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1.
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
2.
遥感图像分类技术对于荒漠草原浅覆盖区第四系覆盖物分类具有重要意义。以内蒙古旗杆甸子幅1∶5万填图试点为例,基于ASTER、GF-2等多源遥感数据,利用植被抑制法、波段比值法、主成分分析以及纹理信息提取等多种方法,充分考虑了多光谱数据的光谱信息和高分辨率数据的形状、空间结构、纹理信息等特征,结合面向对象分类法,对研究区第四系覆盖物进行了分类,并比较分析了不同分类方法的分类效果与精度。结果表明:将波段比值、主成分分析以及纹理分析多种特征作为辅助数据参与分类,其分类效果优于基于单一ASTER数据进行的分类;通过几种不同分类方法的比较分析,发现多特征面向对象分类的总体精度最高,达到85.40%,比多特征传统监督分类的总体精度提高了约11%,分类影像上地物边界清晰。该法分类技术可以为荒漠草原浅覆盖区的地质填图提供相关技术支持。 相似文献
3.
植被的发育限制了遥感在地质学方面的应用, 在植被覆盖区进行岩石填图, 首先要考虑去除植被干扰影响.以内蒙古东乌旗地区为例, 选择先进星载热发射和反射辐射仪(advanced spaceborne thermal emission and reflection radiometer, ASTER)数据, 分别计算研究区内含土壤因子植被指数和不含土壤因子的植被指数, 并对两类不同的植被指数进行主成分分析, 挑选出植被信息被抑制和岩石-土壤信息突出的主成分进行岩性分类, 和利用最大似然法的分类结果进行对比分析, 评价两种方法的岩性分类性能, 植被抑制法的总体分类正确率为82.946 8%, 最大似然法的总体分类正确率为76.364 3%.结果说明在植被覆盖区, 利用植被指数来抑制植被信息是可行的, 和常规分类方法中的最大似然法相比, 大大提高解译的准确性. 相似文献
4.
花敖包特是位于内蒙古西乌珠穆沁旗的一个大型脉状银铅锌多金属矿床,其矿区的蚀变矿物主要是黏土矿物、绿泥石和方解石,此外还可见孔雀石、萤石和赤铁矿等局部蚀变矿化.结合花敖包特矿床的地质特征,本研究利用高级星载热辐射和反射探测器(ASTER)获取的遥感数据进行蚀变矿物填图.利用ASTER可见光-近红外波段和短波红外波段,对校正后遥感数据采用波段比值分析、波段组合分析和主成分分析来提取黏土矿物、绿泥石和方解石化蚀变.利用ASTER热红外波段,根据相关数值关系,对校正后遥感数据计算二氧化硅含量、QI值以及CI值来分析地质体二氧化硅含量变化规律和碳酸盐化蚀变.结合野外勘查结果发现,主成分分析、波段组合分析能够初步划分矿区绿泥石化、碳酸盐化和黏土化蚀变带以及硅化带,其中主成分分析方法取得的效果较好,显示矿区北部以绿泥石化带为主,南部以碳酸盐化和黏土化混合带为主,而热红外波段数值分析方法在矿田尺度下对矿区附近的硅化带和碳酸盐化蚀变也具有一定的识别能力. 相似文献
5.
运用LANDSAT ETM+和ASTER数据进行岩性分类 总被引：5，自引：0，他引：5
本文评价了运用ASTER和LANDSAT ETM+数据进行岩性制图的性能。分别利用ASTER数据不同波段区图像及其组合,以及ETM+数据进行岩性分类,并探讨了将ASTER和ETM+数据叠加在一起进行了岩性分类; 利用现有地质图对所有分类结果进行了定量评价。结果表明,ASTER数据不同波段的岩性识别能力不同,并且较ETM+数据能更准确地识别岩性。更重要的是,把ASTER与ETM+数据结合在一起进行岩性分类,可获得比用任一数据单独分类更高的分类精度,表明二者的光谱特征具有一定的互补性。 相似文献
6.
