共查询到20条相似文献，搜索用时 140 毫秒
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全球变化研究需要将多种传感器光谱波长接近的图像波段一起使用,以满足遥感应用对时间分辨率和区域覆盖的要求,这给遥感图像处理提出了新要求,涉及多传感器多时相数据几何定位一致性问题、辐射归一化问题及地类属性标识一致性问题以及高度自动化处理的问题。针对上述几方面问题,提出了一个基于"不变特征点集"IFPs(Invariant Feature Points set)作为控制数据集的区域级遥感图像自动化处理框架,将图像的几何空间、辐射值空间和类别属性值空间的时空对齐问题纳入到统一框架,提供了一种间接快速处理的手段和理念,并对构建IFPs的关键技术进行了综述。 相似文献
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随着遥感图像分辨率的日益提高,遥感图像的尺寸和数据量也不断地增大,同时随着遥感应用的发展,对图像配准的性能也提出越来越高的要求,基于此,提出一种特征级高分辨率遥感图像快速自动配准方法。首先,对图像进行Haar小波变换,基于小波变换后的近似图像进行配准以提高配准速度;其次,根据不同的遥感图像来源使用不同的特征提取方法(光学图像使用Canny边缘提取算子,SAR图像使用Ratio Of Averages算子),并将线特征转化为点特征;然后,依据特征点间最小角与次小角的角度之比小于某一阈值来确定初始匹配点对;最后,利用改进的随机抽样一致性算法滤除错误匹配点对,并结合分块思想均匀选取匹配点对计算仿射变换参数,进一步提高配准精度。为了验证本文方法的有效性,选择高分辨率World View-2图像、Pleiades图像和Terra SAR图像进行了实验,并与典型的SIFT算法、SURF算法进行比较分析,采用匹配率、匹配效率、均方根误差和时间消耗4个定量评价指标来客观评价算法的配准性能。实验结果表明,本文方法具有较好的有效性,且在不同的情况下具有较高的配准精度。本文提出的特征级高分辨率遥感图像快速自动配准方法,多组高分辨率遥感图像数据的配准实验结果表明该方法能快速实现并具有较高的配准精度和较好的鲁棒性。 相似文献
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本文从SAR图像判读的角度考虑，综述了SAR的工作特点、SAR图像的色调特征和几何特征及它们的形成原目。其中色调特征方面分析了SAR的系统参敷(波长、极化和天线俯角)和地面参数(目标表面有效粗糙度和复介电常数)对图像色调造成的影响，同时分析了图像斑点、虚假目标和其它异常现象对图像判读产生的不利影响．几何特征方面分析了图像比例尺变化、透视收缩、顶底位移、阴影和叠掩对目标影像造成的几何畸变特征和原因，其中对透视收缩的理论分析结论更具客观性。 相似文献
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实例分析遥感图像处理中的主成分分析 总被引：3，自引：0，他引：3
欧春江 《测绘与空间地理信息》2006,29(5):56-59
主成份分析是建立在统计特征基础上的多维(如多波段)正交线性变换。它是遥感图像处理中最常用也是最有用的变换算法之一。本文研究了主成份分析的原理、几何解释与计算过程,并用遥感影像和数据加以说明。 相似文献
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针对快速鲁棒特征SURF描述符匹配精度不高且对光照变化不具有鲁棒性的问题,提出利用亮度排序的快速鲁棒特征描述与匹配算法。该方法在SURF算法的基础上,对特征邻域像素的灰度值进行排序和分段。通过建立索引表对每段的像素进行表示形成描述子,再将每段的描述子串联形成特征描述符对影像进行匹配。实验表明,该算法较SURF算法匹配精度高,匹配可靠性方面提高74.7%,且对线性及非线性光照变化均具有较好的鲁棒性。 相似文献
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基于多源遥感影像融合的影像匹配技术 总被引：6，自引：4，他引：2
多源遥感影像数据配准,常用的方法是多项式纠正法。此方法简单,但不能有效实现图像之间的相互配准。本文介绍了一种基于影像匹配的图像对图像局部纠正技术,包括影像直方图匹配、特征点提取、影像匹配处理技术,以及图像对图像局部纠正等技术,用于多源数据的配准。实验证明这种流程适合不同时相、不同传感器遥感数据(TM数据、SPOT数据与航空影像数据)的精确配准,配准误差可达到子像元级别。 相似文献
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在遥感图像检索中,光谱特征的应用最为广泛。本文研究了基于光谱特征进行遥感图像检索的方法。针对目前应用越来越广泛的多光谱、高光谱遥感图像波段多的特点,提出了基于K-L变换的检索方法,将多维图像降维处理,在此基础上提取遥感图像的光谱特征,通过检索图像与目标图像的光谱特征对比实现多光谱遥感图像的检索,并通过实验验证了本文方法的有效性。 相似文献
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A band selection technique for spectral classification 总被引：2，自引：0，他引：2
De Backer S. Kempeneers P. Debruyn W. Scheunders P. 《Geoscience and Remote Sensing Letters, IEEE》2005,2(3):319-323
In hyperspectral remote sensing, sensors acquire reflectance values at many different wavelength bands, to cover a complete spectral interval. These measurements are strongly correlated, and no new information might be added when increasing the spectral resolution. Moreover, the higher number of spectral bands increases the complexity of a classification task. Therefore, feature reduction is a crucial step. An alternative would be to choose the required sensor bands settings a priori. In this letter, we introduce a statistical procedure to provide band settings for a specific classification task. The proposed procedure selects wavelength band settings which optimize the separation between the different spectral classes. The method is applicable as a band reduction technique, but it can as well serve the purpose of data interpretation or be an aid in sensor design. Results on a vegetation classification task show an improvement in classification performance over feature selection and other band selection techniques. 相似文献
