新疆哈密三种典型蚀变矿物的HyMap高光谱遥感信息提取

利用机载的可见光、近红外及短波红外成像光谱(ＨyMap)数据,对新疆哈密地区岩矿信息识别方法进行研究。基于方解石、 绿泥石和绢云母3种常见蚀变矿物的光谱特征,在遥感数据定标和反射率图像转换的基础上,应用光谱角度模型（SAM）分类法进行自 动匹配识别和信息提取。通过掩膜技术进行方解石、绿泥石和绢云母矿物填图,并结合实验室光谱数据库光谱进行了验证。     

基于端元提取的高光谱影像特定军事对象识别方法研究

高光谱遥感能够获得地物的连续光谱信息,实现地物空间信息、辐射信息、光谱信息的同步获取,因而在军事领域具有巨大的应用价值和广阔的发展前景。本文系统介绍了基于端元的最小噪声分离(MNF)、纯净像元指数(PPI)、终端单元选择、N维散度分析、波谱角度制图(SAM)等高光谱数据处理技术对特定对象的识别与提取的原理和方法。     

基于Hyperion影像的涩北气田油气信息提取

 对柴达木地区涩北气田地质地理环境下的蚀变矿物进行分析，结合卫星高光谱遥感数据Hyperion的图谱，对已知气田区与背景区光谱特征进行相关分析，确定了932.64~1 346.25 nm与2 002.06~2 385.5 nm为油气信息识别的有利波长范围； 利用光谱角制图（SAM）技术提取了涩北气田油气的空间分布信息和台吉乃尔含气构造等远景区，为高光谱遥感油气勘探提供了有效技术方法与途径。     

岷江上游亚高山典型森林植被高光谱特征识别

在高光谱数据分类应用中,地物光谱特征分析是对地物进行分类和检索的基础性工作。选取禾本科斑竹、草本科蕨类、荨麻科冷水花、杉科杉木和棕榈科棕榈树等5种岷江上游亚高山森林植被进行实地光谱测量,建立高光谱相似性度量参量,如欧式距离(Euclidean distance,ED)、光谱角度(spectral angle mapper,SAM)、光谱信息散度(spectral information divergence,SID)、SID和SAM混合SID(TAN)以及基于道格拉斯-普克算法的光谱降维距离(spectral distance based on Douglas-Peucker,SDDP)度量算法,定量分析对亚高山森林植被的识别能力。研究结果表明:5种亚高山森林植被光谱特征的差异主要表现在光谱曲线反射波峰和波谷位置;ED对冷水花的相对光谱识别概率最高;SID和SID(TAN)对斑竹与蕨类的识别概率最高;SDDP对杉木的识别概率最高;SAM,SDDP,ED,SID(TAN)和SID这5种光谱相似性测度算法对亚高山森林植被的相对光谱识别熵分别是1.51,1.59,1.61,2.16和2.18,说明光谱角度制图具有较高的识别能力;而道格拉斯-普克光谱检索算法是在提取光谱曲线特征向量的基础上进行相似性测度,其降低了光谱检索的时间频率,在保证相近识别能力的条件下,能够大大提高程序的检索效率,是一种快速有效的高光谱特征匹配和检索算子。     

未辐射校正高光谱数据应用于分类的可行性分析--应用光谱角制图法

常用的高光谱图像分类识别技术要求图像首先经过辐射校正处理。考虑到该过程的复杂性,我们通过比较光谱角制图法对辐射校正前后高光谱数据分类识别的结果,分析了未辐射校正数据应用于分类的可行性。使用光谱角制图法的原因在于该方法通过比较光谱向量间的角度确定相似程度,且参考光谱可以从原始图像中直接提取,这两点为辐射校正前数据用于分类提供了可能性。     

基于机载多光谱遥感数据的溢油信息提取方法

为研究应用紫外-可见光-近红外-热红外遥感数据识别海上溢油的方法,利用机载多光谱溢油监测设备获取了舟山海域多光谱遥感数据,并利用野外地物光谱仪获取了水体和油膜的反射光谱。通过提取并分析机载多光谱遥感图像上多目标的影像特征和光谱响应特征,利用决策树分类法对海上油膜相对厚度的分布进行分类和制图。研究表明,基于机载多光谱遥感数据的溢油信息提取方法总体分类精度达93.7%,能够准确区分薄油膜和厚油膜,可有效提取海洋溢油污染信息,完全能够满足海洋溢油污染遥感监测需求。     