基于决策树模型的上海城市湿地遥感提取与分类 总被引：5，自引：0，他引：5
城市湿地是上海重要的生态基础并具有复杂多变的自然特性。研究采用决策树分类方法,以TM影像多光谱波段特征为主要分类变量,采用经K-T变换、IHS变换等光谱增强后的数据以及利用灰度共生矩阵分析影像第一主成分的纹理统计量作为辅助分类变量,结合城市湿地几何特征信息,构建上海城市湿地决策树分类模型,进行上海市湿地信息的遥感提取和分类。结果表明：（1）上海城市湿地总面积为1 277.40 km2;其中水田面积最大,占总面积的65.30%;其次为河流、库塘、湖泊和芦苇。（2）决策树模型的分类方法在一定程度上提高了城市湿地提取和分类的精度,使其达到89.05%;与传统的最大似然法相比,总精度提高了约10%。 相似文献
7.
以辽宁省双台子河口湿地为研究对象,以Landsat 8和HJ-1-A/HJ-1-B的多时相遥感影像为数据源,根据研究区现状,将研究区分为旱地、芦苇、水田、碱蓬、混合植被、水面、滩涂、居民点、养殖塘九个类型.利用时间序列的归一化植被指数提取植被与非植被的分类阈值,采用粗糙集理论和多时相遥感影像,对植被和非植被分别进行分类规则的获取,建立了研究区决策树分类模型.为了进行精度评价,利用相同的训练点又进行了同样基于像元的最大似然法分类.最后利用混淆矩阵对上述两种方法进行了精度评估,基于粗糙集的决策树分类法与最大似然法总体分类精度分别为93.70%和91.62%,Kappa系数分别为0.92和0.90,两项指标值基于粗糙集理论法均比最大似然法有所提高.这为构建决策树分类模型进行湿地地表分类信息提取提供了一条新的研究思路. 相似文献
8.
由于传统的岩性分类方法受岩石辐射干扰因素大, 存在"同物异谱"以及"同谱异物"现象, 岩性分类精度低, 所以在深入分析岩石矿物光谱特征基础上, 以西昆仑成矿带地区的二长花岗岩、石英正长岩以及正长岩为研究对象, 基于这3种岩性的实测光谱数据以及先进星载热发射和反射辐射仪(advanced spaceborne theemal emission and reflection radiometer, ASTER)影像数据的波段设置特征, 建立了RI和SI两种光谱指数.利用所建立的RI以及SI光谱指数对ASTER遥感数据进行岩性分类.结果显示, RI和SI两种光谱指数法在提取二长花岗岩时精度达到70%以上, 石英正长岩精度为80%左右, 与最大似然法得到的分类结果相比, 这两种岩性的分类精度明显提高了. 相似文献
9.
选取陕西省榆林气田地区为研究区, 利用ASTER数据多波段的优势, 在研究区开展了基于波段运算和主成分分析的二价铁、黏土矿化以及碳酸盐矿化等烃类微渗漏蚀变信息提取, 圈出4个有效靶区; 并利用ASTER数据的热红外波段进行基于分裂窗简化算法的温度反演得到研究区的温度图像以验证靶区的有效性, 温度反演结果中高温异常区和靶区范围基本吻合; 最后对USGS光谱库中常见蚀变矿物光谱、研究区实测光谱以及原油光谱进行综合分析, 进一步验证靶区有效性, 多种方法相结合以提高烃类微渗漏蚀变信息提取精度。 相似文献
10.
在已有研究区水文地质资料的基础上,进行基于ASTER数据的岩溶地下水天然出露点信息的提取与识别.根据研究目标,以ASTER数据为信息源和调查工具,制定相应的图像处理和分析流程,增强与岩溶地下水天然出露点信息相关的影像特征;应用热红外遥感理论和技术,以ASTER数据热红外波段为信息源,借助岩溶地下水天然出露点与其他地物的温度差异,探索它们之间的可分性;结合ASTER数据可见光/近红外、短波红外波段的解译成果,进行岩溶地下水天然出露点信息识别. 相似文献
11.
