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1.
云类识别是实现卫星云图自动分析的基础,针对卫星云图易受噪声干扰且不同云系往往相互交叠的特点,构造一种面向云类识别的自适应模糊支持向量机。该方法不仅改进了隶属度函数的表现形式,而且通过定义控制临界隶属度和隶属度衰减趋势的参数,使隶属度能根据不同云系样本的具体分布特性自适应调整,解决了传统模糊支持向量机的隶属度函数难以反映样本分布的问题。在MTSAT卫星云图上的实验结果表明,通过提取云图可见光通道的反照率、红外通道的亮温及三种亮温差作为云图的光谱特征,并结合统计纹理特征,所构造的自适应模糊支持向量机分类器能有效区分晴空区、低云、中云、高云及直展云;云类识别准确率优于标准支持向量机和传统模糊支持向量机,且具有更强的稳定性和自适应性。 相似文献
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针对云检测在高亮度地表以及雪覆盖区域存在过度检测的问题,设计了一种不依赖热红外波段的增强型多时相云检测EMTCD(Enhanced Multiple Temporal Cloud Detection)算法。首先,利用云的光谱特征建立单时相云检测规则,并基于云、雪的光谱差异构建了增强型云指数ECI(Enhanced Cloud Index),改进了云、雪的区分能力;其次,以同一区域无云影像为参考,基于ECI指数构建了多时相云检测算法,较好地克服了单时相云检测中高亮度地表、雪和云容易混淆的问题,提高了云检测的精度;最后,选择两个典型区域的Landsat-8 OLI影像,对比分析了不同算法的云检测结果。实验结果表明:ECI指数能够有效区分云、雪,EMTCD方法的平均检测精度达到93.2%,高于Fmask(Function of mask)(81.85%)、MTCD(Multi-Temporal Cloud Detection)(66.14%)和Landsat-8地表反射率产品LaSRC(Landsat-8 Surface Reflectance Code)的云检测结果(86.3%)。因此,本文提出的EMTCD云检测算法能够有效地减少高亮度地表和雪的干扰,实现不依赖热红外波段的高精度云检测。 相似文献
3.
云的存在会对遥感影像的处理及目标识别等产生影响,因此,自动提取云对高分辨率卫星影像的应用具有重要意义。高分影像上更加复杂的云的细节形态及似云目标的干扰,使得高分影像的自动云提取难以达到实用水平。本文以雪地为例,选取形状、纹理和边缘3个差异化特征作为云与似云目标区分的关键,提出了一种区分高分辨率遥感影像中云和似云目标的云检测算法。首先利用Wallis滤波对输入影像进行预处理,增强影像中不同尺度的影像纹理模式;然后对影像进行快速稳定的均值漂移分割,利用灰度和纹理特征构成支持向量机的第一层分类器,将分割后的区域对象分成"云"和普通地物,再利用边缘、形状、纹理等特征结合灰度特征构成支持向量机的第二层分类器,将"云"区分为云区和似云目标;最后使用Grab-cut对云检测结果进行边缘迭代精化。本文算法取得了优良的试验结果,证明了算法在似云目标干扰下对高分辨率遥感影像进行精确云检测的能力。 相似文献
4.
利用风云三号A星MERSI数据,基于ENVI ZOOM软件平台采用面向对象的多尺度影像分割技术,并结合监督分类技术提取云层边缘线,继而对影像进行反演处理时剔除云层覆盖区域的干扰。研究表明,与同时段彩色卫星云图进行视觉对比,分类结果较理想,该分类方法易于操作,可有效提高解译精度。 相似文献
5.
青藏高原积雪对全球气候变化十分重要,针对已有积雪遥感判识方法中普遍采用的可见光与红外光谱数据易受复杂地形与高海拔影响,导致青藏高原地区积雪判识精度较低的问题,提出了一种基于多光谱遥感与地理信息数据特征级融合的积雪遥感判识方法:以风云三号卫星可见光与红外多光谱遥感资料与多要素地理信息作为数据源,由地面实测雪深数据与现有积雪产品交叉筛选出样本标签,构建并训练基于层叠去噪自编码器(SDAE)的特征融合与分类网络,从而有效辨识青藏高原遥感图像中的云、积雪以及无雪地表。经地面实测雪深数据验证,该方法分类精度显著高于使用相同数据源的FY-3A/MULSS积雪产品,略高于国际主流积雪产品MOD10A1与MYD10A1,并且年均云覆盖率最低。试验结果表明该方法可有效地减少云层对积雪判识的干扰,提升分类精度。 相似文献
6.
