多光谱蚀变遥感异常提取方法研究

围岩蚀变是热液成矿作用发生过程的一个重要标志.国内外学者为了利用遥感技术提取围岩蚀变信息,开展了多种图像处理方法研究,如主分量阀值分析法、光谱角法、混合像元分解法、高植被覆盖区矿化蚀变信息提取等.本文综述了在地表覆盖类型不同、地表覆盖程度不同的背景下,特别是植被覆盖严重地区的蚀变遥感异常提取的理论依据及异常提取方法的研究现状,并对多光谱蚀变遥感异常提取的发展前景提出一些设想.     

多光谱蚀变遥感异常提取方法研究

围岩蚀变是热液成矿作用发生过程的一个重要标志。国内外学者为了利用遥感技术提取围岩蚀变信息,开展了多种图像处理方法研究,如主分量阀值分析法、光谱角法、混合像元分解法、高植被覆盖区岩石矿化蚀变信息提取等。本文综述了在地表覆盖类型不同、地表覆盖程度不同的背景下,特别是植被覆盖严重地区的蚀变遥感异常提取的理论依据及异常提取方法研究现状,并对多光谱蚀变遥感异常提取的发展前景提出一些设想。     

基于高光谱数据的河北滦平地区岩性信息提取方法试验研究

Hyperion高光谱数据在蚀变矿物填图方面已广泛应用,而在岩性信息提取方面应用较少.本文以河北滦平地区的Hyperion高光谱数据为数据源,通过对预处理后的Hyperion数据进行最小噪声分离变换(MNF),计算纯净像元指数(PPI),N维光谱空间特征端元采集,结合野外光谱采集进行光谱分析识别端元,最后用光谱角(SAM),光谱信息散度(SID)和二值编码(BE)3种方法进行岩性分类.与已知地质图叠加,通过分类图中不同岩石类型颜色边界与地质图岩性界线吻合程度以及与研究区地质解译分类图的对比来比较3种分类方法.结果表明,SAM方法的分类吻合程度高于其他两种方法,SAM方法是一种有效的高光谱遥感岩性分类方法.     

基于波谱匹配的Hyperion数据大气校正方法对比研究

遥感影像数据的大气校正是高光谱遥感地空对比、信息提取的前提和关键,如何根据不同数据、不同研究区、不同研究目的选择合适的大气校正方法是高光谱遥感应用研究的重点和难点。针对EO\|1卫星Hyperion高光谱遥感数据特点和研究区地形环境特征,分别选择线性回归经验模型、基于MODTRAN4模型的FLAASH和基于DEM数据的ACORN\|3模型不同大气校正方法对研究区Hyperion数据进行大气校正。从波谱匹配、识别的目的出发,通过计算不同方法校正后影像像元的波谱曲线与实测地面波谱曲线的匹配程度分析不同大气校正方法的校正效果。     

基于光谱归一化的变组分光谱混合分析(NMESMA)方法及其应用

混合像元问题在低、中分辨率遥感图像中尤为突出,混合像元的存在不仅会影响地物识别和图像分类精度,也是遥感科学向定量化发展的主要障碍之一。因此,遥感图像混合像元分解及其地表覆盖信息的定量提取是近年来研究的热点。针对城市土地覆盖信息的定量提取问题,利用中等分辨率遥感图像(Landsat TM),集成光谱归一化与变组分光谱混合分析(NMESMA)的方法,基于植被-非渗透表面-土壤(V\|I\|S)模型,定量提取研究区植被、土壤和非渗透表面3类土地覆盖的定量信息,并与固定组分的光谱混合分析(LSMA)分解结果进行对比分析。结果表明:基于光谱归一化的变组分光谱混合分析(NMESMA)方法获得的精度高于传统固定组分的光谱混合分析(LSMA)结果,可有效解决光谱异质性较高的城市区域的混合像元问题,为有效提取城市地表覆盖信息,研究城市生态环境变化和模拟分析,提供了有效的信息提取方法。     