Anis Gasmi Cécile Gomez Hédi Zouari Antoine Masse Danielle Ducrot 《Arabian Journal of Geosciences》2016,9(20):753
The purpose of this study was to examine the efficiency of Advanced Space Borne Thermal Emission and Reflection Radiometer (ASTER) data in the discrimination of geological formations and the generation of geological map in the northern margin of the Tunisian desert. The nine ASTER bands covering the visible (VIS), near-infrared (NIR) and short-wave infrared (SWIR) spectral regions (wavelength range of 400–2500 nm) have been treated and analyzed. As a first step of data processing, crosstalk correction, resampling, orthorectification, atmospheric correction, and radiometric normalization have been applied to the ASTER radiance data. Then, to decrease the redundancy information in highly correlated bands, the principal component analysis (PCA) has been applied on the nine ASTER bands. The results of PCA allow the validation and the rectification of the lithological boundaries already published on the geologic map, and gives a new information for identifying new lithological units corresponding to superficial formations previously undiscovered. The application of a supervised classification on the principal components image using a support vector machine (SVM) algorithm shows good correlation with the reference geologic map. The overall classification accuracy is 73 % and the kappa coefficient equals to 0.71. The processing of ASTER remote sensing data set by PCA and SVM can be employed as an effective tool for geological mapping in arid regions. 相似文献
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13.
Ashraf Emam Zakaria Hamimi Abdelhamid El-Fakharani Ezzat Abdel-Rahman Juan Gomez Barreiro Mohammed Y. Abo-Soliman 《Arabian Journal of Geosciences》2018,11(23):756
Lithological discrimination of Neoproterozoic rocks occupying Nugrus-Hafafit area, South Eastern Desert of Egypt, has been carried out using Operational Land Imager (OLI) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensors’ imagery data. The applicable processing enhancement techniques include optimum index factor (OIF), band ratioing, principal component analysis (PCA), and minimum noise fraction (MNF) transform. The area comprises varieties of low-grade metamorphosed ophiolitic mélange and island-arc rocks, thrusting over high-grade metamorphic gneissic core complexes, and intruded by syn-, late-, and post-tectonic granitoids. The OLI band ratio 6/7 discriminates clearly the ophiolitic serpentinites-talc-carbonate rocks, while 4/5 ratio image is able to separate between mafic and felsic rocks. Moreover, the ASTER band ratio 6/8 is used to distinguish the amphibole-bearing rocks, including amphibolite and hornblende gneiss. The OLI and ASTER second principal component (PC2) images reflect the contrast spectral behavior of ophiolitic mélange rocks through visible-near-infrared (VNIR) and shortwave (SWIR) regions. The OLI-PC3 shows the ability to delineate the Fe-rich rocks, including amphibolite and metamafics, while ASTER-PC3 is effective for quartz-feldspathic granites and psammitic gneisses. Visual interpretation and integration of processed data with petrography and field investigation resulted in complete differentiation for the different lithologies and creation of a new detailed geological map of Nugrus-Hafafit area. 相似文献
14.
Integration of ASTER and landsat TM remote sensing data for chromite prospecting and lithological mapping in Neyriz ophiolite zone,south Iran 
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下载免费PDF全文 Alireza Eslami Majid Ghaderi Sankaran Rajendran Amin Beiranvand Pour Mazlan Hashim 《Resource Geology》2015,65(4):375-388
Chromite deposits in Iran are located in the ophiolite complexes, which have mostly podiform types and irregular in their settings. Exploration for podiform chromite deposits associated with ophiolite complexes has been a challenge for the prospectors due to tectonic disturbance and their distribution patterns. Most of Iranian ophiolitic zones are located in mountainous and inaccessible regions. Remote sensing approach could be applicable tool for choromite prospecting in Iranian ophiolitic zones with intensely rugged topography, where systematic sampling and conventional geological mapping are limited. In this study, Landsat Thematic Mapper (TM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data were used for chromite prospecting and lithological mapping in the Neyriz ophiolitic zone in the south of Iran. Image transformation techniques, namely decorrelation stretch, band ratio and principal component analysis (PCA) were applied to Landsat TM and ASTER data sets for lithological mapping at regional scale. The RGB decorrelated image of Landsat TM spectral bands 7, 5, and 4, and the principal components PC1, PC2 and PC3 image of ASTER SWIR spectral bands efficiently showed the occurrence of major lithological units in the study area at regional scale. The band ratios of 5/3, 5/1, 7/5 applied on ASTER VNIR‐SWIR bands were very useful for discriminating most of rock units in the study area and delineation of the transition zone and mantle harzburgite in the Neyriz ophiolitic complex. Spectral Angle Mapper (SAM) technique was implemented to ASTER VNIR‐SWIR spectral bands for detecting minerals of rock units and especially delineation of the transition zone and mantle harzburgite as potential zones with high chromite mineralization in the Neyriz ophiolitic complex. The integration of information extracted from the image processing algorithms used in this study mapped most of lithological units of the Neyriz ophiolitic complex and identified potential areas of high chromite mineralization (transition zone and mantle harzburgite) for chromite prospecting targets in the future. Furthermore, image processing results were verified by comprehensive fieldwork and laboratory analysis in the study area. Accordingly, result of this investigation indicate that the integration of information extracted from the image processing algorithms using Landsat TM and ASTER data sets could be broadly applicable tool for chromite prospecting and lithological mapping in mountainous and inaccessible regions such Iranian ophiolitic zones. 相似文献
15.
