植被覆盖区卫星高光谱遥感岩性分类

植被高覆盖区岩石和土壤在遥感图像上表现为弱信息、小目标,如何利用卫星高光谱遥感提取岩性弱信息是目前遥感地质应用中的最大挑战之一。以黑龙江呼玛地区为例,选择美国EO-1卫星Hyperion高光谱数据。由于植被与下伏岩石-土壤的光谱混合,分别计算研究区含土壤因子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,以此分离植被和岩石-土壤组分。在含土壤因子植被指数主成分分析的二维组分散点图上,明显区分出背景植被与异常岩石-土壤组分,证实了植被与岩石-土壤组分经主成分分析分离的效果。同时在不添加土壤因子植被指数的分析中,明显区分出植被覆盖信息。通过对实验区典型岩石进行野外光谱测试,然后对光谱进行连续统去除处理,将其作为参考光谱,与分离后的岩石-土壤光谱进行光谱特征拟合（SFF）,从而成功地识别出研究区内不同岩石类型,特别是玄武岩、流纹岩、砂砾岩、安山质凝灰岩、大理岩和石英片岩识别效果较好。根据研究区内不同岩石地层单元内岩石组合特征,通过分离后的组分合成图像,成功地实现了岩性分类。与已知地质图叠加,证实通过卫星高光谱数据提取的不同岩石类型颜色边界与地质图岩性界线吻合较好。结果表明：通过植被与岩石-土壤光谱组分分离,结合高光谱遥感的光谱特征拟合,能够识别不同的岩石类型,实现植被覆盖区岩性分类。     

基于Hyperion高光谱数据和随机森林方法的岩性分类与分析

探索利用高光谱数据的岩性填图新方法是遥感地质应用领域的重要需求之一。本文运用随机森林方法和EO-1Hyperion高光谱数据,对新疆塔里木西北部柯坪地区的局部区域进行岩性分类,并对相关问题进行分析。分别利用光谱特征以及加入光谱一阶导数特征进行岩性分类,并对不同特征对岩性分类的重要性进行分析,同时与现有的基于光谱角制图方法(SAM)进行比较。结果表明,与SAM方法相比,随机森林方法得到了更高精度的岩性分类结果,是一种有效可行的岩性分类方法。根据特征重要性的排序,蓝绿光波段、短波红外波段以及相应的一阶导数特征对研究区Hyperion数据的沉积岩岩性分类贡献更大。     

Mapping the distribution of iron ore minerals and spatial correlation with environmental variables in hilltop mining areas

The prime contribution of this assignment was to examine the hyperspectral remote sensing, based on iron ore minerals identification using spectral angle mapper (SAM) technique. Correlation analyses between field iron contents and environmental variables (soil, water, and vegetation) have been performed. Spectral feature fitting (SFF) and multi-range spectral feature fitting (MRSFF) methods were used for accuracy assessment in extracting iron ore minerals from Hyperion EO-1 data. Spectral inspections as a reference were used in SAM technique for image classification for iron ore minerals: Hematite (24.26%), Goethite (32.98%) and Desert (42.76). Iron ore minerals classification is justified by the United States Geological Survey (USGS) spectral library and field sample points. The regression analysis of USGS and Hyperion reflectance spectra has shown the moderate positive correlation. The regression analyses between iron ore contents and environmental parameters (soil, water, and vegetation) have shown the moderate negative correlation. The examination was significantly effectual in extracting iron ore minerals: Hematite (SFF RMSE?≤?0.51 MRSFF RMSE?≤?0.48), Goethite (SFF RMSE?≤?0.047 MRSFF RMSE?≤?0.438) and Desert (SFF RMSE?≤?0.63 and MRSFF RMSE?≤?0.50); and the MRSFF RMSE histograms indicate the above result likened to a conventional SFF RMSE. MRSFF RMS error result is best because multiple absorption features typically characterize spectral signatures. This analysis demonstrates the potential applicability of the methodology for iron minerals identification framework and iron minerals impact on environmental parameters.     

