多光谱多角度遥感数据综合反演叶面积指数方法研究

叶面积指数是陆地生态系统的一个十分重要的结构参数。用遥感数据求取叶面积指数可以利用光谱的信息,比如通过植被指数来拟合一个经验关系,但很多植被指数明显受土壤背景的影响,对于有明显行结构的农作物,土壤的影响很难消除,植被指数的方法误差较大。多角度遥感包含了大量的地面目标的立体结构信息,具备求解植被特征参数的潜力,但通常多角度遥感反演对光谱信息的利用不足。与以往的反演方法相区别,该文利用行播作物二向反射模型,将多角度与多光谱数据结合进行行播作物LAI反演实验,并对反演算法进行了详细的敏感性分析实验,结果表明采用多角度、多光谱遥感数据相结合的方法可以有效反演行播作物的叶面积指数。     

高分辨率影像解译理论与应用方法中的一些研究问题

近年来，不断发展的遥感技术使遥感数据呈现出高空间分辨率、高光谱分辨率和高时间采集频率的特点。卫星图像空间分辨率已经提高到0．6m级，而航空遥感数字影像分辨率高达0．1m以上。光谱分辨率高达3—4nm。不断发展的高分辨率遥感数据能够提高信息提取和监测精度，并拓展遥感数据的应用范围。目前，国外已经加快对高分辨率图像，特别是高空间分辨率影像，在城市环境、精准农业、交通及道路设施、林业测量、军事目标识别和灾害评估中的应用。但是总的情况是自动化程度不高。介绍高空间分辨率影像信息提取、高光谱和偏振影像信息提取、影像数据融合和高分辨率遥感变化探测等方面迫切需要研究的一些科学问题及其意义。建议建立图像知识库，改善数据共享环境，为有志于从事这方面研究的学者提供参考。     

Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment

Current disaster management procedures to cope with human and economic losses and to manage a disaster’s aftermath suffer from a number of shortcomings like high temporal lags or limited temporal and spatial resolution. This paper presents an approach to analyze social media posts to assess the footprint of and the damage caused by natural disasters through combining machine-learning techniques (Latent Dirichlet Allocation) for semantic information extraction with spatial and temporal analysis (local spatial autocorrelation) for hot spot detection. Our results demonstrate that earthquake footprints can be reliably and accurately identified in our use case. More, a number of relevant semantic topics can be automatically identified without a priori knowledge, revealing clearly differing temporal and spatial signatures. Furthermore, we are able to generate a damage map that indicates where significant losses have occurred. The validation of our results using statistical measures, complemented by the official earthquake footprint by US Geological Survey and the results of the HAZUS loss model, shows that our approach produces valid and reliable outputs. Thus, our approach may improve current disaster management procedures through generating a new and unseen information layer in near real time.     

Spectral conversion of surface albedo derived from meteosat first generation observations

Comparison of surface albedos derived from spaceborne radiometers with different spectral bands requires, first of all, the conversion of these quantities into common spectral intervals. This letter proposes a spectral conversion method specifically dedicated to surface albedo derived in a large-band instrument such as the solar channel onboard the Meteosat first-generation radiometer. This new method accounts for the retrieval algorithm assumptions and radiometer spectral limitations that might have an impact on the retrieved surface albedo in such a large band. It is also shown that the proposed approach has no impact when surface albedo is derived in narrow bands and confirms the results of previously published spectral conversion methods.     

Converting local spectral and spatial information from a priori classifiers into contextual knowledge for impervious surface classification

A classification model was demonstrated that explored spectral and spatial contextual information from previously classified neighbors to improve classification of remaining unclassified pixels. The classification was composed by two major steps, the a priori and the a posteriori classifications. The a priori algorithm classified the less difficult image portion. The a posteriori classifier operated on the more challenging image parts and strived to enhance accuracy by converting classified information from the a priori process into specific knowledge. The novelty of this work relies on the substitution of image-wide information with local spectral representations and spatial correlations, in essence classifying each pixel using exclusively neighboring behavior. Furthermore, the a posteriori classifier is a simple and intuitive algorithm, adjusted to perform in a localized setting for the task requirements. A 2001 and a 2006 Landsat scene from Central New York were used to assess the performance on an impervious classification task. The proposed method was compared with a back propagation neural network. Kappa statistic values in the corresponding applicable datasets increased from 18.67 to 24.05 for the 2006 scene, and from 22.92 to 35.76 for the 2001 scene classification, mostly correcting misclassifications between impervious and soil pixels. This finding suggests that simple classifiers have the ability to surpass complex classifiers through incorporation of partial results and an elegant multi-process framework.     