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
16.
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
17.
ASTER传感器提供了包括可见光-近红外（VNIR）、短波红外（SWIR）、热红外（TIR）共14个波段的地物波谱数据。其中最高分辨率在VNIR波段达到了15m。另外,在获得地物波谱数据的同时,它还能利用在03N波段和03B波段以不同视角获得的图像组成立体像对,从而获得同一地区的高程数据。这些特点为该数据的地质应用提供了很大的方便。由于断裂构造能造成断裂带内及其两侧之间地质环境与地貌特征的差异,这种差异会造成相应地区遥感影像中地物的波谱特征及空间分布即纹理型式上的差别。反之,使用多种图像处理技术,通过增强图像的灰度差并获取纹理图像,可以使这种差异凸现出来分析图像中的这些差异,找出在灰度和纹理上的规律性变化,不但可以推断可能的断裂分布,并可确定它的性质.利用ASTER数据高空间分辨率、多波谱段及同时提供DEM数据的特点,将之用于甘肃白银地区断裂构造的识别和判读。试验表明,利用该数据判读的结果与实地调查结果有相当的一致性。 相似文献
18.
As wheat represents the main staple food and strategic crop in Egypt and worldwide and since remote sensing satellite imagery is the tool to obtain synoptic, multi-temporal, dynamic, and time-efficient information about any target on the Earth, the main objective of the current study is to use remote sensing satellite imagery to generate remotely sensed empirical preharvest wheat yield prediction models. The main input parameters of these models are spectral data either in the form of spectral reflectance data released from Satellite Pour l’Observation de la Terre (SPOT) 4 satellite imagery or in the form of spectral vegetation indices. The other input factor is leaf area index (LAI) that was measured by LAI Plant Canopy Analyzer. The four spectral bands of SPOT4 imagery are green, red, near-infrared, and middle infrared; the five vegetation indices that are forms of ratios between red and near-infrared bands are normalized difference vegetation index, ratio vegetation index, soil-adjusted vegetation index, difference vegetation index, and infrared percentage vegetation index. Another vegetation index is green vegetation index that is calculated through a ratio between green band and near-infrared band. Each of the above-mentioned factors was used as an input factor against wheat yield to generate wheat yield prediction models. All generated models are site-specific limited to the area and the environment and could be applicable under similar conditions in Egypt. The study was carried out in Sakha experimental station by using the dataset from two wheat season 2007/2008 and 2009/2010. The total wheat area was 1.3 ha cultivated by Sakha 93 cultivar. Modeling and validation process were carried out for each season independently. Modeled yield was tested against reported yield through two common statistical tests; the standard error of estimate between modeled yield and reported yield, and the correlation coefficient for a direct regression analysis between modeled and reported yield with each generated model. Generally, as shown from the correlation coefficient of the generated models, green and middle infrared bands did not show good accuracy to predict wheat yield, while the other spectral bands (red and near-infrared) bands showed high accuracy and sufficiency to predict yield. This was proven through the correlation coefficient of the generated models and through the generated models with the wheat crops for the two seasons. Accordingly, the green vegetation index that is generally calculated from green and near-infrared bands showed relatively lower accuracy than the rest of the vegetation index models that are calculated from red and near-infrared bands. LAI showed high accuracy to predict yield as shown from the statistical analysis. The models are applicable after 90 days from sowing stage and applicable in similar regions with the same conditions. 相似文献