排序方式: 共有47条查询结果,搜索用时 15 毫秒
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以2006年泉州市部分QuickBird多光谱影像与全色影像为实验区数据基础,在遥感软件ERDASIMAGINE9.2平台支持下,应用小波融合算法及融合规则实现QuickBird融合处理,在此基础上进行归一化植被指数(NDVI)运算,通过人机交互式提取得到实验区5种主要的绿地类型:公园绿地、生产绿地、防护绿地、附属绿地及其他绿地.结果表明:1)QuickBird多光谱影像、全色影像的信息熵分别为3.975,4.162,而QuickBird融合影像的信息熵为7.251,明显高于前两者,融合后空间信息更丰富;2)QuickBird融合影像的平均梯度是3.328,多光谱影像、全色影像分别是1.552,2.965,融合影像纹理特征更明显;3)QuickBird融合影像与多光谱影像的偏差是0.0359,全色影像的偏差为0.0562,均接近于0,小波融合影像较好地保持了源图像的光谱特性;4)绿地信息分类提取的精度为89.6%,趋近90%. 相似文献
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Timely and accurate information on crop conditions obtained during the growing season is of vital importance for crop management.
High spatial resolution satellite imagery has the potential for mapping crop growth variability and identifying problem areas
within fields. The objectives of this study were to use QuickBird satellite imagery for mapping plant growth and yield patterns
within grain sorghum fields as compared with airborne multispectral image data. A QuickBird 2.8-m four-band image covering
a cropping area in south Texas, USA was acquired in the 2003 growing season. Airborne three-band imagery with submeter resolution
was also collected from two grain sorghum fields within the satellite scene. Yield monitor data collected from the two fields
were resampled to match the resolutions of the airborne imagery and the satellite imagery. The airborne imagery was related
to yield at original submeter, 2.8 and 8.4 m resolutions and the QuickBird imagery was related to yield at 2.8 and 8.4 m resolutions.
The extracted QuickBird images for the two fields were then classified into multiple zones using unsupervised classification
and mean yields among the zones were compared. Results showed that grain yield was significantly related to both types of
image data and that the QuickBird imagery had similar correlations with grain yield as compared with the airborne imagery
at the 2.8 and 8.4 m resolutions. Moreover, the unsupervised classification maps effectively differentiated grain production
levels among the zones. These results indicate that high spatial resolution satellite imagery can be a useful data source
for determining plant growth and yield patterns for within-field crop management. 相似文献
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星基差分GPS用于林区高分辨率遥感图像几何精校正 总被引:6,自引:0,他引:6
为解决林区缺乏大比例尺地形图而难以进行高空间分辨率遥感图像几何精校正问题,采用基于广域差分技术的星基差分GPS精确测定地面控制点(GCP),以多项式变换进行QuickBird pan(0.61 m)图像几何精校正.结果表明,全部GCP的点位误差均小于1个像元,总的点位误差为0.411 9.在进行图像灰度重采样中,最邻近点法、双线性内插法和三次褶积法的效果均无明显差异;就信息量和灰度值变动情况而言,以三次褶积法略好;就图像灰度保真而言,以最邻近点法略好. 相似文献
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基于分量替换的高分辨率遥感图像融合是一种十分重要的融合方法,但对该方法的融合原理的深入分析在国内还鲜有报道。为此对分量替换融合方法进行原理探究和实验对比分析。首先从线性代数的角度来阐述分量替换融合算法的实质,并对两种典型的构造可替换波段的分量替换方法(基于光谱响应函数模拟低分辨率全色波段的Gram-Schmidt融合法(GS1)和基于多元一次线性回归拟合低分辨率全色波段的Gram-Schmidt(GS2)融合法进行原理说明;其次,选取QuickBird全色与多光谱图像数据,进行三种有代表性的基于分量替换的融合方法(PCA,GS1,GS2)的对比分析。通过对比融合前后典型地物的光谱特性变化来评价融合影像的光谱保真性也是该研究的一大特色。结果表明:3种融合方法都具有很高的光谱保真性,GS2融合方法具有最优的光谱保真性,PCA和GS1融合算法次之,尤其是GS1融合处理后的图像存在部分光谱失真的现象,GS2算法的光谱保真性明显优于GS1。从基于分量替换融合方法的实质可以诠释出造成GS1融合图像光谱失真的根本原因,GS1融合算法使用的只是实验室理想环境下所获取的名义上的光谱响应特征。传感器的实际成像过程受到诸多因素的影响,如传感器在轨工作环境、大气、观测角度不同等的影响。GS1算法单纯通过不同波段光谱响应函数的线性组合来模拟低分辨率全色波段并不十分准确,GS2直接利用MS和Pan波段像元灰度值进行线性回归,克服了上述不确定性问题。通过以上对比研究发现,如何利用多光谱数据准确地模拟低分辨率全色波段,直接影响到融合后影像的光谱保真性,是目前高分辨率遥感图像融合的关键技术。 相似文献
