An empirical InSAR-optical fusion approach to mapping vegetation canopy height

Exploiting synergies afforded by a host of recently available national-scale data sets derived from interferometric synthetic aperture radar (InSAR) and passive optical remote sensing, this paper describes the development of a novel empirical approach for the provision of regional- to continental-scale estimates of vegetation canopy height. Supported by data from the 2000 Shuttle Radar Topography Mission (SRTM), the National Elevation Dataset (NED), the LANDFIRE project, and the National Land Cover Database (NLCD) 2001, this paper describes a data fusion and modeling strategy for developing the first-ever high-resolution map of canopy height for the conterminous U.S. The approach was tested as part of a prototype study spanning some 62,000 km² in central Utah (NLCD mapping zone 16). A mapping strategy based on object-oriented image analysis and tree-based regression techniques is employed. Empirical model development is driven by a database of height metrics obtained from an extensive field plot network administered by the USDA Forest Service-Forest Inventory and Analysis (FIA) program. Based on data from 508 FIA field plots, an average absolute height error of 2.1 m (r = 0.88) was achieved for the prototype mapping zone.     

一种基于分段偏最小二乘模型的土壤重金属遥感反演方法

土壤中重金属由于其毒性而成为最有害的环境污染物之一,利用遥感进行土壤重金属检测和分布制图是目前最为高效的手段。采用哨兵二号（Sentinel-2）多光谱影像与实测样品光谱数据,对山西省铜矿峪铜矿尾矿库及其周边农田土壤的铜（Cu）含量进行估算,利用68个土壤样品的反射光谱,优选出适合土壤铜含量预测的波段,结合分段偏最小二乘法（Piecewise Partial Least Squares Regression,P-PLSR）,对土壤铜含量进行估算,将模型用于Sentinel-2影像获得了Cu含量的空间分布。通过P-PLSR对实测样品光谱建模反演Cu含量的决定系数（R²）为0.89,预测偏差比（RPD）为2.82;利用Sentinel-2多光谱影像获得了该区域Cu元素含量空间分布,其Cu含量的估算精度R²为0.74,RPD为1.73,Cu含量高值区空间分布与尾矿库关系密切。Sentinel-2多光谱数据具有高空间分辨率（10、20和60 m）、高时间分辨率和幅宽大（290 km）等优势,通过敏感波段选择并建立反演模型,可实现大范围土壤环境制图。     

面向打印的光谱色彩管理中间空间构造方法

针对光谱色彩管理中光谱空间维度高引起多光谱图像处理时间长、所需存储空间大的问题,提出构造中间空间的方法。首先通过分析色彩管理过程,引入中间空间,建立以中间空间为设备无关颜色空间的光谱色彩管理流程;然后针对多光谱图像的打印输出,采用主成分分析法对打印机特征化光谱样本进行降维,将降维后的特征空间作为中间空间;最后采用特征向量矩阵实现任意多光谱图像数据到中间空间的变换。实验表明,采用打印机特征化光谱样本生成的中间空间与光谱空间的变换效率高,变换的光谱和色度精度高,图像数据降维后能保持源图像光谱的主要信息。     

EMD方法在多光谱遥感图像融合中的应用

多光谱图像具有较高的光谱分辨率,而其空间分辨率比较低,致使融合后的多光谱图像空间细节的表现能力不足。为了克服这种融合图像空间细节表达能力差的问题,本文提出了用EMD(EmpiricalModeDecomposition)方法对多光谱图像进行分解,提取空间细节和纹理信息,并将其叠合到融合图像上的方法。实验表明:改善了视觉效果,提高融合图像的空间表达能力。     

A multi-spectral non-local method for retrieval of boundary layer cloud properties from optical remote sensing data