Visual method for spectral band selection

We present a new method for performing band selection experiments with spectral data. This method allows for the visual inspection and assessment of the experiment results, and includes a statistical significance test. The method follows a standard feature selection approach in which a multivariate distance measure is used as a figure of merit in a search-optimization procedure. For this letter, we have chosen the Jeffries-Matusita distance between each sample and its immediate background. The band selection methodology uses either an exhaustive search over all possible combinations of 1-4 bands or sequential forward selection. To analyze the band selection results, we count the number of times that each band is selected as a member of the best set by the protocol, and we plot the results as a band frequency histogram. This allows us to visually discern spectral patterns that are not evident otherwise, and thus better assess the utility of each spectral band. We can compute band frequency histograms over individual classes of samples or over groups of classes. In addition, we can compute a significance statistic that gives us the probability that a given histogram is not the result of random band selection outcomes.     

基于地物空间信息的浮动先验概率最大似然分类研究

利用遥感影像对地物进行分类识别时，既需要考虑地物波谱信息，也需要考虑其空间信息。现有遥感分类方法主要集中在利用像素的波谱信息，对各个像素进行独立分类，忽略了地物空间信息。考虑到传统最大似然分类（MLC）方法包括先验概率和条件概率密度函数两个核心环节，提出基于空间信息的浮动先验概率MLC方法，融合空间信息和波谱信息，以提高分类精度。在分析地物空间信息的基础上，总结了基于空间信息的浮动先验概率确定原则和依据，包括地物几何空间特征、情景特征、临近像素空间自相关定律、景观参数等，并设计了基于地物空间特征和临近像素空间自相关定律的浮动先验概率确定算法和分类流程。通过分类试验和误差矩阵分析，结果表明：基于空间特征和临近像素空间自相关定律的浮动先验概率MLC方法，能够融合地物的波谱信息和空间信息，克服最小距离、MLC等基于像素波谱信息的分类方法的缺点，显著提高地物分类精度。     

A band selection technique for spectral classification

In hyperspectral remote sensing, sensors acquire reflectance values at many different wavelength bands, to cover a complete spectral interval. These measurements are strongly correlated, and no new information might be added when increasing the spectral resolution. Moreover, the higher number of spectral bands increases the complexity of a classification task. Therefore, feature reduction is a crucial step. An alternative would be to choose the required sensor bands settings a priori. In this letter, we introduce a statistical procedure to provide band settings for a specific classification task. The proposed procedure selects wavelength band settings which optimize the separation between the different spectral classes. The method is applicable as a band reduction technique, but it can as well serve the purpose of data interpretation or be an aid in sensor design. Results on a vegetation classification task show an improvement in classification performance over feature selection and other band selection techniques.     

Crisp and Fuzzy Adaptive Spectral Predictions for Lossless and Near-Lossless Compression of Hyperspectral Imagery

This letter presents an original approach that exploits classified spectral prediction for lossless/near-lossless hyperspectral-image compression. Minimum-mean-square-error spectral predictors are calculated, one for each small spatial block of each band, and are classified (clustered) to yield a user-defined number of prototype predictors that are capable of matching the spectral features of different classes of pixel spectra for each wavelength. Such predictors are used to achieve a prediction, either crisp or fuzzy. Unlike most of the methods reported in the literature, the proposed approach exploits a purely spectral prediction that is suitable in compressing the data in band-interleaved-by-line format, as they are available at the output of the onboard instrument. In that case, the training phase, i.e., clustering and refining of predictors for each wavelength, may be moved offline. Experimental results on Airborne Visible InfraRed Imaging Spectrometer data show improvements over the most advanced methods in the literature, with a computational complexity that is far lower than that of analogous methods by the same and other authors.     