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遥感信息普遍存在着尺度效应,合适的空间分辨率可以反映特定目标的空间结构特性。基于地理学第一规律,选择了目前主要采用的2种方法——局部变异和变异函数对最优尺度的选择进行研究,并针对传统方法的局限性提出了改进方案。通过同一地区的遥感卫星Landsat 7,Spot-5/HRG和QuickBird遥感影像,对不同的景观区域采用不同的方法进行了比较研究。根据实验,得出了局部变异适合微观、变异函数则更适用于宏观问题的结论,并得到了不同数据源在不同景观类型下的最优尺度。最后,根据最优尺度选择的结果,讨论了不同数据源的适用性。图4表4参19 相似文献
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Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies 总被引:1,自引:0,他引:1
Leaf area index (LAI) is a key biophysical variable that can be used to derive agronomic information for field management and yield prediction. In the context of applying broadband and high spatial resolution satellite sensor data to agricultural applications at the field scale, an improved method was developed to evaluate commonly used broadband vegetation indices (VIs) for the estimation of LAI with VI–LAI relationships. The evaluation was based on direct measurement of corn and potato canopies and on QuickBird multispectral images acquired in three growing seasons. The selected VIs were correlated strongly with LAI but with different efficiencies for LAI estimation as a result of the differences in the stabilities, the sensitivities, and the dynamic ranges. Analysis of error propagation showed that LAI noise inherent in each VI–LAI function generally increased with increasing LAI and the efficiency of most VIs was low at high LAI levels. Among selected VIs, the modified soil-adjusted vegetation index (MSAVI) was the best LAI estimator with the largest dynamic range and the highest sensitivity and overall efficiency for both crops. QuickBird image-estimated LAI with MSAVI–LAI relationships agreed well with ground-measured LAI with the root-mean-square-error of 0.63 and 0.79 for corn and potato canopies, respectively. LAI estimated from the high spatial resolution pixel data exhibited spatial variability similar to the ground plot measurements. For field scale agricultural applications, MSAVI–LAI relationships are easy-to-apply and reasonably accurate for estimating LAI. 相似文献
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近年来,艾比湖湖面发生严重萎缩,造成湖泊大面积裸露,为沙尘暴提供了大面积沙源。通过对高分辨率QuickBird影像数据的处理,分析了艾比湖湖域的风蚀现状。在湖岸周边强度以上的风蚀面积约占63%,主要分布在无植被覆盖的东部和西部;微度风蚀和中度风蚀面积约占37%,主要分布在有植被覆盖的南北两岸。湖泊裸露的干涸湖底,位于湖泊的西北部,面积达430km2。影像的色调、形状和纹理变化上反映了干涸湖底的地貌形态和风系的方向。结合实地测量数据的分析发现,湖泊西北向东南方向形成宽约4.5km,长约10km,坡度为1/5000的带状坡面,为沙尘暴的发生创造了有利的条件。要改善艾比湖流域生态环境,主要依靠增加入湖水量以减小干涸湖底的沙源,达到控制艾比湖地区风蚀的目的。 相似文献
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基于纹理特征和SVM的QuickBird影像苹果园提取 总被引:3,自引:0,他引:3
为提高高空间分辨率遥感影像(高分影像)中苹果园提取精度,基于Quick Bird遥感数据,研究综合光谱特征和纹理特征的苹果园自动提取方法。该方法首先采用最佳指数因子(OIF)获取多光谱波段最佳组合,然后采用不同大小滑动窗口(从3像素×3像素到13像素×13像素)提取全色波段的灰度共生矩阵(GLCM)、分形和空间自相关3种纹理特征并分别与光谱特征组合,最后通过支持向量机(SVM)分类进行苹果园分类识别。研究表明:在分类特征上,与单一光谱或纹理特征相比,光谱特征结合纹理特征能有效提高苹果园提取精度(Fa)和总体分类精度(OA),其中光谱+GLCM纹理(9像素×9像素)分类精度最高,Fa和OA分别为96.99%和96.16%,比光谱+分形纹理分别提高0.63个百分点和1.56个百分点,比光谱+空间自相关纹理显著提高11.92个百分点和9.20个百分点。在分类方法上,通过对比分析SVM、最大似然和神经网络3种方法的分类结果,探明SVM分类识别苹果园精度最高。最后对苹果园提取结果进行面积统计,结果表明GLCM纹理结合SVM分类的苹果园面积估算与目视解译结果的一致性超过98%。 相似文献