排序方式： 共有54条查询结果，搜索用时 31 毫秒
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由于受到大气的影响,传感器接收到的辐射信息不能真实地反映地表反射光谱信息,因此,从遥感影像中去除大气的影响,即进行大气校正,是高光谱遥感数据处理中极为重要的环节。文章介绍了EO-1hyperion高光谱数据的特点,以及用FLAASH（Fast Line of Sight Atmospheric Analysis of ... 相似文献
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高光谱遥感能以纳米量级宽度的窄波段及多达数百个的波段,对目标进行连续的光谱成像,但其海量数据及相邻波段高度相关造成的数据冗余却制约着它的应用.因此,对高光谱遥感影像分类须进行有效的处理、寻找最优特征,以增强地物的最大可分性.本文首先针对EO-4 Hyperion高光谱影像波段维数高,相关性强和数据量大等特点,利用独立成... 相似文献
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The uncertainties involved in remote sensing inversion of CDOM (Colored Dissolved Organic Matter) were analyzed in estuarine and coastal regions of three North American rivers: Mississippi, Hudson, and Neponset. Water optical and biogeochemical properties, including CDOM absorption and above-surface spectra, were collected in very high resolution. CDOM’s concentrations (ag(440), absorption coefficient at 440 nm) were inverted from EO-1 Hyperion images, using a quasi-analytical algorithm for CDOM (QAA-CDOM). Uncertainties are classified to five levels, in which the underwater measurement uncertainty (level 1), image preprocessing uncertainty (level 4) and inverse model uncertainty (level 5) were evaluated. Results indicate that at level 1, in situ CDOM measurement is significant with 0.1 in the unit of QSU and 0.01 in the unit of ag(440) (m−1). At level 4, surface wave is a potential uncertainty source for high-resolution images in estuarine and coastal regions. The remote sensing reflectance of wavy water is about 10 times of the truth. At level 5, the overall uncertainty of QAA-CDOM inversion is 0.006 m−1, with accuracy R2 = 0.77, k = 1.1 and RMSElog = 0.33 m−1. The correlations between uncertainties and other water properties indicate that the large uncertainty in some rivers, such as the Neponset and Atchafalaya, might be caused by high-concentration chlorophyll or sediments. The relationships among the three level uncertainties show that the level 1 uncertainty generally does not propagate into level 4 and 5, but the large uncertainty at level 4 usually introduce large uncertainty at level 5. 相似文献
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In this study, we test the potential of two different classification algorithms, namely the spectral angle mapper (SAM) and object-based classifier for mapping the land use/cover characteristics using a Hyperion imagery. We chose a study region that represents a typical Mediterranean setting in terms of landscape structure, composition and heterogeneous land cover classes. Accuracy assessment of the land cover classes was performed based on the error matrix statistics. Validation points were derived from visual interpretation of multispectral high resolution QuickBird-2 satellite imagery. Results from both the classifiers yielded more than 70% classification accuracy. However, the object-based classification clearly outperformed the SAM by 7.91% overall accuracy (OA) and a relatively high kappa coefficient. Similar results were observed in the classification of the individual classes. Our results highlight the potential of hyperspectral remote sensing data as well as object-based classification approach for mapping heterogeneous land use/cover in a typical Mediterranean setting. 相似文献