A V Kulkarni J Srinivasulu S S Manjul Mathur P 《Journal of the Indian Society of Remote Sensing》2002,30(1-2):73-80
Snow is highly reflective in the visible region of the electromagnetic spectrum making it possible to easily distinguish on a satellite image. However, cloud cover and mountain shadows pose a serious problem in the identification of snow in a mountainous region. Therefore, to identify snow in such an environment, a Normalized Difference Snow Index (NDSI) has been applied. The NDSI is based on the high reflectance of snow in the visible region and its low reflectance in the SWIR region, whereas, reflectance of cloud remains high compared to snow in the SWIR region. Efforts have been made to carry out field observations on reflectance of various land features near Manali in Himachal Pradesh (HP) to develop NDSI values for identifying snow. Field data have been collected using three field radiometers, viz., Multi-band Ground Truth Radiometer (GTR) operating in the 12 spectral bands ranging from visible to near-infrared wavelengths, Near-Infrared Ground Truth Radiometer (NIGTR) operating in the SWIR range, and Ratio-Radiometer (RR) operating in two spectral bands, one in the visible range, and another band in the SWIR range. All these three field radiometers have been designed and developed indigenously at the Space Applications Centre (ISRO), Ahmedabad. NDSI values for all types of snow, such as, fresh, clear, patchy and wet, have been found to be in the range 0.9 to 0.96. In addition, the NDSI value for snow under mountain shadow is found to be more than 0.9. This suggests the use of NDSI method for snow cover monitoring under mountain shadow. NDSI values for other land features such as soil, vegetation, and rock were substantially different than snow. However, water bodies have NDSI values close to snow and they need to be masked during snow cover delineation using NIR band. 相似文献
7.
A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors 总被引：1，自引：0，他引：1
Fernando Sedano Pieter KempeneersPeter Strobl Jan KuceraPeter Vogt Lucia SeebachJesús San-Miguel-Ayanz 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(5):588-596
This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow. 相似文献
8.
随着激光雷达技术的发展及广泛应用,点云数据的分类及理解成为了目前一个研究热点。本文研究了较复杂的电力线路走廊场景的点云自动分类方法,目标类别为地面、植被、建筑物、电力塔、电力线等。本文首先归纳、定义了点云分类所需的关键特征,并利用JointBoost实现地物分类;同时,考虑到点云数据量大,其分类速度较慢,本文结合地物空间上的相互关联关系,提出了一种序列化的点云分类及特征降维方法。该方法在保证分类精度的前提下,使分类所需特征维数降低,缩短了分类所需时间。实际的电力线路走廊的激光扫描点云数据分类实验证明本文研究的分类方法的有效性。 相似文献
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10.
利用数字化的NOAA AVHRR数据进行小比例尺、准同步宏观制图是一种低成本和快速的制图方法。笔者应用本文方法制作了第一幅小比例尺的、完整的中国假彩色卫星影象地图。它包含了全部的南中国海,这是使用其他的遥感数据(如陆地卫星或SPOT卫星)难以做到的。该图采用了热红外、近红外和可见光的红波段所构成的假彩色合成方案,提供了丰富的色彩和影象信息。本图制作时实施的数据处理内容包括:地图投影变换、影象反差增强和锐化、彩色平衡调整、去云处理和海陆分离处理、利用植被指数原理的影象时相修正和数字镶嵌操作等。根据影象地图的载负量和视觉效果,图上精心选取并设计了地理要素、地图符号和注记。该图是应用遥感新技术开发的地图新产品,被十七届国际摄影测量与遥感会议选为展示成果,并被该会评为获奖成果。 相似文献
11.
Intercomparison of deep convective cloud fractions from passive infrared and microwave radiance measurements 总被引：2，自引：0，他引：2
The common method to detect deep convective clouds is from satellite infrared (IR) measurements, which is based on thresholds of cloud-top temperatures. However, thick cirrus clouds with high cloud tops are difficult to screen out using IR methods, resulting in an overestimation of deep convective cloud fractions. Two aircraft cases with simultaneous Millimeter-wave Imaging Radiometer, Multispectral Atmospheric Mapping Sensor, and ER-2 Doppler radar measurements during the Convection and Moisture Experiment 3 in August 1998 are analyzed to investigate the influence of high thick cirrus clouds on two previously developed IR methods. In contrast, a microwave method based on the brightness temperature differences between the three water vapor channels around 183.3 GHz of the Advanced Microwave Sounding Unit-B (AMSU-B) (183.3/spl plusmn/1,183.3/spl plusmn/3, and 183.3/spl plusmn/7 GHz) can screen out high thick cirrus clouds efficiently. The tropical deep convective cloud fractions (30/spl deg/S-30/spl deg/N) estimated by the IR methods and the AMSU-B method are compared. Although their geographical distributions are in well agreement with each other, the total fractions detected by the IR methods are about 2-3.5 times greater than that detected by the AMSU-B method. Moreover, the overestimation of deep convective cloud fractions by the IR method (11-/spl mu/m brightness temperature less than 215 K) can result in a displacement in the detected location of the deep convective clouds. The average thick cirrus clouds cover 2.5 times the area of the deep convective clouds that generates them. 相似文献
12.
