卫星遥感数字图像的地面辐射改正研究

讨论了地形、大气以及地理位置对卫片像元土尤辐射三分量(直射、散射、邻坡反射)的影响和这种影响所引起的卫星遥感数据变化。研究了地面光辐射、地形、大气和遥感数据之间的定量关来，并以此为基础，研究了卫星遥感数字图像地面辐射改正的原理和方法。经地面辐射改正后的卫星遥感数据能满足遥感应用基础研究和地面辐射状况研完的需要。     

热红外遥感地表温度反演研究现状与发展趋势

从Planck函数和热红外辐射传输方程出发,概述了热红外遥感反演地表温度的基本原理,总结了当前反演地表温度常用到的热红外遥感器及相应波段。将热红外遥感地表温度反演算法分为单通道、劈窗和多通道3大类,分析了每一类中较具代表性算法的原理、适用条件及精确度。从热红外遥感机理、发射率、环境辐射、混合像元、大气影响等方面概述了热红外遥感反演地表温度面临的主要问题,并对热红外遥感地表温度反演的发展趋势进行了展望。     

中国不同土地利用类型分光辐射地表反照率研究

选取2000～2015年遥感反演的地表反照率数据及2000和2015年两期土地利用数据,采用经典统计学方法分析不同土地利用类型分光辐射地表反照率的特征及其年际变化趋势。为了解土地利用类型的反照率特征、认知区域气候或陆面模式中能量模块的相关分光辐射变量的物理过程提供科学依据。结果表明:不同的土地利用类型具有差异明显的地表反照率特征,同种土地利用类型地表反照率的差异甚至超过了不同类别土地利用类型之间的差异,说明了地表反照率巨大的空间异质性;大部分土地利用类型在短波总辐射及分光辐射地表反照率满足:近红外短波可见光,说明短波地表反照率的上限值更大程度上取决于近红外波段的地表反照率;研究时段内,各土地利用类型在3个波段地表反照率分别呈现出了不同的变化趋势,但是大部分土地利用类型分光辐射地表反照率的年际变化速率较小,基本保持稳定。     

高光谱场景辐亮度模型仿真研究

高光谱遥感的地面场景是高光谱遥感系统中影响因素最复杂多变的部分。首先基于星载高光谱遥感成像的辐射传输过程,对非均匀的朗伯表面的入瞳处大气辐亮度传输模型进行了研究,得到只需要考虑目标与邻近像元反射率,大气传输因子的辐亮度简化模型。之后介绍了大气中光子扩散原理,并采用蒙特卡洛方法对大气点扩散函数进行仿真;联合地表目标像元反射率数据计算得到基于非均匀朗伯面地表的邻近像元反射率;然后总结了大气传输模型软件MODTRAN计算入瞳处辐亮度数据的原理步骤,并利用其反演了朗伯表面的相关大气传输参数。最终利用基于传感器入瞳处的辐亮度数据表征了高光谱地面场景。     

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。     

高光谱图像非线性解混方法的研究进展

由于空间分辨率的限制,高光谱遥感图像中存在大量混合像元,对混合像元的解混是实现地物精确分类和识别的前提。与传统的线性解混方法相比,非线性解混方法在寻找组成混合像元的端元以及每个端元的丰度时具有较高的精度。分析了光谱非线性混合的原理,总结了近年来提出的非线性解混算法,重点对双线性模型、神经网络、基于核函数的非线性解混算法以及基于流形学习的非线性解混算法进行了介绍和分析。最后总结了混合像元非线性解混未来发展的趋势。     

城市地表端元丰度与地表亮温关系

由于城市人口急剧增长,城市化进程成为全球变暖的主要贡献来源:城市裸露、半裸露土地和不透水面温度较高。针对城市地表物质丰度与温度的关系,以武汉市2013年夏季Landsat-8遥感数据为数据源,使用多端元光谱混合分析法动态提取每个像元中的植被-不透水面-土壤(V-I-S)端元组分,并采用大气顶层辐射值反演地表亮温,通过聚类分析典型地表物质类型的地表亮温区位表现,结合三角图分析每个像元内的端元丰度与相应地表亮温之间的关系,拟合多元回归方程探究端元丰度对地表温度的影响能力,以及地表亮温对端元丰度数值变化的表现。实验结果表明,V-I-S端元丰度情况可解释夏季地表亮温98.563%的变化,其中不透水面丰度对夏季亮温的影响最大,而植被丰度的大小与植被温度调节能力成正比。     