Hyperspectral satellite data analysis for pure pixels extraction and evaluation of advanced classifier algorithms for LULC classification

The study was carried out for Indian capital city Delhi using Hyperion sensor onboard EO-1 satellite of NASA. After MODTRAN-4 based atmospheric correction, MNF, PPI and n-D visualizer were applied and endmembers of 11 LCLU classes were derived which were employed in classification of LULC. To incur better classification accuracy, a comparative study was also carried out to evaluate the potential of three classifier algorithms namely Random Forest (RF), Support Vector Machines (SVM) and Spectral Angle Mapper (SAM). The results of this study reemphasize the utility of satellite borne hyperspectral data to extract endmembers and also to delineate the potential of random forest as expert classifier to assess land cover with higher classification accuracy that outperformed the SVM by 19% and SAM by 27% in overall accuracy. This research work contributes positively to the issue of land cover classification through exploration of hyperspectral endmembers. The comparison of classification algorithms’ performance is valuable for decision makers to choose better classifier for more accurate information extraction.     

基于ALOS遥感影像的湿地地表覆被信息提取研究

为了验证ALOS遥感影像湿地地表覆被信息提取的可行性,以黑龙江省三江平原典型内陆淡水沼泽湿地为研究对象,通过ALOS遥感影像波段的光谱及纹理特性分析,探讨适合水体、旱地、水田、沼泽、林地、建设用地、草甸等覆被类型的分类特征;基于非监督、监督及面向对象分类方法,遴选能实现最优分类结果的特征组合,为湿地地表覆盖分类数据源及方法的选择提供参考。结果表明：非监督、监督及面向对象分类方法的总体精度分别达到63.86%、96.14%和85.26%;非监督分类方法整体分类效果不够理想;面向对象方法虽然得到了相对较高的分类精度,但是针对建设用地、林地及草甸地类信息提取的精度处于较低水平;监督分类方法能取得较好效果,最适合于湿地地表覆被信息提取。     

Performance of image classification on hyperspectral imagery for lithological mapping

SVM and SAM classifiers for the lithological mapping using Hyperion data in parts of Gadag schist belt of western Dharwar craton, Karnataka, India were used. The main objective of the present study is to assess and compare the potential use of Hyperion data set for lithological mapping. Accuracy assessment of the derived thematic maps was based on the analysis of the confusion matrix statistics computed for each classification map. For consistency, the same set of validation points were used in evaluating the accuracy of the lithological thematic maps produced. On the basis of the accuracy assessment results, it appears that SVM generally outperformed the SAM classifier in both OA accuracy and individual classes’ accuracies. OA accuracy and Kc for SVM is 96.93% and 0.9655, whereas for SAM it is 74.02% and 0.7085 respectively. SVM classification is the best in describing the spatial distribution and the cover density of each lithology, as was also indicated from the statistics of the individual class results. The individual class accuracy were also analyzed for the SVM and the result show that PA ranges from 87% to 100% and UA ranges from 91% to 100%, whereas for SAM ranges from 15% to 95%, and from 31% to 100% respectively. The SVM method could effectively classify and improve on the existing geological map for the Gadag schist belt (GSB) using hyperspectral data. The results could be validated through field visits. Therefore, it is concluded that hyperspectral remote sensing data can be efficiently used to improve existing maps, especially in areas where same rock types show variable degree of alteration over smaller spatial scales.     

高光谱遥感油气探测技术

高光谱遥感探测技术已成为探测油气藏的前沿新技术之一.研究以油气微渗漏地表共生异常理论为基础, 采用基于小波主成份分析(principal component analysis, PCA)最大似然分类、端元提取分类、光谱库典型蚀变光谱分类和植被指数决策树分类方法, 对榆林典型稀疏植被地区的进行油气勘探, 提取了与烃异常相关的粘土、碳酸盐、植被异常等相关的专题信息产品, 得出综合异常区图.对照分析已知气井与油气异常区分布, 证明了油气微渗漏信息的提取与识别方法的有效性.      