资源三号测绘卫星自动云检测 总被引：4，自引：0，他引：4
光学卫星遥感影像自动云检测是卫星产品生产系统的一个重要环节。利用资源三号卫星编目生成的浏览图,采用树状判别结构进行云检测,对浏览图进行分块,提取子块图像的特征进行云地分类。由于云类和地物类过于繁杂,且浏览图的分辨率较低,传统通过图像特征对云地进行分类的算法有很大的局限性,针对这一问题,本文提出了在分类之前对原始的子块图像进行增强处理,扩大云和地物的纹理差异,然后以二阶矩、一阶差分等作为云地分类的图像特征,并在多尺度空间内进行特征延拓,经过综合分析估计云在影像中的比例。该云检测算法应用于资源三号卫星应用系统工程,实际测试结果表明,该算法能够较好地提升云量检测的准确率。 相似文献
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针对光学卫星成像时云导致的测区有效成像覆盖周期不确定问题,在卫星轨道计算的基础上,结合云量时空分布的历史统计规律、卫星历史观测数据的覆盖能力,研究建立光学卫星测区有效成像覆盖周期预估模型,回答测区有效成像覆盖效率与时间等问题,直接服务于光学卫星数据获取、产品生产和应用服务的计划安排和应用决策。 相似文献
15.
N. Sifakis D. Paronis I. Keramitsoglou 《International Journal of Applied Earth Observation and Geoinformation》2004,5(4):305
This paper describes an operational application of AVHRR satellite imagery in combination with the satellite-based land cover database CORINE Land Cover (CLC) for the comprehensive observation and follow-up of 10000 fire outbreaks and of their consequences in Greece during summer 2000. In the first stage, we acquired and processed satellite images on a daily basis with the aim of smoke-plume tracking and fire-core detection at the national level. Imagery was acquired eight times per day and derived from all AVHRR spectral channels. In the second stage, we assessed the consequences of fire on vegetation by producing a burnt-area map on the basis of multi-annual normalised vegetation indices using AVHRR data in combination with CLC. In the third stage we used again CLC to assess the land cover of burnt areas in the entire country. The results compared successfully to available inventories for that year. Burnt area was estimated with an accuracy ranging from 88% to 95%, depending on the predominant land cover type. These results, along with the low cost and high temporal resolution of AVHRR satellite imagery, suggest that the combination of low-resolution satellite data with harmonised CLC data can be applied operationally for forest fire and post-fire assessments at the national and at a pan-European level. 相似文献
16.
云覆盖作为天气和气候变化的一个重要因子,对地表-大气能量平衡和水循环有着重要的影响,因此,快速、准确地利用卫星遥感技术检测云覆盖具有重要的实用价值和科学意义。利用卫星遥感数据,尤其是常用的Moderate Resolution Imaging Spectroradiometer(MODIS)影像数据,因其具有较高的光谱和时间分辨率,以及2330 km扫描幅宽,为大范围实时、准确地进行云检测提供了可能。目前,基于MODIS数据发展了大量的云检测方法,但因地表类型的多样性和大气状况(如空气污染和沙尘事件等)的复杂性,目前已有的云检测方法,检测精度通常具有较大的不确定性,且针对不同地表和大气状况缺乏普适性,同时也缺乏对检测精度的定量化评估。因此,本文首先比较了常用的3种云检测算法,并基于前人经验提出了两种改进方法(方法4和方法5),首先区分出云和冰雹,摒弃了不稳定的亮温波段,两种算法均适用于复杂地表和大气状况的云检测算法。结果显示,方法5可以较好地应用于基于MODIS数据的云检测,总体精度达92.6±7%,改进了现有基于MODIS数据的云检测算法;方法4平均总体精度82.9±13%,虽然精度相对较低,但云残留少,适合作为对云敏感度高的研究工作的云检测方法。 相似文献
17.
