共查询到18条相似文献，搜索用时 109 毫秒
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多光谱数据的最佳波段选择直接影响图像的目视解译和信息提取。在分析TM影像各波段间的标准差、相关系数和最佳指数因子内在联系的基础上,提出采用最佳指数因子与蚀变信息光谱特征相结合的方法选择遥感影像的最佳波段组合。研究表明,最佳指数因子与蚀变信息光谱特征相结合是多光谱数据最佳波段选择的理想方法;TM4＋TM5＋TM7波段组合获取的合成图像构造清晰,岩性差异显著,最有利于蚀变信息的提取。 相似文献
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利用灰度共生矩阵的多光谱影像纹理分析的方法,实现了利用k-mean算法对Aster影像数据进行分类,并对基于单波段影像纹理的分类结果、单纯光谱信息分类结果和结合基于主要波段影像纹理的分析方法及在分类过程中结合掩膜等技术所得纹理特征影像分类结果进行比较。 相似文献
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蚀变矿物虽然在光谱上具有诊断特征,但是和其它地物目标相比属于一种弱信息,需要进行数据挖掘来展现隐含的信息。多年来遥感工作者不断探索蚀变矿物识别的方法,在多光谱中已成功应用比值法、主成分分析法等。但是比值法常针对的是单波段运算,忽略了蚀变矿物的组合光谱特征。本文针对这个问题,在新疆包古图斑岩铜矿Ⅱ号和Ⅴ号矿体中利用Aster数据,探讨其典型蚀变矿物在Aster光谱中的响应特征,利用由比值法原理衍生出的相关吸收波段深度法（RBD）和逻辑算法进行蚀变分带识别研究。结果表明,由于逻辑算法考虑了蚀变矿物组合的整体多个光谱特征, 其识别效果比基于单个光谱特征的RBD法减少了假异常,提高了识别精度。 相似文献
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ASTER数据的自组织神经网络分类研究 总被引：8，自引：0，他引：8
传统的遥感数据分类方法大多基于统计学的参数估计，假设数据分布服从高斯正态分布。神经网络方法无需参数估计和统计假设，因而，近来越来越多地应用于遥感数据分类之中。介绍了基于聚类分析的自组织特征映射分类方法。ASTER卫星数据是新型遥感数据，包括 3个15 m分辨率波段和 3个30 m分辨率的短波红外波段。选择北京地区的ASTER数据作为方法实验数据，首先对数据进行了小波融合，然后进行了土地覆盖类型的自组织特征映射神经网络分类研究，把研究结果同最大似然判别法得到的分类结果进行了比较，分类精度比最大似然判别法总体提高了9％。 相似文献
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基于ASTER数据遥感影像的决策树分类 总被引：6，自引：0，他引：6
以黑龙江省北安市为研究区域,尝试利用ASTER视反射率值进行便利、准确的土地利用分类研究。对ASTER数据进行波段相关分析,确定最佳组合波段;然后重点分析转换为视反射率值的影像特征和光谱特征,从中提取各种典型地物的光谱曲线; 并依据提取的光谱曲线建立基于地物反射率值大小关系或阈值的决策树模型,对研究区不同地物类型进行分类,并对结果进行精度评价。应用效果表明,该方法简单有效,但对于混合光谱容易错分。 相似文献
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利用World View-Ⅱ遥感影像高空间分辨率和高波谱分辨率的数据优点,结合Aster数据波段互补的优势,进行南极大陆边缘拉斯曼丘陵Fe信息提取研究。研究结果表明,使用World View-Ⅱ与Aster数据对比分析的主成分法,提取Fe3+信息,采用World View-Ⅱ与Aster协同数据下的波段运算法,提取Fe2+信息,可以达到优势互补的目的。Fe信息提取结果显示,含铁矿物信息分布区主要集中在混合片麻岩与副片麻岩区域,分别占Fe信息总面积的80.39%和15.73%,与野外调查结果基本一致。 相似文献
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岩土分类与一般地表的地物分类相比难度大得多,针对已有的分类方法（监督分类和非监督分类）对于岩土分类精度不高、分类效果欠佳问题提出一种基于多特征波段岩土层次分类方法。它是一种自顶向下、逐步求精的层次分类方法,该方法结合无监督分类和监督分类两种分类方法的优势,利用多个特征波段组合,有层次地将不同类型的岩土体逐步分开,实现对岩土的精确分类。对北京市怀柔山区附近的ASTER影像数据进行的岩土分类实验结果表明,基于多特征波段岩土层次分类识别方法能显著提高岩土分类精度,总体精度提高10%,Kappa系数提高了0.1,并且能识别以往分类识别方法难以区分的岩石阴影和水体等地物,能够有效地克服“同物异谱”现象。 相似文献
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用ASETR图像和地统计学纹理进行岩性分类 总被引：9，自引：0，他引：9
运用新获取的ASTER数据可以对岩性进行识别与分类：首先运用地统计学中的变差函数来计算分析几种选定的岩性单元的灰度值空间变化特征；运用ASTER数据的可见光一近红外波段、短波红外(SWIR)波段以及二者的组合进行岩性的分类，分析分类精度的变化。用变差函数作为纹理的计算函数来提取图像纹理，并与原始的光谱数据结合，进行岩性的分类。结果表明，与单纯的光谱分类相比，加入纹理信息可显著改善分类精度；用不同方向的滞后距离提取的图像纹理对图像的分类结果有一定的差异，尤其是对存在明显的各向异性的岩石单元。 相似文献
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通过对闽江口水下三角洲的遥感影像预处理,利用波段标准差、波段间相关性、最佳指数公式等定量方法和光谱分析法,得出TM遥感数据在闽江口水下三角洲调查中最佳波段组合为TM432,同时表明仅仅利用定量的统计参数并不能准确确定最佳波段组合,还需要结合研究对象的光谱特征。 相似文献
10.
