算法。 相似文献
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激光测高仪回波波形饱和现象客观存在,为增加可用激光点数目、提高饱和波形测高精度,本文提出了一种波形饱和识别与测高误差改正方法,首先,利用回波波形峰度系数对饱和波形进行识别,然后,针对饱和现象对波形高斯拟合的影响,计算高斯拟合波形与原始波形相交区域的形心位置,以形心位置差异确定因波形饱和导致的测高误差并改正。最后,采用ICESat/GLAS（Ice,Cloud and land Elevation Satellite/Geo-science Laser Altimeter System）在青海湖、纳木错、色林错采集的波形数据进行实验。结果表明,经本文算法改正后数据误差均值为0.03 m,大型湖泊区域可实现约0.05 m的测高精度,结合峰度的饱和识别方法可以对波形进行有效筛选,可发现GLAS遗漏的饱和波形,饱和改正算法可以有效改正波形饱和引起的测高误差,改正后精度明显优于GLAS提供的饱和改正结果,相关结论对高分七号卫星激光波形处理有一定参考价值。 相似文献
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激光测高卫星在获取全球高程控制点方面具有独特的优势,本文针对ICESat(Ice,Cloud and land Elevation Satellite)卫星上搭载的地球激光测高系统GLAS(Geo-science Laser Altimetry System),提出了一种多准则约束的高程控制点筛选算法。算法综合利用全球公开版的SRTM(Shuttle Radar Topography Mission)DEM数据对GLAS进行粗差剔除,然后利用GLA14产品中的云量、姿态质量标记、饱和度参数、增益参数等多种与测距有关的属性参数进行粗粒度的筛选,保留受云层、大气、地表反射率等影响较小的激光足印点,最后结合GLA01的波形特征参数做进一步精细筛选,提取出高精度的激光点作为高程控制点。本文还采用天津、河北两个实验区的数据,利用高精度的DEM成果数据对筛选的结果进行了验证。实验结果表明,经多准则约束筛选后的激光足印点具有很高的高程精度,能够作为1∶50000甚至1∶10000立体测图时的高程控制点使用,研究结论可为国产高分辨率卫星在境外地区进行无地面控制点的立体测图提供参考。 相似文献
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S. Ghosh S. Nandy S. Patra S. P. S. Kushwaha A. Senthil Kumar V. K. Dadhwal 《Journal of the Indian Society of Remote Sensing》2017,45(2):327-335
In the present study, parameters derived from Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System (GLAS) full waveform were used for land cover classification in western part of Doon valley, Uttarakhand, India. Three parameters, viz, height, front slope angle (afslope) and canopy return ratio (rCanopy) were extracted from the returned full waveform signals. k-means (KM), partitioning around medoids (PAM), and fuzzy c-means (FCM) with different cluster sizes were used for classifying the land cover types with the help of GLAS-derived parameters. Among the clustering methods, KM performed the best. The overall accuracy (89.41 %) of all methods were quite significant with cluster size three i.e. with three classes forest, mango orchard and other class including agriculture, barren/fallow land, settlement, dry river bed, etc. The accuracy of the PAM (60 %) and the FCM (68.4 %) decreased drastically at four clusters with the separation of agriculture from barren/fallow land. The accuracy of the PAM and the FCM further decreased with increase in the number of clusters whereas KM showed reliable results for all clusters. KM with five clusters was able to distinguish five different land covers, viz, forest, mango orchard, agriculture and barren/fallow land and other class including settlement, dry river bed, etc. with an overall classification accuracy of 72.93 %. The study presents a method for classifying land cover types using GLAS full waveform data. 相似文献
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Yanqiu Xing Alfred de Gier Junjie Zhang Lihai Wang 《International Journal of Applied Earth Observation and Geoinformation》2010
Light Detection And Ranging (LiDAR) has a unique capability for estimating forest canopy height, which has a direct relationship with, and can provide better understanding of the aboveground forest carbon storage. The full waveform data of the large-footprint LiDAR Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat), combined with field measurements of forest canopy height, were employed to achieve improved estimates of forest canopy height over sloping terrain in the Changbai mountains region, China. With analyzing ground-truth experiments, the study proposed an improved model over Lefsky's model to predict maximum canopy height using the logarithmic transformation of waveform extent and elevation change as independent variables. While Lefsky's model explained 8–89% of maximum canopy height variation in the study area, the improved model explained 56–92% of variation within the 0–30° terrain slope category. The results reveal that the improved model can reduce the mixed effects caused by both sloping terrain and rough land surface, and make a significant improvement for accurately estimating maximum canopy height over sloping terrain. 相似文献