Large area tree maps, important for environmental monitoring and natural resource management, are often based on medium resolution satellite imagery. These data have difficulty in detecting trees in fragmented woodlands, and have significant omission errors in modified agricultural areas. High resolution imagery can better detect these trees, however, as most high resolution imagery is not normalised it is difficult to automate a tree classification method over large areas. The method developed here used an existing medium resolution map derived from either Landsat or SPOT5 satellite imagery to guide the classification of the high resolution imagery. It selected a spatially-variable threshold on the green band, calculated based on the spatially-variable percentage of trees in the existing map of tree cover. The green band proved more consistent at classifying trees across different images than several common band combinations. The method was tested on 0.5 m resolution imagery from airborne digital sensor (ADS) imagery across New South Wales (NSW), Australia using both Landsat and SPOT5 derived tree maps to guide the threshold selection. Accuracy was assessed across 6 large image mosaics revealing a more accurate result when the more accurate tree map from SPOT5 imagery was used. The resulting maps achieved an overall accuracy with 95% confidence intervals of 93% (90–95%), while the overall accuracy of the previous SPOT5 tree map was 87% (86–89%). The method reduced omission errors by mapping more scattered trees, although it did increase commission errors caused by dark pixels from water, building shadows, topographic shadows, and some soils and crops. The method allows trees to be automatically mapped at 5 m resolution from high resolution imagery, provided a medium resolution tree map already exists. 相似文献
18.
有效监测人工水产养殖水面的分布变化对于海洋资源管理、生态环境保护、防灾减灾具有重要意义。本文以Landsat 5、SPOT 5和GF-1卫星影像为数据源,选择广东省北莉岛为研究区,使用线性光谱解混方法获取中等空间分辨率卫星影像的人工水产养殖水面面积,通过面向对象多尺度分割的方法结合支持向量机分类算法提取高空间分辨率卫星影像的人工水产养殖水面分布。研究结果表明,与单一卫星影像相比,综合多源中高空间分辨率卫星数据延长了人工水产养殖水面变化分析可追溯的时间跨度,提高了监测精度;联合光谱解混和面向对象分类方法开展人工水产养殖长时序遥感监测是可行的。近20多年来,北莉岛人工水产养殖水面的面积经历了先增加后缓慢减少的变化过程,1995—2000年平均增速为23.39 hm2/a,2000—2006年平均增速为23.95 hm2/a,2006—2019年平均减少速度为1.96 hm2/a。 相似文献
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LiDAR点云的分类提取是点云数据处理中的首要步骤。为了提高复杂场景中点云数据分类提取方法的适用性,文中根据三维数学形态学思想,提出一种基于地物空间形状特征的点云提取方法。方法首先建立网格索引,划分网格空间,进行点云数据组织,然后根据地物在网格空间中的形状特征设计出四种参数可控的空间网格算子,最后结合点云反射强度信息自动提取特定地物点云。通过对复杂场景中的铁路地物要素LiDAR点云中建筑、电力杆线、铁路轨道的提取和郊区机载LiDAR点云中的地面与建筑屋顶的提取,验证提取算法的适用性,为点云分类提取功能模块的程序设计提供便捷方法。 相似文献
20.
针对高分五号可见短波红外高光谱相机AHSI (visible-shortwave infrared Advanced HyperSpectral Imager)可见光波段存在的薄云干扰,本文提出了一种联合统计信息与散射模型的校正方法。利用AHSI影像邻近波段间地表与云雾辐射的统计差异,实现对不同场景下相对薄云辐射RCR (Relative Cloud Radiance)的准确估计。基于此,根据不同波段的散射特性,分别利用分级暗目标统计和散射模型约束策略,获取可见光波段的绝对云辐射强度,最终实现影像校正。通过设置模拟与真实实验对方法的有效性和鲁棒性进行目视和定量检验。模拟实验中,可见光波段内的薄云干扰均可被有效地去除,校正结果与真实地表十分一致;此外,RMSE (RootMean-Square Error),MAE (Mean Absolute Error)和SA (Spectral Angle) 3个评价指标的值分别为1.9891,1.6822和0.4901,远小于对比方法。真实实验中,不同场景内的薄云可被有效抑制,在较为准确恢复降质地表信息的同时保持晴空区光谱特征;Q指数,SSIM (Structural Similarity Index)和UQI (Universal Quality Index)的计算结果优于对比方法。综上,本文提出方法可用于不同场景下高分五号AHSI影像可见光波段的薄云校正,可得到目视效果良好且光谱保真度高的校正结果。 相似文献