Feature Identification via a Combined ICA–Wavelet Method for Image Information Mining

Image transformation is required for color–texture image segmentation. Various techniques are available for the transformation along the spatial and spectral axes. For instance, the HSV–wavelet technique is shown to be very effective for image information mining in remote-sensing applications. However, the HSV transformation approach uses only three spectral bands at a time. In this letter, a new feature set, obtained by combining independent component analysis and wavelet transformation for image information mining in geospatial data, is presented. Experimental results show the effectiveness of the presented method for image information mining in Earth observation data archives.      

一种融合光谱匹配和张量分析的高分辨率遥感影像目标探测器

提出了一种基于光谱分析理论和张量代数理论的高分辨率遥感影像目标探测器。首先在向量空间中对目标光谱进行特征匹配;然后对光谱特征与目标相似的像素进行空间\\|光谱特征一体化张量描述,进而在张量特征空间中对目标和背景进行分类。实验表明,张量学习机能够有效地对高分辨率影像进行目标识别。加入光谱特征匹配后的目标探测器能够极大地降低目标探测时间,同时进一步提高目标探测精度。     

Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring

Monitoring Earth dynamics using current and future satellites is one of the most important objectives of the remote sensing community. The exploitation of image time series from sensors with different characteristics provides new opportunities to increase the knowledge about environmental changes and to support many operational applications. This paper presents an image fusion approach based on multiresolution and multisensor regularized spatial unmixing. The approach yields a composite image with the spatial resolution of the high spatial resolution image while retaining the spectral and temporal characteristics of the medium spatial resolution image. The approach is tested using images from Landsat/TM and ENVISAT/MERIS instruments, but is general enough to be applied to other sensor pairs. The potential of the proposed spatial unmixing approach is illustrated in an agricultural monitoring application where Landsat temporal profiles from images acquired over Albacete, Spain, in 2004 and 2009 are complemented with MERIS fused images. The resulting spatial resolution from Landsat allows monitoring small and medium size crops at the required scale while the fine spectral and temporal resolution from MERIS allow a more accurate determination of the crop type and phenology as well as capturing rapidly varying land-cover changes.     

遥感反演中不确定性信息处理的一种数学方法

在遥感反演中对先验知识的表达和应用方法是多阶段目标反演中急需解决的关键问题。论述一种在遥感反演中处理不确定性信息的一种数学方法 ,引入未确知有理数和盲数的概念和运算方法 ,定量计算反演参数在可能取值区间上反演前后的可信度及其改变量。为遥感反演中先验知识的积累、更新和应用提供定量的方法依据。     

Hyperspectral Pixel Unmixing via Spectral Band Selection and DC-Insensitive Singular Value Decomposition

This letter proposes a linear two-class hyperspectral pixel-unmixing algorithm that uses a band selection method to determine the best bands for pixel unmixing, low-pass prefiltering to remove high-frequency content, and a new version of the well-known singular value decomposition (SVD) method, which is insensitive to dc offsets (DCI-SVD). The proposed method is compared to the best level discrete wavelet transform approach for dimensionality reduction and least squares estimation and quadratic programming for unmixing (DWT-LSE-QP). The contributions of this letter are given as follows: (1) the band selection and filter selection algorithm and (2) the DCI-SVD algorithm. The dc insensitivity of the DCI-SVD method is proven, and simulation results using data from an analytical spectral device spectroradiometer show the efficacy of the proposed method and its superiority to the DWT-LSE-QP-based approach in the harder unmixing cases.     