植被覆盖区高光谱蚀变矿物填图

根据典型蚀变矿物诊断性波谱特征,在基岩裸露区利用高光谱遥感技术进行蚀变填图研究取得极大的成功。然而,在植被覆盖严重区,利用高光谱技术进行蚀变矿物填图研究成果较为罕见。本文利用河北省承德市大营子地区Hyperion数据为例,在植被覆盖度大于70％地区,基于综合光谱信息模型,把蚀变矿物信息与背景信息置于相同参考水平理念的基础上,开展了典型蚀变矿物填图研究,蚀变矿物填图结果与野外检查结果吻合较好。最后,根据蚀变矿物组合特征,并结合遥感地质解译结果及地质资料,圈定了找矿有利地段。     

高光谱遥感城市植被胁迫监测研究

开发有效的城市植被胁迫监测方法对于林业资源管理和营造良好的城市生态环境具有重要意义。采用EO-1卫星过境广州市东边建成区所采集的Hyperion高光谱影像,通过选取合适的植被指数进行分类,以及混合像元分解获得植被丰度这两种方法进行植被胁迫的识别,对比两者实验结果表明:在植被信息提取中植被丰度的方法要比指数法可靠且精度高;通过地面光谱测量,说明基于植被光谱理论的丰度分析能更好地表示植被胁迫的特征,为城市林业管理提供定性和定量的研究应用。     

EO-1 Hyperion高光谱数据的预处理

针对EO-1 Hyperion高光谱遥感数据的特点，在图像质量检查的基础上，对Hyperion图像进行了未定标和受水汽影响波段的去除、坏线修复、条纹去除、Smile效应降低、大气纠正等预处理，获得了较好质量的图像，为图像的进一步分析和实际应用提供了保障。结果表明图像大气纠正后光谱优化处理能进一步提高图像的质量。     

辽东地区矿化蚀变遥感信息提取的研究和应用

针对工作区(辽东-吉南)植被覆盖率高,河流水体及冲积物等干扰信息多的特点,本文采用比值法、主成分分析法和光谱角制图法相结合,进行大面积遥感矿化蚀变异常信息的提取;在矿化信息分割过程中,引入面向对象的思想,基于实地考察的蚀变信息提取模型,结合矿点、地质构造和遥感图像光谱特征和色彩等多种信息,对图像波段和像元统一进行因子分析和处理,确立切割阈值,克服了主成分变换后主成分分量物理意义不明确的缺点。本次工作建立起植被覆盖地区的遥感矿化蚀变异常提取的一套有效的技术体系,在遥感应用于找矿具有很强的现实意义。     

基于Landsat 的城市热特征研究——以兰州市为例

运用Landsat5和Landsat7获得的遥感数据评估兰州市区的热特征,为了量化城市土地利用和覆盖密度,在对植被-非渗透面-土壤(V-I-S) 模型修正的基础上,运用线性光谱混合分析技术(LSMA),分解了不同种类的地表组成并对非渗透面密度进行了分类。结果表明,研究区有很高的热效应,这种热效应与城市开发密度高相关,并通过植被覆盖信息、非渗透面的空间分布及与其相关联的热特征,可以有效量化城市土地利用、开发密度和热格局。     

Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy

We analyze the capability of Hyperion spaceborne hyperspectral data for discriminating land cover in a complex natural ecosystem according to the structure of the currently used European standard classification system (CORINE Land Cover 2000). For this purpose, we used Hyperion imagery acquired over Pollino National Park (Italy).Hyperion pre-processed data (30 m spatial resolution) were classified at the pixel level using common parametric supervised classification methods. The algorithms' performance and class level accuracy were compared with those obtained for the same area using airborne hyperspectral MIVIS data (7 m spatial resolution).Moreover, in selected test areas characterized by heterogeneous land cover (as mapped by MIVIS classification) a Linear Spectral Unmixing (LSU) technique was applied to Hyperion data to derive the abundance fractions of land cover endmembers. The accuracy of the LSU analysis was evaluated using the Residual Error parameter, by comparing Hyperion LSU results with land cover fractional abundances achieved from reference data (i.e., MIVIS and air-photo classification).The results show the potential of Hyperion spaceborne hyperspectral imagery in mapping land cover and vegetation diversity up to the 4th level of the CORINE legend, even at the sub-pixel level, within a fragmented ecosystem such as that of Pollino National Park. Moreover, we defined a criterion for evaluating the Hyperion accuracy in retrieving land cover abundances at the sub-pixel scale. Sub-pixel analysis allowed us to determine the optimal threshold to select the areas on which consistent fractional land cover monitoring can be achieved using the Hyperion sensor.     