Fine classification of construction land using high-resolution remote sensing images: a case study in planning restricted zone of nuclear power plant

Detailed construction land information plays a significant role in monitoring planning restricted zone of nuclear power plant and ecological environment protection. This study focuses on developing fine classifying method of construction land in planning restricted zone of nuclear power plant using high spatial resolution GF(GaoFen)-1 remote sensing images. The object-oriented classification method is used in this study; the important process of which is image segmentation and classification. Multi-scale segmentation method, rule-based decision tree, and the nearest neighbor classifier are used in classifying construction land classes, i.e., road, industrial, and residential. An optimal segmentation scale is crucial to image segmentation in object-oriented classification. Instead of laborious trial-and-error experiments for optimal image segmentation, the change rates of the local variance in the homogeneous region are calculated to get the optimal segmentation scales. Multi-level classification strategy is used in the following classification. Rule-based decision tree is used to classify road and water, vegetation and non-vegetation, and industrial and residential. And the nearest neighbor classifier is used to classify cropland and forest within the vegetation land use type. The accuracy assessment result shows that the overall accuracy is 89.67% and Kappa coefficient is 0.85 for object-oriented classification, which is much higher than pixel-based maximum likelihood classifier (overall accuracy is 79.17% and Kappa coefficient is 0.74) and support vector machine classifier (overall accuracy is 74.16% and Kappa coefficient is 0.68).     

基于面向对象分类的稀土开采区遥感信息提取方法研究

离子吸附型稀土矿是我国宝贵的矿产资源,运用遥感影像分类技术提取稀土开采区可以准确地实现对稀土开采状况的监测,但仅利用光谱信息往往难以保证分类精度。本文以江西寻乌稀土矿区为研究区,以IKONOS影像为数据源,应用面向对象分类方法提取了稀土开采区的遥感信息。针对稀土开采区的分布特点,选择基于边缘的分割算法进行影像分割;结合地形信息、光谱信息及几何信息建立规则集,进行特征提取;最后采用隶属度函数法实现面向对象分类,并与传统的光谱角填图分类进行对比分析。研究结果表明,面向对象分类法提取稀土开采区的总体精度为92.49%,Kappa系数为0.857 6,与传统监督分类方法相比有了很大的提高。     

A comparison of different land-use classification techniques for accurate monitoring of degraded coal-mining areas

Classification of different land features with similar spectral response is an enigmatical task for pixel-based classifiers, as most of these algorithms rely only on the spectral information of the satellite data. This study evaluated the performance of six major pixel-based land-use classification techniques (both common and advanced) for accurate classification of the heterogeneous land-use pattern of Jharia coalfield, India. WorldView-2 satellite data was used in the present study. The land-use classification results revealed that Maximum Likelihood classifier algorithm performed best out of the four common algorithms with an overall accuracy of about 84%. The advanced classifiers used in the study were Neural-Net and Support Vector Machine both of which gave excellent results with an overall accuracy of 91% and 95%, respectively. It was observed that use of very high-resolution data is not sufficient for obtaining high classification accuracy, selection of an appropriate classification algorithm is equally important to get better classification results. Advanced classifiers gave higher accuracy with minimal errors, hence, for critical planning and monitoring tasks these classifiers should be preferred.     

一种基于光谱角和光谱距离自动加权融合分类方法

高分辨率遥感影像分类一直是业内研究的热点之一,考虑到影像地物光谱角和光谱距离在分类中具有较好的互补性,提出了一种基于光谱角和光谱距离自动加权融合的分类方法,对传统多分类器分类的融合策略进行改进,能够在训练阶段根据样本自动地调整好各分类器对各类别进行分类的权重系数,使得融合后的分类结果更加科学和准确。QuickBird影像的分类实验表明,方法的分类精度明显优于单纯的光谱角或距离法,可广泛用于各种高分辨率影像的分类识别。     

吉林西部多时相遥感数据分类方案的构建及应用

为了深化遥感监测方法在生态环境调查中的应用,本文以吉林西部为试验区,设计了一种多时相遥感数据分类方案。该方案以物候信息为主,结合地物特征变量(植被、水体和土地信息)构建的多维特征空间数据集用于土地覆被分类。该遥感分类方案提取了9种地表覆被类型,结果表明:地表植被季节变化信息和土地利用信息的引入能明显改善土地覆被的分类精度;与基于原始波段的分类方案相比,多时相遥感数据分类方案的分类精度最好,总体分类精度为95.50%,Kappa系数为95.04%。     

