共查询到20条相似文献，搜索用时 140 毫秒
1.
将基于典型相关分析的正交变换的基础理论用于NOAA/AVHRR数据的多元变化检测 ,对具体实施步骤进行了深入的探讨 ,并对部分中间结果进行了分析。实验表明 ,该方法应用于NOAA/AVHRR数据的多元变化检测具有明显的优势 ,克服了传统方法存在的缺陷 ,具有良好的应用前景。 相似文献
2.
3.
中国西北地区NDVI变化及其与温度和降水的关系 总被引：59，自引：0，他引：59
稀疏的植被覆盖是干旱和半干旱地区最主要的环境特征,因此长期定量的植被分布和变化观测能够分析干旱和半干旱地区的环境变化。在以干旱和半干旱地区为主要的中国西北地区存在着森林减少、土地侵蚀、盐碱化和沙漠扩张等严重的环境问题,生态环境十分脆弱。通过NOAA/AVHRR建立近20年来中国西北地区NDVI变化序列,利用差分法、斜率变化和主成分分析3种方法分析植被变化。3种方法显示出基本一致的结果,即大部分地区植被状况恶化,局部地区有所好转。通过分析植被变化与温度、降水变化的关系,发现NDVI与降水存在明显的正相关关系,而与温度变化的关系并不明显,表明降水是影响西北地区植被变化最主要的自然因素。 相似文献
4.
5.
6.
极地海冰反照率直接影响极区的热收支,反照率的变化对地气系统热量收支平衡及气候变化等的研究具有重要意义。本文采用由美国国家海洋与大气管理局NOAA (National Oceanic and Atmospheric Administration)发射的NOAA卫星携带的先进的甚高分辨率辐射仪AVHRR (Advanced Very High Resolution Radiometer) Level-1B (L1B) 数据,经宽带反射率转换、各向异性校正、大气订正、云检测等处理,得到4 km宽带晴空地表反照率产品。将AVHRR反照率与北冰洋地表热收支SHEBA (Surface Heat Budget of the Arctic Ocean)实验数据进行印证,印证结果显示在冰雪冻结期二者平均偏差为-0.07,标准偏差为0.05。本文处理了2008年—2010年的AVHRR数据,结合第4次北极科学考察现场观测数据研究了北极冰面月平均反照率的变化,从降雪和冰脊两个方面分析了反照率的变化,结果显示反照率在冰雪融化过程中变化约为0.3,变化较大且较为迅速,表面粗糙的多年冰海域和较为平滑的一年冰海域的反照率在雪融化时期变化约为0.2且变化相对缓慢。研究结果表明,由冰雪融化引起的反照率变化较为快速且幅度较大,是引起北极反照率变化的主导因素。 相似文献
7.
《国土资源遥感》2021,(1)
有关春旱的时空特征信息对于许多农业应用和决策都至关重要。本研究利用NOAA/AVHRR的植被状态指数(vegetation condition index,VCI)产品,对1995—2010年间青藏地区的植被干旱情况进行了全面的时空分析。针对VCI作为干旱指标的特点,采用了多种方法,其中包括干旱的频率分析、趋势分析和Mann-Kendall实验。研究表明青藏地区受季风影响较小,横断山脉和祁连山地区干旱发生的频率比较低,且多为轻中旱。根据分析表明,该地区干旱的趋势并不是单向变化的,可以将其分为2个阶段:2000年以前VCI指数较高,且波动相对较大; 2000年之后VCI指数相对较低,且相对稳定。 相似文献
8.
9.
条件植被温度指数及其在干旱监测中的应用 总被引：93，自引：0，他引：93
应用NOAA－AVHRR数据，在用条件植被指数、条件温度指数和距平植被指数进行年度间相对干旱程度监测的基础上，提出了条件植被温度指数的概念，它适用于监测某一特定年内某一时期（如旬）区域级的相对干旱程度。条件植被温度指数的定义既考虑了区域内归一化植被指数的变化，又考虑了在归一化植被指数值相同条件下土地表面温度的变化。陕西省关中平原地区2000年3月下旬干旱的监测结果表明，条件植被温度指数能较好地监测该区域的相对干旱程度，并可用于研究干旱程度的空间变化特征，对干旱的监测结果与用土壤热惯量模型反演的土壤表层含水量的结果基本吻合。 相似文献
10.