基于HICO高光谱的黄河口湿地植被识别方法

湿地植被制图是湿地自然资源管理过程中的一项重要任务。文中选取黄河口湿地为研究区,应用海岸带高光谱成像仪影像,提出了一种基于包络线去除的改进型光谱角匹配(Spectral Angle Mapper based on Continuum Removal,SAM_CR)方法,对芦苇、狄草、碱蓬和怪柳等湿地典型植被进行分类提取。结果表明,SAM_CR湿地典型植被种类识别的总体精度由传统SAM方法的74.87%提高到80.61%。     

高光谱遥感积雪制图算法及验证

雪盖面积是高山地区和季节雪盖区水文和气象模型的重要输入因子。机载和星载遥感数据提取的雪盖面积是融雪径流模型的重要组成部分。对应不同传感器件的光谱特征，多种分类方法被相继提出。但是，缺乏相对独立的验证手段来评价各种分类方法，其主要原因是缺乏地面真实状态。针对该现状，本研究利用高光谱图像的细分光谱特征，建立高光谱影像及其对应“地面真相”的像对数据库来发展和验证积雪制图算法，并展示MODIS积雪制图算法验证和ASTER混合像元分解雪盖制图算法研究的应用实例。     

基于SVM遥感矿化蚀变信息提取研究

提出一种基于支持向量机(SVM)遥感数据矿化蚀变信息提取的新方法。该方法首先根据蚀变岩及矿体围岩的实测光谱数据,利用光谱角度制图法(SAM)提取训练样本,应用交叉比对(cross-validation)算法确定最优SVM模型参数,选择径向基(RBF)核函数,训练SVM分类器模型;然后,用训练好的SVM模型进行遥感矿化蚀变信息提取;最后,选择青海芒崖地区的ETM数据进行遥感矿化蚀变信息提取试验。试验结果经野外检查和验证,效果良好。     

草地光谱分类最佳时相选择分析

利用2003年5-10月在环青海湖地区获取的典型天然草地与人工草地多时相地面高分辨率光谱数据，首先分析了最大似然分类法、支持向量机分类法、光谱角分类法、最小距离分类法和人工神经网络分类法所对应的最佳光谱变换方案；通过16个时相光谱数据的分类对比实验，分别确定了天然草地与人工草地分类、人工草地分类、天然草地分类的最佳时相；最后利用TM遥感数据对地面光谱数据分析结果进行了补充验证。     

Lithological mapping from hyperspectral data by improved use of spectral angle mapper

The spectral angle mapper (SAM), as a spectral matching method, has been widely used in lithological type identification and mapping using hyperspectral data. The SAM quantifies the spectral similarity between an image pixel spectrum and a reference spectrum with known components. In most existing studies a mean reflectance spectrum has been used as the reference spectrum for a specific lithological class. However, this conventional use of SAM does not take into account the spectral variability, which is an inherent property of many rocks and is further magnified in remote sensing data acquisition process. In this study, two methods of determining reference spectra used in SAM are proposed for the improved lithological mapping. In first method the mean of spectral derivatives was combined with the mean of original spectra, i.e., the mean spectrum and the mean spectral derivative were jointly used in SAM classification, to improve the class separability. The second method is the use of multiple reference spectra in SAM to accommodate the spectral variability. The proposed methods were evaluated in lithological mapping using EO-1 Hyperion hyperspectral data of two arid areas. The spectral variability and separability of the rock types under investigation were also examined and compared using spectral data alone and using both spectral data and first derivatives. The experimental results indicated that spectral variability significantly affected the identification of lithological classes with the conventional SAM method using a mean reference spectrum. The proposed methods achieved significant improvement in the accuracy of lithological mapping, outperforming the conventional use of SAM with a mean spectrum as the reference spectrum, and the matching filtering, a widely used spectral mapping method.     