基于光谱匹配的热红外高光谱数据岩性分类研究

从岩石光谱出发,结合光谱谱带强度特征和光谱波形特征,针对机载热红外高光谱数据（TASI）,在以往算法基础上,提出一种改进的算法--光谱离散能级波形匹配法（SDEM）,并将其运用到岩性分类研究中。SDEM算法能识别岩石光谱间的微小差异,并在充分考虑光谱谱带强度和波形特征的同时,有效减弱数据噪声。与传统的岩性分类方法--高光谱角度制图法（SAM）相比,改进的算法能更精确地区分岩石相似光谱,识别易混淆岩性,对出现“异物同谱”现象的岩石也具有更好的区分能力。将SDEM、SAM方法应用于甘肃柳园地区TASI数据岩性分类研究中,可看出SDEM方法能识别出SAM未识别或识别错误的岩性。通过研究区野外查证,可知SDEM方法所得岩性分类结果更符合岩石实际分布情况。可见光谱离散能级波形匹配法具有较好的岩性分类效果,能更好地区分地物。     

植被覆盖区高光谱蚀变矿物信息提取

在植被覆盖严重地区,基于综合光谱信息模型,开展了高光谱遥感典型蚀变矿物信息提取研究.以河北省承德市大营子地区为例,利用Hyperion数据,在植被覆盖度大于70%的地区,根据蚀变矿物与背景地物波谱特征的差异,提取了该区绿泥石、斜绿泥石、方解石和白云石蚀变矿物分布信息,与已知地质矿产资料及野外检查结果一致.最后依据蚀变矿...     

基于面向对象分类技术的雅砻江流域地表信息提取与分析

雅砻江是川西最重要的河流之一,雅砻江流域内的土地利用情况与流域内的生态环境及河流水质密切相关。以Landsat 8 OLI遥感影像为数据源,采用面向对象的分类方法,建立分类规则,获取研究区的土地利用情况,利用Fragstats 4.2软件进行区域景观空间分布特征分析。结果表明:采用面向对象的分类方法的分类总体精度达到8711%,Kappa系数达到0.855。雅砻江流域内的区域整体景观连接性较好。草地和林地是研究区的主导类型,整体看分布较为集中。     

基于各类特征对齐迁移网络的多时相遥感图像分类

为了在目标域遥感图像不存在标记数据的情况下实现自动分类,论文提出一种基于特征对齐的迁移网络.网络以各类类心对齐和协方差对齐作为迁移策略,全面描述域间各类别之间的对应关系,实现知识迁移.另外,网络采用线性修正单元作为激活函数,能够产生稀疏特征,提高分类效果.该迁移网络能够同时获得对齐的特征和自适应分类器,不需要目标域的标记数据,实现无监督迁移学习.在多时相的Hyperion高光谱遥感图像和WorldView-2多光谱遥感图像上的实验结果证明了该迁移网络的有效性.      

多源遥感数据图谱协同岩石单元分类方法

岩石单元的结构、构造、差异风化和出露状况在遥感图像上综合表现为图形纹理特征即“图”标志，其矿物成分和组合则表现为光谱特征即“谱”标志.传统遥感岩石单元分类以利用其光谱特征为主，图形纹理特征为辅，因此分类精度有限.以新疆维吾尔自治区与甘肃省交界的北山西段为研究区，开展岩石单元图形指数和光谱指数协同分类方法研究.基于Worldview-2全色图像构建的图形指数，能够量化岩石单元的层理、构造、展布形态和微地貌等特征，包括0°和45°定向滤波图像及灰度共生矩阵计算出的同质性和异质性特征图像、熵特征图像；光谱指数基于Worldview-2多光谱图像和ASTER（Advanced Spaceborne Thermal Emission and Reflection Radiometer）短波红外波段图像利用比值、和-差方法构建.多源遥感图像构建的光谱指数其光谱波段涵盖可见光-近红外及短波红外，包括RI（Ratio index）_ASTER、SI（Spectral index）_ASTER、SI_Worldview-2.采用面向对象方法对建立的图谱指数进行多尺度分割，依据不同岩石单元出露规模建立适宜的分割尺度，利用光谱指数自动提取相应岩石信息，实现岩石单元自动分类.结果表明，实验区基于图谱协同方法共划分出17类岩石单元，总体精度达到83.62%，而单独利用Worldview-2和ASTER图像，仅划分出13类和14类岩石单元.提出的图谱协同岩石分类方法可为我国西部高海拔深切割无人区地质调查及找矿工作提供新思路和遥感技术支撑.      

