一种基于PPI的高光谱数据矿物信息自动提取方法

本文通过分析PPI算法后续处理存在的问题,引入最大距离法(MD)实现基于PPI的端元自动分类,并将获得的未知端元在波谱库中遍历以匹配最佳地类,最终完成基于PPI端元的矿物信息的自动提取。实验采用美国内华达州Cuprite地区的机载AVIRIS和我国东天山地区的星载Hyperion高光谱遥感数据,利用IDL编程实现矿物信息的自动提取,通过对实验结果的比较分析,验证了本文方法的有效性和实用性。     

基于像元二分模型的地表植被覆盖度估算模块化设计与实现

地表植被覆盖度是的一种应用广泛的定量遥感产品,在水文、生态、区域变化等方面都具有重要的意义。像元二分模型是应用最多的一种遥感估算地表覆盖度的方法。目前,用遥感的方法进行地表植被覆盖度估算没有完整、系统的工具,用户只能逐步进行操作,效率低下,鉴于上述情况,本文运用IDL交互式数据语言,基于ENVI二次开发了一个植被覆盖度估算程序,取得了一定的成效,对遥感定量产品的生产、应用具有一定意义。     

Detection of dryland degradation using Landsat spectral unmixing remote sensing with syndrome concept in Minqin County,China

This study was to detect dryland degradation coupling linear spectral unmixing model of Landsat images with syndrome concept in temperate dryland system, Minqin, China. The phenological contrast and complementation between green vegetation fraction in summer, sandland fraction and saline land fraction in spring, was firstly structured to quantify degradation characteristics by simple correlation analysis with ground data. The spatiotemporal patterns of the three degradation indicators were interpreted with the help “dust bowl” syndrome, qualitatively deciphered the degradation causal clusters, loops and important consequences in the study area. The results indicate water-using and distribution pattern was changed, agricultural intensity and productivity increased, salinization lessened in oasis, whereas sandification risk heightened. This approach developed in this study, has the potentially broad applicability, for dryland system monitoring and modelling.     

流域尺度的不透水面遥感提取

一个地区的不透水面覆盖度不仅是该地区城镇化程度重要指示因子,也是该地区生态环境状况的重要指示因子.现有的不透水面遥感提取方法,多集中在城区尺度上.而流域尺度上快速、准确的不透水面遥感提取方法在国内外还鲜有研究.本研究以覆盖海河流域同一季节的Landsat影像为数据源,利用已有土地利用数据集中的道路、城市、农村和工业用地...     

遥感尺度内涵及遥感像元尺度效应分析的改进分形方法

本文首先分析了遥感影像尺度的三层次内涵。重点针对遥感像元尺度,分析了遥感像元尺度效应及其分形机理,由于现有分形方法没有考虑影像本身尺度(空间分辨率),造成尺度间分形维数的比较时像元尺度效应变化难以有效反映,本文针对此问题提出了基于表面积的加窗分形布朗运动和加窗双层地毯两种改进分形方法。为验证改进分形方法的可靠性,采用了不同像元尺度下系列监督分类进行验证。试验结果表明,每种地物的分维数都随着空间分辨率的降低或像元尺度的缩小,呈总体下降趋势,在某些特征尺度上会出现预示着某些地物结构的拐点,这些拐点对观测该区域地物具有一定指示意义。系列监督分类精度也一定程度上证明了以上两种改进分形方法在分析尺度效应中的可行性。因此本文的方法对于分析遥感像元尺度效应和探索地物尺度聚合规律具有一定的理论意义。     

A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis,United States

This study developed an analytical procedure based upon a spectral unmixing model for characterizing and quantifying urban landscape changes in Indianapolis, Indiana, the United States, and for examining the environmental impact of such changes on land surface temperatures (LST). Three dates of Landsat TM/ETM+ images, acquired in 1991, 1995, and 2000, respectively, were utilized to document the historical morphological changes in impervious surface and vegetation coverage and to analyze the relationship between these changes and those occurred in LST. Three fraction endmembers, i.e., impervious surface, green vegetation, and shade, were derived with an unconstrained least-squares solution. A hybrid classification procedure, which combined maximum-likelihood and decision-tree algorithms, was developed to classify the fraction images into land use and land cover classes. Correlation analyses were conducted to investigate the changing relationships of LST with impervious surface and vegetation coverage. Results indicate that multi-temporal fraction images were effective for quantifying the dynamics of urban morphology and for deriving a reliable measurement of environmental variables such as vegetation abundance and impervious surface coverage. Urbanization created an evolved inverse relationship between impervious and vegetation coverage, and brought about new LST patterns because of LST's correlations with both impervious and vegetation coverage. Further researches should be directed to refine spectral mixture modeling by stratification, and by the use of multiple endmembers and hyperspectral imagery.     

结合SOM神经网络和混合像元分解的高光谱影像分类方法研究

本文对SOM神经网络算法进行改进,在标类的过程中采用3个策略加以控制,对初始产生的自组织映射图进行调整。通过改进,那些映射到可靠神经元的像素得到了很好的分类,而那些映射到不可靠神经元的像素都被作为不可分像元而提取出来。继而,从混合像元分解的角度来对这些不可分像元进行处理,按类型分解的思想确定混合像元的类别,实现对不可分像元的分类。将SOM神经网络和混合像元分解相结合的分类方法应用于高光谱图像的分类中,通过实验表明了该方法能较好地改善分类效果,提高分类精度。     

基于高光谱Hyperion数据的线性光谱混合模型与神经网络模型的比较

混合像元问题是定量遥感中的热点问题之一,为了改进从遥感数据中提取定量信息,人们建立了各种混合光谱分解技术,其中线性光谱混合模型和神经网络模型就是两种比较成熟的方法。以陕西省横山地区的高光谱Hyperion数据为研究基础,通过最小噪声变换(MNF)、像元纯度指数(PPI)转换和RMS误差分析的迭代方法相结合提取影像中的纯净像元作为终端端元。分别运用神经网络模型和线性光谱混合模型对影像进行光谱分解,得到各个组分的分解图像。以标准植被指数(NDVI)影像为衡量标准,选取训练样本点,分别对两种模型进行回归分析,结果显示NDVI影像与线性光谱混合模型植被分解图像的判定系数(R2=0.91)要大于其与神经网络模型的判定系数(R2=0.81)。进一步分析表明在一般情况下,线性光谱混合模型具有比神经网络模型略高的分离精度,但是神经网络模型对细部信息的提取的效果要好于线性光谱混合模型,最后提出了端元均方根误差(EAR)指数,一种新的混合像元分解的思路。     

论第二次土地调查与土地详查的主要区别

本文针对第二次全国土地调查任务与特点,阐述了土地详查与第二次土地调查在调查技术手段,土地分类标准,面积量算与检查方式等方面存在的主要差异,以利于更好的理解与掌握第二次土地调查中的相关技术要点,提高土地调查的效率与成果质量。