四种卫星遥感数据源的黄土滑坡灾害解译效果对比研究 总被引：1，自引：0，他引：1
卫星遥感技术已成为研究地质灾害最有效的方法之一。针对大量的卫星遥感数据源,如何根据研究区特点和研究的目的,选择满足精度要求且经济合理的遥感数据源是值得研究的问题。本文以延安宝塔区为试验区,收集ETM＋、Spot 5、CBERS-02、QuickBird数据,采用相同的波段组合及融合方法进行滑坡灾害解译效果对比研究,定量分析最小可识别图斑和最佳成图比例尺,依据对比结果提出滑坡灾害区划卫星遥感数据的选择原则,为定性、定量区划研究提供依据。 相似文献
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Adel Zein Bishta 《Arabian Journal of Geosciences》2018,11(23):755
Multispectral, multiresolution remotely sensed data were processed to emphasize geological interpretation of Jabal Daf-Wadi Fatima area. The investigated area is situated in the central western part of Saudi Arabia and geologically consists of igneous and metamorphosed rocks overlain by sedimentary sequence belonging to the Arabian-Nubian Shield. Three sets of digital satellite data, Landsat-7 ETM+, ASTER, and SPOT-5, were used in this study. The application of image processing techniques enables to identify and delineate the lithologic units and the structural features of the study area. The results of this study indicate that the confusion matrix of the three maximum likelihood supervised classifications of the three datasets shows that the Landsat ETM+ bands scored the best degree of average and overall accuracy (77 and 78%, respectively). This classification distinguishes most of the rock units for mapping in the investigated area. The supervised classification of ASTER and SPOT bands has lower degrees of accuracy than the classified Landsat data. The supervised classification of SPOT bands has a degree of average and overall accuracy of 66 and 67%, respectively, but it is the best for distinguishing the spectral signatures of the different members of Fatima Formation (lower, middle, and upper members). The statistical analyses of the confusion matrices of classifications and the interpretation of the produced classified thematic maps revealed that the classification accuracy does not necessary depend on the spatial resolution of satellite data. The data of the highest spatial resolution such as SPOT data are also very useful in emphasizing and classifying the rock units of a small outcrop area. The detailed geological map of Jabal Daf-Wadi Fatima area is interpreted in this work from supervised classified images of different resolutions as well as the structure map of this area. This study shows that it is preferable to use the supervised classifications of multiresolution data for rock unit discrimination in detailed field mapping. 相似文献
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Remotely sensed image analysis using spectral-spatial information plays a key role in modern remote sensing applications. This article presents a new semi-automatic framework for spectral-spatial classification of hyperspectral images. The proposed framework benefits from a combination of pixel-based and object-based classification scenarios in which the main parameters are adaptively tuned. In order to reduce the complexity of the method, an unsupervised band selection technique is used as well. Meanwhile, the wavelet thresholding is applied in order to smooth the selected bands. The classification results after applying the proposed method to well-known standard hyperspectral datasets are better than those of the most of the other state-of-the-art approaches. As an example, the overall classification accuracy achieved by applying the proposed semi-automatic spectral-spatial classification framework to the Salinas dataset is more than 99% for 10% training samples per class. Moreover, the vital parameters are adaptively set in our approach. 相似文献
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高光谱遥感影像分类是高光谱遥感影像处理和应用的重要组成部分。然而，高光谱遥感影像具有波段数量较多和空间分辨率较高等特点，给分类任务带来一定的挑战。为了提高分类精度，充分利用影像的空间信息和像素间的局部信息，提出一种引导滤波联合局部判别嵌入的高光谱影像分类方法。首先，对高光谱遥感影像进行归一化，利用主成分分析方法实现特征提取，将提取的第一主成分影像作为引导图像；其次，采用引导滤波分别提取各波段影像的空间特征；然后，将提取的空间影像特征进行叠加，通过局部Fisher判别分析完成低维嵌入；最后，将得到的低维嵌入特征输入支持向量机分类器得到分类结果。采用Indian Pines和Pavia University两幅高光谱影像进行实验的结果表明：在分别从各类地物中随机选取10%和100个样本作为训练样本的情况下，其总体分类精度分别提高到98.28%和99.45%；对比其他相关方法，该方法能够获取更高的分类精度。该方法在低维嵌入的同时，有效利用了影像的空间信息，改善了分类效果。 相似文献
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Selma Etteieb Mounir Louhaichi Chariton Kalaitzidis Ioannis Z. Gitas 《Arabian Journal of Geosciences》2013,6(12):5017-5032