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This paper discusses a statistical and band transformation based approach to select bands for hyperspectral image analysis. Hyperspectral images contain large number of spectral bands with redundant information about the spectral classes in the image scene. It is necessary to reduce the high dimensionality of the data for the processing of hyperspectral data. We report a feature selection technique that removes correlated spectral bands using band decorrelation technique and obtains maximum variance image bands based on factor analysis. Factor analysis method of band selection technique is also validated against existing methods of band selection. The study is carried out for the agriculturally rich area of Musiri region of South India that has varied landcover types. Evaluation of the band selection procedure is done using signature separability measures such as Euclidean distance, Divergence, Transformed divergence and Jeffries Matusita distance. Results indicated that selected bands exhibited maximum separability and also occurred predominantly at wavelength 700 nm, 850, 1000 nm, 1200 nm, 1648 nm and 2200 nm. 相似文献
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基于决策树的CBERS遥感影像分类及分析评价 总被引：1，自引：0，他引：1
以江苏省徐州市为研究区,以城市土地利用遥感分类为目标,采用CBERS多光谱数据的近红外波段、全球环境监测植被指数(GEMI)、归一化植被指数(NDVI)及主成分分析得出的第一和第二主成分作为分类的特征数据,基于先验知识和统计分析构建层次分类决策树,进而发展和改进了决策树交互式构建算法,实现了城市土地利用遥感分类。通过与最大似然分类器(MLC)和支持向量机分类器(SVM)分类结果的比较分析,表明基于多种特征的决策树分类器能够有效应用于CBERS遥感数据分类,在研究区具有良好的推广性。 相似文献
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Crisp and Fuzzy Adaptive Spectral Predictions for Lossless and Near-Lossless Compression of Hyperspectral Imagery 总被引：3，自引：0，他引：3
Aiazzi B. Alparone L. Baronti S. Lastri C. 《Geoscience and Remote Sensing Letters, IEEE》2007,4(4):532-536
This letter presents an original approach that exploits classified spectral prediction for lossless/near-lossless hyperspectral-image compression. Minimum-mean-square-error spectral predictors are calculated, one for each small spatial block of each band, and are classified (clustered) to yield a user-defined number of prototype predictors that are capable of matching the spectral features of different classes of pixel spectra for each wavelength. Such predictors are used to achieve a prediction, either crisp or fuzzy. Unlike most of the methods reported in the literature, the proposed approach exploits a purely spectral prediction that is suitable in compressing the data in band-interleaved-by-line format, as they are available at the output of the onboard instrument. In that case, the training phase, i.e., clustering and refining of predictors for each wavelength, may be moved offline. Experimental results on Airborne Visible InfraRed Imaging Spectrometer data show improvements over the most advanced methods in the literature, with a computational complexity that is far lower than that of analogous methods by the same and other authors. 相似文献
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TM 图像的信息量分析及特征信息提取的研究 总被引：1，自引：0，他引：1
图像信息量分析是图像处理的基础,为此,本文研究了三个不同植被覆盖类型区,即多林区(森林覆盖在40％以上)、一般林地分布的丘陵区(森林覆盖10-30％)和农田为主的丘陵与平原区的图像信息量。分析同一地区冬夏两季的图像信息特征后得知,红外波段的信息量高于可见光波段,其中信息量最大的是TM5波段,最小的是TM2波段。同时对不同情况下波段间的相关性、均值和标准差等统计特征值也进行了分析。据此就图像增强、信息特征提取方法,如主成分分析、缨帽变换(KT变换)、比值等方法以及波段组合等进行了系统研究,并就其实用条件进行了探讨和评价。 相似文献
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Hyperspectral remote sensing technique is widely applied for geological studies including the study of extra-terrestrial rocks.