11.
Mixed pixel is a key issue in medium to coarse resolution remote sensing image, and it seriously restricts the remote sensing classification. This paper presents an Independent component analysis (ICA) algorithm based on the variational Bayesian (VB) methods, named VBICA, for spectral unmixing in multispectral remote sensing image. The model assumes that the mixed pixels to be separated are given as linear mixtures. The matrixes of linear mixtures are assumed to be unknown. In the Bayesian framework, the endmember and abundance have finally been achieved with Bayesian inference and approximate variational algorithm. The proposed method is evaluated and tested on a numerical simulative image from the noise resistance, area size, pixel purity, estimated number of endmembers and real multispectral remote sensing image of 100?×?100 pixels. Experimental results on simulated image demonstrated that compared to the Fast ICA algorithm, the proposed algorithm can give more accurate results, and the validity of the proposed algorithm is verified by the real multispectral remote sensing image of the similarity on spectral curves, average similarity and ground objects distribution maps. 相似文献
12.
本文提出了一种新的结合多光谱和变化检测技术的多时相卫星数据集分类方法。该方法以数理统计中的最近邻法为基础,其目标函数是使得正确分类的平均概率得到最优化,即把每个分类类别看成同等重要。该新算法被应用于一个农业作物分类的研究区域,并利用覆盖该区的不同季节的SPOT和LANDSAT TM多时像影像。结果表明,与单时像影像相比,使用五个不同季节的多时像影像可以充分地提高分类精度。为了说明该方法在大尺度范围内的效果,本文选取瑞典道拉河流域作为研究区。由于不同地物的分布高度重叠,不可能得到像元水平上满意的分类精度。这就需要引进一种新的概念：像元概率分类法。基于像元的概率向量可用于判别传统分类法的可靠性并测量单个像元的不确定性（熵）。概率分类法同时提供了不同地物的面积的无偏估计,无论所感兴趣的区域的大小。这已经在不同特性的耕地试验点进行了检验。 相似文献
13.
为了提高多光谱影像变化检测的精度,本文提出了一种结合空间上下文与慢特征分析的方法。首先采用自适应空间上下文提取算法围绕像素构建自适应区域,探索像素周围的上下文信息;然后通过迭代慢特征分析,由相应像素周围的成对自适应区域定量计算成对像素之间的变化强度,增强变化区域与未变区域的可分性;最后生成变化强度图像,采用大津阈值法作二值分类,将变化强度图划分为二值变化检测图。利用Landsat 7卫星ETM+传感器的图像,与4种基于代数的方法及基于变换的方法进行对比试验,结果表明,本文方法在降低漏检方面有所改善,提高了召回率。 相似文献
14.
15.
16.
《地理信息系统科学与遥感》2013,50(3):330-342
This study evaluates the performance of an artificial neural network, specifically a multilayer perceptron, and a maximum likelihood algorithm to classify multitemporal Landsat ETM+ remote sensor data. The study area in Turkey is a mountainous region that contains many small scattered fields, usually 5-10 pixels in size. The classifiers were employed to identify eight land cover/use features covering the bulk of the study area using the same training and test datasets in order to avoid any difference resulting from sampling variations. Results show that the neural network approach performed better in extracting land cover information from multispectral and multitemporal images with training data sets including a large amount of mixed and atypical pixels. The maximum likelihood classifier was found to be ineffective, particularly in classifying spectrally similar categories and classes having subclasses. 相似文献
17.
R. S. Ayyangar P. M. Sarma P. P. Nageswara Rao S. S. Budwal 《Journal of the Indian Society of Remote Sensing》1982,10(1):35-44
The size and reliability of the training sets or sample area for the classification of airborne multispectral scanner data obtained over an agricultural area with the help of an interactive computer system have been examined in this study. The experiment reported herein suggests that a training set of not less than 50 pixels would adequately represent all the likely variations in any particular field. The evaluation of the results further reveals that if the training sets can adequately represent the field variations characteristic of the region, the corresponding training statistics can be utilized both on scanline and pixel directions. 相似文献
18.