山地TM遥感影像大气辐射校正模型改进及地表反射率反演

基于光学遥感辐射传输理论,着重阐述了地形对天空散射光相互作用及邻近像元的影响,提出了一种改进的山地大气辐射校正模型及地表反射率反演方法;基于IDL编程实现模型算法,选择贵州黎平县丘陵森林覆盖典型研究区,结合Landsat-5TM和1∶50000DEM数据进行了实例验证、评价与分析。研究结果表明,本研究方法能够同时有效消除TM数据的大气与地形影响,提高地表反射率反演精度与数据质量,将进一步推动山地光学遥感数据的定量分析与应用。     

三维结构真实遥感像元场景的生成

遥感像元场景模型是遥感机理研究的关键组成,通过调查光与遥感像元场景的相互作用,可以帮助人们理解遥感信号产生的机理,并验证遥感物理模型。从植被野外测量、三维结构真实遥感像元场景的参数化描述、场景数据结构、场景生成方法和流程等方面阐释了遥感像元场景模型。植被的野外测量和统计是结构真实场景的基础;场景生成方法为：（1）使用L系统生成结构真实植株,进而生成遥感像元场景;（2）按统计规则直接生成遥感像元场景。试验表明该遥感像元场景模型可以生成符合遥感像元统计规律的三维场景,是准确和便捷地计算光与植被相互作用的可靠基础。     

遥感图像边缘检测的不确定性及其处理方法探讨

遥感图像边缘检测是遥感图像处理领域的难点和热点,多种不确定性因素造成边缘像元的位移和变形,影响了遥感图像边缘检测的进展。本文较系统地总结了影响遥感图像边缘检测的不确定性因素,从噪声、随机性和模糊性、尺度效应、混合像元以及光谱的不确定性等几个方面进行具体阐述,并指出了遥感图像边缘检测不确定性的研究重点与发展方向。     

基于地质统计学纹理的遥感影像分类研究进展

传统的基于像素与像素基础上的遥感影像光谱分类方法忽视了邻近像素值之间潜在有用的空间信息,三十多年来,人们一直都在谋求利用遥感影像本身所固有的空间信息以加强光谱分类,尽管从事该方面研究的人一直都很少,其实现的手段主要依靠对原始影像的滤波,滤波的一般方式是生成纹理波段以指导接下来的分类。近年来,变异函数被用来表达空间依赖性,并取代简单的方差滤波成为了纹理分类的主要手段,在这篇综述性的论文中,笔者主要讨论了两类将基于地质统计学的纹理信息集成到遥感影像分类中的应用,它们代表了当前遥感影像纹理分类的主流。     

Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images

Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. However, the resulting super-resolution land-cover maps often have uncertainty as no information about sub-pixel land-cover patterns within the low-resolution pixels is used in the model. Accuracy can be improved by incorporating supplemental datasets to provide more land-cover information at the sub-pixel scale; but the effectiveness of this is limited by the availability and quality of these additional datasets. In this paper, a novel super-resolution land-cover mapping technology is proposed, which uses multiple sub-pixel shifted remotely sensed images taken by observation satellites. These satellites take images over the same area once every several days, but the images are not identical because of slight orbit translations. Low-resolution pixels in these remotely sensed images therefore contain different land-cover fractions that can provide useful information for super-resolution land-cover mapping. We have constructed a Hopfield Neural Network (HNN) model to solve it. Maximum spatial dependence is the goal of the proposed model, and the fraction maps of all images are constraints added to the energy function of HNN. The model was applied to synthetic artificial images as well as to a real degraded QuickBird image. The output maps derived from different numbers of images at different zoom factors were compared visually and quantitatively to the super-resolution map generated from a single image. The resulting land-cover maps with multiple remotely sensed images were more accurate than was the single image map. The use of multiple remotely sensed images is therefore a promising method for decreasing the uncertainty of super-resolution land-cover mapping. Moreover, remotely sensed images with similar spatial resolution from different satellite platforms can be used together, allowing a fusion of information obtained from remotely sensed imagery.     

Technical note On estimating measurement error in remotely-sensed images with the variogram

Previously, several methods have been developed to estimate the signal-to-noise ratio of remotely sensed imagery. Of these, the most appropriate is a method based on spatial dependence for estimating the signal-to-noise ratio of Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) imagery. The intercept on the ordinate of the modelled sample variogram, known as the nugget variance, is used to estimate noise. However, while the nugget variance is due to measurement error, it depends also on short-range spatial variation that has not been measured, underlying variation that has been measured (but which may result in a non-linear form of variogram near the ordinate), sampling effects, and the choice of model fitted to the sample variogram. For remotely-sensed imagery there is no short-range variation that has not been measured because the pixels are contiguous or overlapping. Further, there are usually many pixels and so sampling effects are negligible. However, it is impossible to account for the form of the variogram near the ordinate when selecting a mathematical model. Consequently, while the nugget variance remains as the most appropriate method of estimating measurement error in remotely sensed images, it may be less reliable than previously thought.     

Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing

Machine Learning - Spatial autocorrelation is inherent to remotely sensed data. Nearby pixels are more similar than distant ones. This property can help to improve the classification performance,...     

Restoration of clouded pixels in multispectral remotely sensed imagery with cokriging

The presence of clouds and their shadows in remotely sensed images limits their potential uses for extracting information. The commonly used methods for replacing clouded pixels by land cover reflection estimates usually yield poor results if the images being combined exhibit radical differences in target radiance due, for example, to large date separation and high temporal variability. This study focuses on introducing geostatistical techniques for interpolating the DN values of clouded pixels in multispectral remotely sensed images using traditional ordinary cokriging and standardized ordinary cokriging. Two case studies were conducted in this study. The first case study shows that the methods work well for the small clouds in a heterogeneous landscape even when the images being combined show high temporal variability. Although the basic spatial structure in large size clouds can be captured, image interpolation‐related artefacts such as smoothing effects are visually apparent in a heterogeneous landscape. The second case study indicates that the cokriging methods work better in homogenous regions such as the dominantly agricultural areas in United States Midwest. Various statistics including both global statistics and local statistics are employed to confirm the reliability of the methods.     

Non-linear mixture modelling without end-members using an artificial neural network

Many methods of analysing remotely sensed data assume that pixels are pure, and so a failure to accommodate mixed pixels may result in significant errors in data interpretation and analysis. The analysis of data containing a large proportion of mixed pixels may therefore benefit from the decomposition of the pixels into their component parts. Methods for unmixing the composition of pixels have been used in a range of studies and have often increased the accuracy of the analyses. However, many of the methods assume linear mixing and require end-member spectra, but mixing is often non-linear and end-member spectra are difficult to obtain. In this paper, an alternative approach to unmixing the composition of image pixels, which makes no assumptions about the nature of the mixing and does not require end-member spectra, is presented. The method is based on an artificial neural network (ANN) and shown in a case study to provide accurate estimates of sub-pixel land cover composition. The results of this case study showed that accurate estimates of the proportional cover of a class and its areal extent may be made. It was also shown that there was a tendency for the accuracy of the unmixing to increase with the complexity of the network and the intensity of training. The results indicate the potential to derive accurate information from remotely sensed data sets dominated by mixed pixels.     

Managing uncertainty when aggregating from pixels to objects: habitats,context-sensitive mapping and possibility theory

Object-oriented remote sensing software provides the user with flexibility in the way that remotely sensed data are classified through segmentation routines and user-specified fuzzy rules. This paper explores the classification and uncertainty issues associated with aggregating detailed ‘sub-objects’ to spatially coarser ‘super-objects’ in object-oriented classifications. We show possibility theory to be an appropriate formalism for managing the uncertainty commonly associated with moving from ‘pixels to parcels’ in remote sensing. A worked example with habitats demonstrates how possibility theory and its associated necessity function provide measures of certainty and uncertainty and support alternative realizations of the same remotely sensed data that are increasingly required to support different applications.     

Using genetic algorithms in sub-pixel mapping

In remotely sensed images, mixed pixels will always be present. Soft classification defines the membership degree of these pixels for the different land cover classes. Sub-pixel mapping is a technique designed to use the information contained in these mixed pixels to obtain a sharpened image. Pixels are divided into sub-pixels, representing the land cover class fractions. Genetic algorithms combined with the assumption of spatial dependence assign a location to every sub-pixel. The algorithm was tested on synthetic and degraded real imagery. Obtained accuracy measures were higher compared with conventional hard classifications.     

利用正演计算方法分析地形和噪音对多波段遥感数据的影响

在没有干扰和噪音影响的理想条件下,多波段遥感数据中不同地物在散点图上的位置是一
个点。但在实际情况中,地形和噪音大大地改变了目标分布形状。利用DEM数字高程数据以及太
阳高度角和方位角等参数计算出地表接收到的辐射能量,然后设置已知目标,利用散点图来分析不
同反射率下目标所占的空间位置。再通过增加不同强度的噪音,分析目标在散点图上的变化规律,
以便寻找目标分离的有效特征。