Mediterranean forests are characterized by spatiotemporal heterogeneity that is associated with Mediterranean climate, floristic biodiversity and topographic variability. Satellite remote sensing can be an effective tool for characterizing and monitoring forest vegetation distribution within these fragmented Mediterranean landscapes. The heterogeneity of Mediterranean vegetation, however, often exceeds the resolution typical of most satellite sensors. Hyper-spectral remote sensing technology demonstrates the capacity for accurate vegetation identification. The objective of this research is to determine to what extent forest types can be discriminated using different image analysis techniques and spectral band combinations of Hyperion satellite imagery. This research mapped forest types using a pixel-based Spectral Angle Mapper (SAM), nearest neighbour and membership function classifiers of the object-oriented classification. Hyperion classification was done after reducing Hyperion data using nine selected band combinations. Results indicate that the selection of band combination while reducing the Hyperion dataset improves classification results for both the overall and the individual forest type accuracy, in particular for the selected optimum Hyperion band combination. One shortcoming is that the performance of the best selected band combination was superior in terms of both overall and individual forest type accuracy when applying the membership classifier of the object-oriented method compared to SAM and nearest neighbour classifiers. However, all techniques seemed to suffer from a number of problems, such as spectral similarity among forest types, overall low energy response of the Hyperion sensor, Hyperion medium spatial resolution and spatiotemporal and spectral heterogeneity of the Mediterranean ecosystem at multiple scales. 相似文献
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Detection of soil salinity changes and mapping land cover types based upon remotely sensed data 总被引：2，自引：1，他引：1
Hamid Reza Matinfar Sayed Kazem Alavi Panah Farhad Zand Kamal Khodaei 《Arabian Journal of Geosciences》2013,6(3):913-919
Soil salinity is a major environmental hazard. The global extent of primary and secondary salt affected soils is about 955 and 77 M?ha, respectively. Soil salinity tends to increase in spite of considerable effort dedicated to land reclamation. This requires careful monitoring of the soil salinity status. The objectives of this study were: (a) to evaluate the capability of thematic mapper (TM) and multispectral scanner (MSS) imagery for mapping land cover types, (b) to analyse the spectral features of sail crusts relative to bare soil and gravely soil surface conditions, and (c) to detect the soil salinity changes during the period 1975–2004 in the Ardakan area located in the central Iranian Deserts. The Landsat MSS and TM on two different dates of September 14, 1975 and September 11, 2004, respectively, were used. Due to great confusion between some classes, the TM 6 was included in the band combination. The result of the image classification based on the combination of TM bands 3, 4, 5, and 6 showed of the classification results. For multi-temporal analysis, both TM and MSS images were classified with the same method but with a different number of training classes. The TM-classified image was regrouped to make it comparable with MSS regrouped classified image. The comparison between the classified images showed about 39% of the total area had changed in 29 years. The result of this study revealed the possibility of detecting important soil salinity changes by using Landsat satellite data 相似文献
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从分析ETM各波段图像的信息特征入手,在黄土覆盖等环境因素干扰严重的地区,利用遥感技术对其铀矿化的信息进行了提取.在ERDAS软件的支持下,应用图像融合、波段比值、主成分分析、光谱指数等技术方法,对ETM遥感影像数据进行增强处理.其中,图像融合与传统的图像融合略有不同,是在进行图像融合前将原始图像（ETM1～5,7）进行低通滤波处理,使得融合后的图像既保留高分辨率数据的空间信息,又保留低分辨率数据的光谱信息.在对研究区的遥感数据进行增强处理后,选取有利波段组合,圈定了有利铀成矿的远景区. 相似文献
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以深圳市东部滨海地区为试验区,对2004年11月21日ASTER遥感数据进行辐射和几何精校正处理,实地建立分类样地;根据多边形样地矢量数据计算分析12类地物在ASTER各波段光谱反射图和分类叠合图,同时进行植被指数和短波红外5个波段主成分分析;结合GIS并利用ASTER光谱波段、第一主成分、植被指数、立体像对生成的地形因子建立土地利用分类决策树表;再根据决策树表对ASTER影像进行土地利用分类。经验证,分类结果总体精度达到85.1%。应用效果表明,利用ASTER数据进行土地现状资源调查具有很好的性价比,能够满足土地利用现状调查的准确度和精度。 相似文献