利用地理标签数据感知城市活力

高度活跃的城市是社会稳定发展的基础。基于地理标签感知的城市活力能够量化城市发展现状,探索城市活力的影响机制,为精细化城市治理提供技术支撑。传统城市活力研究依赖于街区的活力调查,时间长,费用高。本文研究利用兴趣点和社交媒体签到等地理标签数据,提出了城市活力度量指标,探索性分析城市活力的分布模式。基于土地利用、道路和建筑物等数据计算建成环境指标,构建城市活力和建成环境之间的普通线性回归与空间自回归模型,揭示了影响城市活力的建成环境因素。基于深圳市的试验结果表明:兴趣点和社交媒体签到数据能够较好地指示城市活力。深圳市的城市活力主要受商业用地、工业用地、土地混合利用以及路网密度、地铁站点密度的影响。住宅用地和建筑物占地密度对基于POI的城市活力具有显著影响。     

光谱角法在ASTER影像土地利用分类中的应用

为了验证光谱角法(SAM)对ASTER影像分类效果,本文对SAM分类的原理进行了阐述和分析,采用了SAM方法以ASTER遥感影像数据为数据源对泸沽湖地区的土地利用进行分类研究,并对分类精度进行了分析。研究结果表明SAM方法用于ASTER数据是一种有效的分类方法,对提高ASTER影像分类精度具有重要的意义。     

Integration of remote sensing,gravity and geochemical data for exploration of Cu-mineralization in Alwar basin,Rajasthan, India

In spite of the dominance of traditional mineral exploration methods that demand physical characterization of rocks and intense field work, remote sensing technologies have also evolved in the recent past to facilitate mineral exploration. In the present study, we have processed visible near infrared (VNIR) and shortwave infrared (SWIR) bands of Advanced space-borne thermal emission and reflection radiometer (ASTER) data to detect surface mineralization signatures in Mundiyawas - Khera area in Alwar basin, north-eastern Rajasthan, India using spectral angle mapper (SAM). The potential of SAM method to detect target under variable illumination condition was used to delineate galena, chalcopyrite, malachite etc. as surface signatures of mineralization. It was ensured that the identified surface anomalies were spectrally pure using pixel purity index. Spectral anomalies were validated in the field and also using X-Ray diffraction data. Spectral anomaly maps thus derived were integrated using weight of evidence method with the lineament density, geochemical anomaly, bouger anomaly maps to identify few additional potential areas of mineralization. This study thus establishes the importance of remote sensing in mineral exploration to zero in on potentially ore rich but unexplored zones.     

结合光谱角的最大似然法遥感影像分类

遥感影像含有丰富的信息,反映了地物特征。其中光谱角侧重描述了光谱的形状特征,具有对多光谱图像增益不敏感的特点。最大似然法是遥感影像分类最常用的方法之一,文中对该方法的后验概率判别函数进行修改,将光谱角以概率因子的形式加入到判别函数中构造一种新的判别函数,有机地将光谱角这一特征信息加入影像分类。通过实验,并与最大似然法和光谱角匹配法分类结果进行比较,结果表明,结合光谱角的最大似然分类法的分类精度得到提高。     

Evaluating the Performance of Multi-Class and Single-Class Classification Approaches for Mountain Agriculture Extraction Using Time-Series NDVI

Supervised multi-class classification (MCC) approach is widely being used for regional-level land use–land cover (LULC) mapping and monitoring. However, it becomes inefficient if the end user wants to map only one particular class. Therefore, an improved single-class classification (SCC) approach is required for quick and reliable map production purpose. In this regard, the current study attempts to evaluate the performance of MCC and SCC approaches for extracting mountain agriculture area using time-series normalized differential vegetation index (NDVI). At first, samples of eight LULC classes were acquired using Google Earth image, and corresponding temporal signatures (TS) were extracted from time-series NDVI to perform classification using minimum distance to mean (MDM) and spectral angle mapper (i.e., multi-class SAM—MCSAM) under MCC approach. Secondly, under SCC approach, the TS of three agriculture classes (i.e., agriculture, mixed agriculture and plantation) were utilized as a reference to extract agriculture extent using Euclidean distance (ED) and SAM (i.e., single-class SAM—SCSAM) algorithms. The area of all four maps (i.e., MDM—19.77% of total geographical area (TGA), MCSAM—21.07% of TGA, ED—15.23% of TGA, SCSAM—13.85% of TGA) was compared with reference agriculture area (14.54% of TGA) of global land cover product, and SCC-based maps were found to have close agreement. Also, the class-wise detection accuracy was evaluated using random sample point-based error matrix which reveals the better performance of ED-based map than rest three maps in terms of overall accuracy and kappa coefficient.     