Mapping spatial variations of iron oxide by-product minerals from EO-1 Hyperion

This study aimed to map mine waste piles and iron oxide by-product minerals from an Earth Observing 1 (EO-1) Hyperion data set that covers an abandoned mine in southwest Spain. This was achieved by a procedure involving data pre-processing, atmospheric calibration, data post-processing, and image classification.

In several steps, the noise and artefacts in the spectral and spatial domains of the EO-1 Hyperion data set were removed. These steps include the following: (1) angular shift, which was used to translate time sequential data into a spatial domain; (2) along-track de-striping to remove the vertical stripes from the data set; and (3) reducing the cross-track low-frequency spectral effect (smile). The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm in combination with the radiance transfer code MODTRAN4 was applied for quantification and removal of the atmospheric affect and retrieval of the surface reflectance. The data set was post-processed (filtering, spectral polishing) in order to remove the negative values and noise that were produced as the a result of de-striping and atmospheric calibration. The Mahalanobis distance algorithm is used to differentiate the area covered by mine piles from other main land-use classes. The spatial variations of iron oxide and carbonate minerals within the mine area were mapped using the Spectral Feature Fitting (SFF) algorithm.

The pre-processing of the data and atmospheric correction were vital and played a major role on the quality of the final output. The results indicate that the vertical stripes can be removed rather well by the local algorithm compared to the global method and that the FLAASH algorithm for atmospheric correction produces better results than the empirical line algorithm. The results also showed that the method developed for correcting angular shifts has the advantage of keeping the original pixel values since it does not require re-sampling.

The classification results showed that the mine waste deposits can be easily mapped using available standard algorithms such as Mahalanobis Distance. The results obtained from the SFF method suggest that there is an abundance of different minerals such as alunite, copiapite, ferrihydrite, goethite, jarosite, and gypsum within the mine area. From a total number of 754 pixels that cover the mine area, 43 pixels were classified as sulphide and carbonate minerals and 711 pixels remained unclassified, showing no abundance of any dominant mineral within the area presented by these pixels.     

View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data

Imaging spectrometry has the potential to provide improved discrimination of crop types and better estimates of crop yield. Here we investigate the potential of Hyperion to discriminate three Brazilian soybean varieties and to evaluate the relationship between grain yield and 17 narrow-band vegetation indices. Hyperion analysis focused on two datasets acquired from opposite off-nadir viewing directions but similar solar geometry: one acquired on 08 February 2005 (forward scattering) and the other on 14 January 2006 (back scattering). In 2005, the soybean canopies were observed by Hyperion at later reproductive stages than in 2006. Additional Hyperion datasets were not available due to cloud cover. To further examine the impact of viewing geometry within the same season, Hyperion data were complemented by 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) images (bands 1 and 2) acquired in consecutive days (05-06 February 2005) with opposite viewing geometries (− 42° and + 44°, respectively). MODIS data analysis was used to keep reproductive stage as a constant factor while isolating the impact of viewing geometry. For discrimination purposes, multiple discriminant analysis (MDA) was applied over each dataset using surface reflectance values as input variables and a stepwise procedure for band selection. All possible Hyperion band ratios and the 17 narrow-band vegetation indices with soybean grain yield were evaluated across years through Pearson's correlation coefficients and linear regression. MODIS-derived Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) were evaluated within the same growing season. Results showed that: (1) the three soybean varieties were discriminated with highest accuracy in the back scattering direction, as deduced from MDA classification results from Hyperion and MODIS data; (2) the highest correlation between Hyperion vegetation indices and soybean yield was observed for the Normalized Difference Water Index (NDWI) (r = + 0.74) in the back scattering direction and this result was consistent with band ratio analysis; (3) higher Hyperion correlation results were observed in the back scattering direction when compared to the forward scattering image. For the same reproductive stage, stronger shadowing effects were observed over the MODIS red band in the forward scattering direction producing lower and lesser variable reflectance for the sensor. As a result, the relationship between MODIS-derived NDVI and soybean yield improved from the forward (r of + 0.21) to the back scattering view (r of + 0.60). The same trend was observed for SR that increased from + 0.22 to + 0.58.     