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The Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and land Elevation Satellite (ICESat) is a spaceborne LiDAR sensor. It is the first LiDAR instrument which can digitize the backscattered waveform and offer near global coverage. Among others, scientific objectives of the mission include precise measurement of vegetation canopy heights. Existing approaches of waveform processing for canopy height estimation suggest Gaussian decomposition of the waveform which has the limitation to properly characterize significant peaks and results in discrepant information. Moreover, in most cases, Digital Terrain Models (DTMs) are required for canopy height estimation. This paper presents a new automated method of GLAS waveform processing for extracting vegetation canopy height in the absence of a DTM. Canopy heights retrieved from GLAS waveforms were validated with field measured heights. The newly proposed method was able to explain 79% of variation in canopy heights with an RMSE of 3.18 m, in the study area. The unexplained variation in canopy heights retrieved from GLAS data can be due to errors introduced by footprint eccentricity, decay of energy between emitted and received signals, uncertainty in the field measurements and limited number of sampled footprints.Results achieved with the newly proposed method were encouraging and demonstrated its potential of processing full-waveform LiDAR data for estimating forest canopy height. The study also had implications on future full-waveform spaceborne missions and their utility in vegetation studies. 相似文献
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Space born systems like Geoscience Laser Altimeter System (GLAS) onboard collect data for ice, cloud and Land. Elevation satellite
(ICESat) collects an unparalleled data set as waveform over terrestrial targets, helps in evaluating the global elevation
data. In this study we compared the Digital Elevation Surface (DES) generated by Cartosat-1 point data and DES generated by
merging the Cartosat-1 data with ICESat data. Outputs in the form of interpolated surfaces were evaluated with the help of
differential global positioning system (DGPS) points collected from study area. The study showed the results that the DES
generated from Cartosat — 1 data had less elevation accuracy when compared with the DGPS data. While merging Cartosat-1 point
height data with ICESat/GLAS data resulted in better accuracy. On the practical side for processing the interpolation, based
on the research the ICESat /GLAS with Cartosat-1 height data can produce better DES compared to the Cartosat-1 stereo data.
The DES was generated using geostatistical interpolation methods in which the global polynomial method proved to be the better
for generating the surface compare to other interpolation techniques studied in this work. For co-kriging method, the accuracy
decreases compare to the kriging interpolation, due to the complexity of parameters that were used for interpolation. On the
theory side, based on this research the statement of which interpolation technique is better than the other cannot be mentioned
easily, because these are based on the data type, parameters and also on method of interpolation. So research experiment should
be more intensely and with more focused. 相似文献
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GNSS-R信号在面向复杂场景的陆表遥感应用中存在信噪比（SNR）低和有效信息难以辨别提取的问题，严重制约了其在陆表遥感领域的应用拓展。为从海量低信噪比的星载GNSS-R陆表数据中快速区分杂波信号和有效信息，本文通过统计归纳分析，基于星载时延多普勒图（DDM）相关峰的显著程度评定，提出了一种DDM波形分类方法。随后，利用该方法对UK TechDemoSat-1（TDS-1）星载陆表观测数据进行了波形分类。最后，比较了分类后波形对应的SNR情况，同时结合典型地物类型对分类结果进行了相关性分析，证实了波形分类方法的可行性与有效性。 相似文献
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结合机载、星载激光雷达对GLAS(地球科学激光测高系统)光斑范围内的森林地上生物量进行估测,并利用MODIS植被产品以及MERIS土地覆盖产品进行了云南省森林地上生物量的连续制图。机载LiDAR扫描的260个训练样本用于构建星载GLAS的森林地上生物量估测模型,模型的决定系数(R2)为0.52,均方根误差(RMSE)为31Mg/ha。研究结果显示,云南省总森林地上生物量为12.72亿t,平均森林地上生物量为94Mg/ha。估测的森林地上生物量空间分布情况与实际情况相符,森林地上生物量总量与基于森林资源清查数据的估测结果相符,表明了利用机载LiDAR与星载ICESatGLAS结合进行大区域森林地上生物量估测的可靠性。 相似文献
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Hyperspectral image and full-waveform light detection and ranging (LiDAR) data provide useful spectral and geometric information for classifying land cover. Hyperspectral images contain a large number of bands, thus providing land-cover discrimination. Waveform LiDAR systems record the entire time-varying intensity of a return signal and supply detailed information on geometric distribution of land cover. This study developed an efficient multi-sensor data fusion approach that integrates hyperspectral data and full-waveform LiDAR information on the basis of minimum noise fraction and principal component analysis. Then, support vector machine was used to classify land cover in mountainous areas. Results showed that using multi-sensor fused data achieved better accuracy than using a hyperspectral image alone, with overall accuracy increasing from 83% to 91% using population error matrices, for the test site. The classification accuracies of forest and tea farms exhibited significant improvement when fused data were used. For example, classification results were more complete and compact in tea farms based on fused data. Fused data considered spectral and geometric land-cover information, and increased the discriminability of vegetation classes that provided similar spectral signatures. 相似文献