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Although remote sensing data have been used to estimate total suspended matter (TSM) in coastal waters, it has limitations when applied to estuary waters in low spatial resolution situations. The spatial resolution of ocean color satellites such as SeaWiFS and MODIS is usually ～1 km, and therefore is not adequate for small, local-scale areas such as the Zhujiang (Pearl) River estuary. In contrast, 30 m-resolution EO-1 Hyperion imagery has potential for studying TSM in localized areas. We measured the surface spectral radiance reflectance of the river estuary water in the visible and near infra-red spectral range. Sensitivity analysis indicated that the ratio of remote sensing reflectance at 813 nm (Rrs(813)) to reflectance at 559 nm (Rrs(559)) could be used to estimate TSM concentration, and a linear relationship was established between the ratio and in-situ TSM concentration. We applied the linear relationship to Hyperion imagery to map TSM concentration in the estuary. The Hyperion imagery provided sufficient spatial resolution to detect spatiotemporal changes in TSM concentrations in the estuary small estuary area. This study demonstrated the usefulness of Hyperion imagery for mapping the distribution of TSM in estuary waters. 相似文献
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Rongxing Li Jung-Kuan Liu Anuchit Sukcharoenpong Jiangye Yuan Haihong Zhu Shaoming Zhang 《Marine Geodesy》2013,36(3):271-286
Changes in the coverage of seagrass populations are considered to be a key indicator of the health and biodiversity of coastal ecosystems. The overall extent of seagrass meadows is declining worldwide, primarily due to human-induced disturbances. In Tampa Bay, Florida, a nearly 35% loss of seagrass coverage occurred from the 1950s to the 2000s. This decline was primarily due to the effects of human population growth. To examine closely the continuing declining trend of this major indicator of the health of coastal ecosystems, a systematic approach for extracting seagrass patches using EO-1 Hyperion hyperspectral imagery has been developed. In our previous work, a method based on Locally Excitatory Globally Inhibitory Oscillator Networks (LEGION) was developed and successfully applied to military object recognition using hyperspectral and multispectral imagery. It showed great potential in target detection of hyperspectral imagery. In this work, it is extend and applied in seagrass extraction. This study includes (a) dimensionality reduction of the hyperspectral data, (b) seagrass extraction using LEGION and four other methods, and (c) analysis and evaluation of the results in an experiment involving two test sites at Tampa Bay, Florida. The results demonstrated that the methodology has the potential to provide timely seagrass coverage information for coastal zone management at greatly increased efficiency. 相似文献
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亚热带人工针叶林冠层叶氮浓度的星载高光谱遥感能力评估(英文) 总被引：1，自引：0，他引：1
冠层叶片氮浓度(CNC)是影响森林生态系统生产力的重要参数之一。本研究探讨了星载成像光谱遥感在估测亚热带红壤丘陵区人工针叶林CNC的表现。分析包括了星载成像光谱数据(Hyperion影像)覆盖的两条样带上的57个野外样方,并将其分为三个子集(A-C)。利用一元回归和偏最小二乘回归方法分析了CNC与成像光谱信息之间的关系。在A-C子集中,CNC与近红外反射率(NIR)之间的相关性一致都呈现显著的正相关关系(R2=0.29,0.33和0.36,P0.05或P0.01)。另外,我们利用归一化的氮指数(NDNI)估计森林CNC的变异。在3个子集中,NDNI与CNC都呈现极显著的正相关关系,但相关性不高(R2=0.38,0.20和0.17,P0.01)。然后利用偏最小二乘方法分析了CNC与整个成像光谱数据(反射率、对数变换和一阶导数变换)之间的相关性,对于各个子集相关性不同且相对微弱。在分析已有数据和对比前人文献基础上,文章分析了影响成像光谱遥感森林CNC的可能原因,并指出研究区人工针叶林单一的冠层结构可能减弱了该地区森林CNC与成像光谱信息之间的相关关系。 相似文献
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作物LAI的遥感尺度效应与误差分析 总被引：7，自引：2，他引：5
以黑河中游盈科绿洲为研究区, 利用Hyperion高光谱数据, 采用双层冠层反射率模型(ACRM)迭代运算反演LAI; 通过LAI的均值化(LAImean)以及Hyperion数据反射率线性累加反演LAI(LAIp), 定量分析LAI反演的尺度效应; 从模型的非线性和地表景观结构的空间异质性2个方面分析引起反演误差的原因, 并在LAI-NDVI回归方程的基础上利用泰勒展开的方法对低分辨率数据反演结果进行了误差纠正。结果表明, 地表景观结构的空间异质性是造成多尺度LAI反演误差的关键因素, 通过泰勒展开式能很好地实现大尺度数据LAI反演结果的误差纠正。 相似文献
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EO-1 Hyperion高光谱数据的质量评价 总被引：4，自引：0，他引：4
本文以影像的客观评价方法对扬州地区一景Hyperion影像的L1R数据进行质量评价。主要采用辐射精度、信息量、清晰度、信噪比等指标对图像质量进行分析,经过评价认为,影像获取时受到成像环境和天气的影响,可用波段数量可能产生一定范围的浮动。本文的影像存在44个未定标波段,25个受水汽影响波段,而SWIR130以后的波段中存在大量噪声。影像信息主要集中于VNIR和SWIR波长较短范围的约94个波段内,该范围内影像质量较高,能发挥高光谱分辨率优势,并为正确还原光谱信息提供可能。影像数据中存在的条带现象和辐射畸变经过一定处理去除后,在农业调查、监测、管理,森林覆盖、灾害预警,地质调查,找油以及海洋水色研究等领域将有良好的应用前景。 相似文献