Contextual Spatiospectral Postreconstruction of Cloud-Contaminated Images

A general method has been proposed recently for the contextual reconstruction of cloud-contaminated areas in multitemporal multispectral images. It is based on the idea of making the prediction process learn from information available in the cloud-free neighborhood of contaminated areas. Though promising, this method does not fully exploit all available information, thus leaving room for further methodological enhancements. This letter presents a postreconstruction methodology for improving the contextual reconstruction process by opportunely capturing spatial and spectral correlations characterizing the considered image. In addition, we propose a solution to a problem that has not yet been addressed in the remote sensing literature, i.e., the generation of an error map beside the reconstructed images to provide end-users with helpful indications about reconstruction reliability. Thorough experiments conducted on a multitemporal sequence of Landsat-7 ETM+ images are reported and discussed.     

天宫一号高光谱数据相关检测法蚀变信息提取

天宫一号高光谱成像仪是目前中国空间分辨率和光谱综合指标最高的星载光谱成像仪.针对天宫一号高光谱数据,提出了一种基于波段相关检测的蚀变信息提取方法.该方法在对天宫一号高光谱数据进行波段相关检测提取蚀变异常特征波段的基础上,结合异常探测、端元提取和光谱匹配等方法的各自优势进行天高一号高光谱数据的蚀变信息提取.利用该法成功提取了天宫一号高光谱数据中的Al-OH矿物蚀变异常及4种蚀变矿物,结果表明该方法可以有效提取天宫一号高光谱数据中的蚀变信息,有助于天宫一号高光谱数据在地质矿产资源调查中发挥巨大的应用潜力.     

高光谱目标探测中的空间和光谱尺度效应

高光谱遥感目标探测主要利用目标和背景的光谱特征差异进行目标识别。一般情况下,影像的空间和光谱分辨率越高,探测效果越好。但多数情况下空间和光谱分辨率难以同时满足需求。针对该问题,本文利用Field Imaging Spectrometer System(FISS)地面高光谱成像仪器,通过在稀疏草地上布设人工绿色目标,研究了目标和背景光谱相似情况下,单一均匀背景下小目标探测问题,提出空间和光谱尺度定量分析方法,得到目标探测适用的空间和光谱尺度。结果表明:(1)利用FISS高光谱仪器进行人工目标探测,所需的空间分辨率约为目标尺寸的2倍以内;(2)当光谱分辨率优于40 nm时,目标和背景的两个主要特征:反射峰的位置和波段趋势差异均可被描述,在原始空间分辨率5倍(0.85 cm)以内,探测精度可以达到0.94以上。由于反射峰间距20 nm,当光谱分辨率低于40 nm时,该特征消失,造成探测精度的下降;(3)当光谱分辨率低于40 nm时,选取目标、背景光谱特征差异较大的波段可提高探测的有效性,在舍弃目标背景相似波段后,探测精度上升,得到本实验的最佳波段组合为红、绿、蓝、黄及红边波段。     

论空间数据挖掘和知识发现的理论和方法

针对现有空间异常探测方法在构建空间邻近域以及准确探测各类空间异常模式方面的局限性,本文提出了一种基于多层次专题属性约束的空间异常探测方法(spatial outlier detection by considering multi-level thematic attribute constraints,MTACSOD)。首先采用层次约束Delaunay三角网构建合理、稳定的空间邻近域;进而根据空间邻接实体间的专题属性距离,针对各空间连通子图施加全局和局部约束;最后通过一个异常模式识别指标提取各类空间异常模式。该方法不需要人为输入参数,通过模拟实验比较和实际应用验证了本文方法的优越性和有效性。     

显著性权重RX高光谱异常点检测

高光谱图像异常点检测中,传统RX异常点检测算法忽略了空间相关性,背景估计不准确。本文提出了一种基于图像局部邻域光谱显著性分析的加权RX算法。该算法通过引入图像显著性分析,对基于概率密度为权重的图像背景建模进行改进,建立光谱显著性权重图,重新定义RX算法中的均值向量和协方差矩阵,并给不同的目标赋予不同的权值,达到优化背景估计的目的。利用合成高光谱数据和真实高光谱数据进行异常点检测实验,结果表明,对于同一组数据,本文算法检测到的异常点数比传统算法多,虚警率较低,有效地提高了检测率。