多源遥感数据在新疆卡拉麦里地区岩矿识别中的应用

遥感技术广泛应用于地质基础调查、矿产资源勘探、环境评估和地质灾害调查等方面。它已从多光谱发展到高光谱阶段,Landsat- 8是目前最具有代表性和最常用的多光谱数据,ASTER具有高的分辨率和多波段特征,资源一号02D(ZY1- 02D)卫星是我国2019年发射的高光谱业务卫星。为了更好地了解多源遥感数据在岩矿识别中的作用,在新疆东天山卡拉麦里地区进行了相关研究。结果表明：Landsat- 8 OLI的PCA变换结果清晰识别了研究区不同的岩性和地层;使用Landsat- 8 OLI、ASTER和ZY1- 02D高光谱数据,分别采取不同的图像端元提取方法,在进行光谱分析的基础上,利用光谱角填图（SAM）即可得到研究区的主要矿物分类图件。通过野外验证,应用GIS技术进行集成和分析,修正相关图件后,便得到了精准的矿物分类综合图。研究表明：多源遥感数据的集成在岩矿识别方面效果良好、前景巨大。     

The development of forest damage — control and prognosis on the basis of remote sensing data

Forest damage can be caused by three types of stress which exercise their influence by predisposing, causative and contributory factors. Predisposing factors, above all permanent immision stress, cause the general diminishing of the trees' vitality. Causative fators such as long and extreme periods of dryness cause a fast and evident deterioration of the forest stand, contributory factors as eg pest attacks cause their mortification. When using remote sensing data to monitor the development of forest damage it is necessary therefore not only to pay attention to spectral signatures characterizing the degree of green space and thus the information of the canopy but also to spectral signs showing the water supply of the stands. This has not been done so far though nowadays the necessary multispectral satellite data are on regular offer. For this reason it may be suggested that the existing possibilities to predict the development of forest damage on the basis of remote sensing data have not yet been fully used.In order to examine these possibilities, in the environs of Berlin, ie in the east and south of the Land of Brandenburg both the vegetation index which has been known from literature for a long time and an index for the difference in remission identifying the water bands in the short-wave infrared have been calculated on the basis of Landsat-TM data. A multi-temporal comparison shows that despite the bad conditions of the forest in 1991 on the test site located north-east of Berlin due to a relatively good water supply in that year the pine forest damage had not progressed in 1992. This allows to draw the conclusion that at least in the pine forests of the East German interior lowland the development of forest damage is essentially determined by dryness stress. In this way on the basis of 1992 Landsat-TM data there was not only an inventory made of the actual condition of the forest on the east and south-east of the Land of Brandenburg but also trends of damage progress shown. The damage classification of pine stands be completed by a prognosis as to the development of damage.     

基于Hyperion高光谱数据的矿物蚀变提取——以内蒙古西部狼山地区炭窑口矿床为例

以已知的矿床——内蒙古炭窑口矿床为研究区,基于围岩矿化蚀变理论,利用高光谱遥感技术对矿化蚀变异常信息进行提取。首先对Hyperion影像进行预处理,包括去坏线、条带、大气校正及光谱重建。其次,从反演后的反射率数据提取5类端元矿物,通过分析矿物组合,最终确定5类矿物并利用波谱角制图法在研究区填图,进而提取矿化蚀变信息。在以上研究过程中改进了去除条带的方法。在获得蚀变信息后,结合炭窑口研究区的地质图进行应用分析,通过解释异常,验证其是否为矿化引起的蚀变。最后,再根据研究区的地质情况和前人对矿床的研究成果,尤其是对含矿地层的研究成果,结合提取的蚀变矿物,预测其他有利靶区。结果表明,获得的矿化蚀变信息与矿点吻合较好,具有一定的借鉴意义。