Spectral unmixing of airborne hyperspectral data for baseline mapping of mine tailings areas

The Kam Kotia mine tailings areas near Timmins in Ontario, Canada have been generating and discharging acidic mine drainage (AMD) into the surrounding areas for more than 35 years, killing large areas of forest and polluting the local water system. This paper presents results from the remote sensing monitoring programme in the Kam Kotia mine. Hyperspectral TRW (Thompson Ramo Wooldridge Inc.) Imaging Spectrometer III data were acquired over the Kam Kotia mine and tailings areas. This paper describes (1) the data pre‐processing (noise removal, atmospheric correction, spectral smile correction, scene‐based calibration) needed to radiometrically calibrate the images and (2) a novel procedure which combines constrained spectral mixture analysis and threshold‐based classification. With this developed procedure one can retrieve fraction maps of major mine tailings‐related surface materials and hence generate a surface map separating green vegetation, transition zones, dead vegetation, and oxidized tailings, and calculate the extent (surficial area) of each of the zones. The four zones are correlated with the extent and degree of vegetation cover affected by tailings material and are interpreted to span respectively from very low to medium, high, and very high AMD pollution. This procedure can be used to monitor changes in the course of the boundary between affected zones and finally quantify the rehabilitation process in mine tailings areas with high vegetation cover.     

用混合像元线性模型提取中等植被覆盖区的粘土蚀变信息

基于混合像元线性分解模型,针对中等植被覆盖区提出了一种提取粘土蚀变信息的新方法。主要分3步实现:用混合像元线性分解模型提取植被覆盖丰度;对线性模型进行完善,并依此重构不含有植被信息的新的多波段图像;利用TM5/TM7增强粘土蚀变信息。经验证,提取的植被信息以及粘土类蚀变信息与实际吻合较好,与基于比值-主成分分析的方法相比有明显的优越性。
     

近20多年来赣州地区稀土矿区遥感动态监测

稀土资源是现代科技所需的重要资源,由于其有很高的经济价值,稀土资源的开采活动越来越频繁,过度开采现象严重,对稀土矿区的实时监控成为保护环境资源的重要环节。遥感技术在监测土地利用变化方面已经有了完善的技术方法。相较于普遍使用的Landsat-TM/ETM+数据,我国研发的HJ卫星(中国环境与灾害监测预报小卫星)数据具有更短的重访周期,能够对稀土矿的开采进行更加有效的检测。通过结合Landsat-TM/ETM+与HJ-1/CCD数据,根据矿区植被覆盖度的变化及时监测稀土矿区活动情况,对江西定南地区20a来稀土矿区开采变化情况进行监测,并提出保护建议,为实现矿产资源的可持续发展提供理论依据。     

定量消除植被影响的补偿置换方法研究

针对植被覆盖区的岩性识别,本文在线性混合象元分解的基础上通过对ＴＭ影象中混和象元进行补偿置换,即用象元中非植被地物光谱信息置换植被部分,裸露象元在此过程中没有变化,高植被象元（包括全植被象元）被掩膜,从而达到定量消除植被影响的目的,同时混合象元中非植被信息得到增强。     

基于宽波段和窄波段植被指数的草地LAI反演对比研究

叶面积指数是一个重要的植被生理生态参数,为探讨不同植被指数反演叶面积指数的可行性,基于同空间分辨率不同光谱分辨率的HJ\|1B CCD1和Hyperion遥感影像数据,以内蒙古自治区赤峰市克斯克腾旗贡格尔草原为研究对象,选取几种常见宽波段植被指数和高光谱窄波段植被指数并结合4种常用回归模型,比较分析了不同植被指数反演叶面积指数的精度。结果表明:对于全部植被指数而言,PVI、MSAVI等综合考虑了土壤、环境等因素的植被指数较传统植被指数NDVI、RVI反演草地LAI精度更高。通过对比发现,在反演草地LAI方面,窄波段植被指数比宽波段植被指数表现出明显的优势。其中,窄波段垂直植被指数PVI验证模型的确定性系数R²为0.65,均方根误差RMSE为0.15,说明实测LAI和模拟LAI值之间具有较好的变化一致性。最后基于Hyperion影像和窄波段垂直植被指数PVI的估算模型生成研究区叶面积指数空间分布图。