Since it has many spectral bands, discrimination between rocks and minerals can be done more precisely. To perform chemical
and mineralogical mapping and to study the rocks on the lunar surface, India has proposed to launch its first lunar remote
sensing satellite Chandrayaan-1 in the year 2008. For mineralogical mapping, the mission will carry a Hyperspectral Imager
(HySI) instrument, which operates in the VNIR region. This paper presents-an attempt to study the spectral response of lunar-akin
terrestrial rocks, in the VNIR region (as in the case of the proposed HySI on-board Chandrayaan-1). For this purpose, rocks
similar to those present on the lunar surface were collected and their spectral response in the 64 simulated bands of HySI
sensor were studied using a spectro-radiometer. Petrographic studies and modal analysis were carried out using thin sections
of the rock samples. On studying the spectral response of the lunar-like rock samples in the 64 HySI bands, it is seen that
there are distinct absorption features in bands 58 (923.75nm-927.5nm) and 63 (942.5nm-946.25nm) of the NIR wavelength ranges,
for basalt rocks; distinct reflectance features in band 20 (590nm to 600nm) for ganmbbro: distinct reflectance features in
band 19 (580nm to 590nm) and absorption in band 18 (570-580nm) for gabbroic anorthosite and distinct reflection features in
band 63 (942.5nm to 946.25nm) for anorthosite. Thus, this study demonstrates the possibility of identifying the minerals and
rocks on lunar surface using the hyperspectral approach and the spectral signatures of lunar-like rocks present on Earth. 相似文献
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S. Rajendran A. Thirunavukkaraasu B. Poovalingaganesh K. Vinod Kumar G. Bhaskaran 《Journal of the Indian Society of Remote Sensing》2007,35(2):153-162
The remote sensing community in geology is widely using the Multispectral Landsat Thematic Mapper (TM) data which has a wider
choice of spectral bands (six between 0.45 and 2.35 μm, plus a thermal infrared channel 10.4-12.5 urn). These were evaluated for low-grade magnetite ores mapping over the high-grade
granulite region of Kanjamalai area of Tamil Nadu state, India. The Fourier Transform Infrared (FTIR) spectroscopy data (0.4-4.0
μm) for powders of the magnetite ores exposed with granulite rock and published spectral reflectance data were used as guides
in selecting TM band reflectance ratios, which maximize discrimination of magnetite ores on the basis of their respective
mineralogies. The study shows that the weathering mineralogy of magnetite ores causes absorption features in their reflectance
spectra which are particularly characteristic of the near infrared. Comparison of TM data with field and petrographic observations
shows the presence of magnetite and aluminosilicate minerals & show strong absorption at 0.7-1 μ.m wavelength spectral region
& increase in the product of two TM band ratios: band 5 (1.55-1.75 μm) to band 4 (0.76-0.9 μm) and band 3 (0.63-0.69 μm) to
band 4 (0.76-0.9 μm). Various computer image enhancement and data extraction techniques such as interactive digital image
classification techniques using color compositing stretched ratio, maximum likelihood and thresholding statistical approaches
using Landsat TM data are used to map the low-grade magnetite ores of the granulite region. The field traverses and local
verification enhanced to map the other rock types namely granulites and gneisses of the study area. 相似文献
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Spectral reflectance Studies have been carried out on various features (man-made as well as natural) with reference to urban environment using a portable spectro-radiometer in wavelength regions ranging from 0.45 to 1.0 μm of Kanpur city and its surrounding areas. The signature values, thus collected, were used to draw spectral reflectance curves of each feature separately and to determine/select the optimum wavelength regions suitable for urban area studies. It has been observed that the best band width suitable for urban feature discrimination are between 0.45 to 0.55 μm (blue region) & 0.69 to 0.80 μm (near-infrared region) of the spectrum. 相似文献