The present study proposes land surface temperature (LST) retrieval from satellite-based thermal IR data by single channel radiative transfer algorithm using atmospheric correction parameters derived from satellite-based and in-situ data and land surface emissivity (LSE) derived by a hybrid LSE model. For example, atmospheric transmittance (τ) was derived from Terra MODIS spectral radiance in atmospheric window and absorption bands, whereas the atmospheric path radiance and sky radiance were estimated using satellite- and ground-based in-situ solar radiation, geographic location and observation conditions. The hybrid LSE model which is coupled with ground-based emissivity measurements is more versatile than the previous LSE models and yields improved emissivity values by knowledge-based approach. It uses NDVI-based and NDVI Threshold method (NDVITHM) based algorithms and field-measured emissivity values. The model is applicable for dense vegetation cover, mixed vegetation cover, bare earth including coal mining related land surface classes. The study was conducted in a coalfield of India badly affected by coal fire for decades. In a coal fire affected coalfield, LST would provide precise temperature difference between thermally anomalous coal fire pixels and background pixels to facilitate coal fire detection and monitoring. The derived LST products of the present study were compared with radiant temperature images across some of the prominent coal fire locations in the study area by graphical means and by some standard mathematical dispersion coefficients such as coefficient of variation, coefficient of quartile deviation, coefficient of quartile deviation for 3rd quartile vs. maximum temperature, coefficient of mean deviation (about median) indicating significant increase in the temperature difference among the pixels. The average temperature slope between adjacent pixels, which increases the potential of coal fire pixel detection from background pixels, is significantly larger in the derived LST products than the corresponding radiant temperature images. 相似文献
19.
自动形态学端元提取（automated morphological endmember extraction，AMEE）算法将结构元素内最纯像元与混合度最大的像元之间的光谱角距离定义为形态学离心率指数（morphological eccentricity index，MEI）来定量化地表示像元的纯净度。然而作为参考标准的混合度最大的像元在不同的结构元素内也是不同的，尤其是当结构元素内的纯净像元占大多数时，像元的均值光谱将更接近纯像元，此时像元的MEI越高，纯度反而越低。针对这一问题，本文提出一种像元纯度指数（pure pixel index，PPI）算法与AMEE算法相结合的端元提取算法PPI-AMEE。在结构元素内，利用PPI指数代替AMEE算法中的MEI指数来寻找最纯像元。变换结构元素时，只有最纯净的像元始终能够投影到随机生成的直线的两端，其PPI值会不断累计增大，而其他像元的PPI值则无法持续增大。累计记录每个像元的PPI值，直至满足迭代终止条件，最终形成一幅PPI图像，端元将在PPI值较大的像元中选取。PPI-AMEE算法只在相对较小的结构元素内运行PPI算法，然后再结合数学形态学中的膨胀操作对整幅图像进行处理，其同时兼顾了图像的光谱信息和空间信息。最后，采用模拟数据及美国内华达州Cuprite地区的机载可见光/红外成像光谱仪（airborne visible infrared imaging spectrometer，AVIRIS）高光谱数据对提出的PPI-AMEE算法进行试验验证。试验结果表明，PPI-AMEE算法的端元提取精度总体上优于AMEE算法和PPI算法。 相似文献
20.
Land use classification is one of the major problems in remote sensing. Previous studies focused on multispectral information, texture-based features, and features based on edge detection to classify land usage from satellite images. In a previous study, structural features are introduced to classify land development using high-resolution satellite images. These structural features were based on line support regions (LSRs). LSRs are introduced to detect and represent straight lines in images using a pixel-grouping process. The structural features are calculated on these grouped pixels. It is shown that gradient-magnitude-based pixel grouping may also be used in structural feature calculations. Therefore, the aim of this letter is twofold. First, the previous structural feature calculation method is shown to be more general than the LSR. Second, LSR-based features are shown to require fairly high computation compared to gradient-magnitude-based features with similar classification performance 相似文献