Comparison of Different Mapping Techniques for Classifying Hyperspectral Data

Hyperion is a space borne sensor which provides powerful tool in discriminating land cover features including urban area and in preparation of urban maps. It gives hyperspectral images in 242 bands within 400?nm to 2,500?nm wavelength range with 10?nm band-width. The Hyperion image in raw form is badly affected with several atmospheric effects which cause haziness. In this study hyperspectral image is atmospherically corrected by using FLAASH model of ENVI. After atmospheric correction the urban area was mapped using the spectral endmember collected by the procedure which includes minimum noise fraction (MNF), pixel purity index (PPI) and n-dimensional visualization in ENVI software. The aim of this study is to map the urban area using several mapping techniques such as Spectral Angle Mapper (SAM), Mixture Tune Matched Filtering (MTMF) and Linear Spectral Unmixing. The urban land covers displayed noticeable differences from one another in the spectral responses in the Hyperion image. The overall accuracy of the SAM classified map was 89.41%, which indicated good potential of Hyperion image for Classification. Use of the other approaches, linear spectral unmixing and MTMF have improved the classification results.     

Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data

Information on Earth's land surface cover is commonly obtained through digital image analysis of data acquired from remote sensing sensors. In this study, we evaluated the use of diverse classification techniques in discriminating land use/cover types in a typical Mediterranean setting using Hyperion imagery. For this purpose, the spectral angle mapper (SAM), the object-based and the non-linear spectral unmixing based on artificial neural networks (ANNs) techniques were applied. A further objective had been to investigate the effect of two approaches for training sites selection in the SAM classification, namely of the pixel purity index (PPI) and of the direct selection of training points from the Hyperion imagery assisted by a QuickBird imagery and field-based training sites. Object-based classification outperformed the other techniques with an overall accuracy of 83%. Sub-pixel classification based on the ANN showed an overall accuracy of 52%, very close to that of SAM (48%). SAM applied using the training sites selected directly from the Hyperion imagery supported by the QuickBird image and the field visits returned an increase accuracy by 16%. Yet, all techniques appeared to suffer from the relatively low spatial resolution of the Hyperion imagery, which affected the spectral separation among the land use/cover classes.     

空-谱角匹配与多指数法相结合的OLI遥感影像水体提取

针对复杂环境条件下水体遥感提取结果不连续且易与植被、建筑物、阴影相混淆的难题,基于Landsat 8 OLI影像,以石家庄市平山县岗南水库和宿迁市骆马湖附近河流为研究区,提出了一种空-谱角匹配与多指数法相结合的水体信息提取方法;并与单波段阈值法、归一化差分水体指数法（NDWI）、光谱角匹配法（SAM）、自动水体提取指数法（AWEI）和一类支持向量机法（OC-SVM）的水体提取结果进行对比分析和精度评定。试验结果表明,本文提出的方法兼顾了多特征之间的互补性优势,引入的空间信息有效地抑制了噪声的干扰,且以像素为基元的提取策略较好地保持了水体的边缘信息,避免了出现平滑掉细节信息的情况;与传统方法相比,本文方法受植被、建筑和阴影的干扰最小,对细小水体也具备较好的识别能力。     

Mapping East African tropical forests and woodlands — A comparison of classifiers

In mapping the forest–woodland–savannah mosaic of Budongo Forest Reserve, Uganda, four classification methods were compared, i.e. Maximum Likelihood classifier (MLC), Spectral Angle Mapper (SAM), Maximum Likelihood combined with an Expert System (MaxExpert) and Spectral Angle Mapper combined with an Expert System (SAMExpert). The combination of conventional classifiers with an Expert System proved to be an effective approach for forest mapping. This was also the first time that the SAMExpert had been used in the mapping of tropical forests. SAMExpert not only maps with high accuracy, but is also fast and easy to use, making it attractive for use in less developed countries. Another advantage is that it can be executed on a standard PC set up for image processing.Combining the conventional classifiers (MLC and SAM) with the Expert System significantly improved the classification accuracy. The highest overall accuracy (94.6%) was obtained with SAMExpert. The MaxExpert approach yielded a map with an accuracy of 85.2%, which was also significantly higher than that obtained using the conventional MLC approach.The SAMExpert classifier accurately mapped individual classes. Of the four classes of woodland mapped, the Open Woodland (with Terminalia) and Wooded Grassland classes were more accurately mapped using SAMExpert. The Open Woodland had been previously identified by ecologists, but had never been mapped.