A multi-spectral non-local (MSN) method is developed for advanced retrieval of boundary layer cloud properties from remote sensing data, as an alternative to the independent pixel approximation (IPA) method. The non-local method uses data at both the target pixel and neighboring pixels to retrieve cloud properties such as pixel-averaged cloud optical thickness and effective droplet radius. Radiance data to be observed from space were simulated by a three-dimensional (3D) radiation model and a stochastic boundary layer cloud model with two-dimensional (horizontal and vertical) variability in cloud liquid water and effective radius. An adiabatic assumption is used for each cloud column to model the geometrical thickness and vertical profiles of cloud liquid water content and effective droplet radius, neglecting drizzle and cloud brokenness for simplicity. The dependence of radiative smoothing and roughening on horizontal scale, optical thickness and single scattering albedo are investigated. Then, retrieval methods using 250-m horizontal resolution data onboard new generation satellites are discussed. The regression model for the MSN method was trained based on datasets from numerical simulations. The training was performed with respect to various domain averages of optical thickness and effective radius, because smoothing and roughening effects are strongly dependent on the two variables. Retrieval accuracy is discussed here with datasets independent of those used in the training, towards assessing the generality of the technique. It is demonstrated that retrieval accuracy of cloud optical thickness, which is often retrieved from single-spectral visible-wavelength data, is improved the most using neighboring pixel data and secondly using multi-spectral data, and ideally with both. When the IPA retrieval method is applied to optical thickness and effective radius, the root-mean-square relative errors can be 15-90%, depending on solar and view directions. In contrast, the MSN method has errors of 4-10%, which is smaller than IPA by a factor of 2-10. It is also suggested that the accuracy of the MSN method is insensitive to some assumptions in the inhomogeneous cloud input data used to train the regression model.     

基于SVM的高维多光谱图像分类算法及其特性的研究

针对传统模式分类算法在处理高维多光谱图像时面临的困难，文章把支持向量机(Support Vector Machine，SVM)用于高维多光谱图像分类，有效地减弱了Hughes现象，获得了比传统方法更好的分类精度。研究了高维多光谱图像分类中SVM的分类性能与训练样本数目和数据维数之间的关系。实验结果表明，与传统模式分类方法相比，SVM具有分类精度高、推广性强的优点，尤其是当学习样本数目较少、数据维数高时，SVM的优势更加明显。     

火工烟火药剂燃烧火焰多光谱辐射测温技术研究

为了准确测量火工烟火药剂的燃烧火焰温度,根据多光谱辐射测温技术的工作原理研制了多光谱高温计;基于二次测量的数据处理方法,得到了燃烧火焰的真实温度.以黑火药为例,采用多光谱高温计对其燃烧火焰的辐射能量进行了测定,应用二次测量法对测得的数据进行迭代计算,从而得到黑火药燃烧温度.实验表明,该多光谱高温计能够很好地应用于火工烟火药剂燃烧温度的测定,且具有高稳定性.为火工烟火药剂燃烧特性的研究奠定了基础.     

多光谱视觉检测系统及其在稻飞虱发生早期检测中的应用

为了研究作物病虫害发生后植株冠层多光谱图像特征值的变化规律,设计了一套室内多光谱视觉检测系统试验平台。文章介绍了该试验平台在稻飞虱发生早期水稻冠层多光谱图像特性研究中的应用。在实验室条件下对盆栽稻株进行接虫,然后分12个时段(接虫后14h、16h、20h、22h、24h、38h、44h、48h、62h、69h、73h)对受稻飞虱侵害后的稻株冠层进行多光谱图像采集,共采集到NIR、R、G、B四个通道及其组合通道(CIR、RGB)的稻株冠层图像561张,然后在Matlab中进行图像处理和数据分析,结果表明:稻飞虱迁入后,NIR通道的冠层叶片灰度值算术平均值在时间维度上光谱图像特征值变化非常显著,其次是G、R通道。因此,在稻飞虱迁入后,应着重观察NIR、G、R通道冠层叶片灰度值算术平均值在时间维度上的变化规律,从中挖掘用于稻飞虱入侵检测和危害程度分级的相关模型。     

Using covariance intersection for change detection in remote sensing images

In this paper, an unsupervised change detection technique for remote sensing images ac-quired on the same geographical area but at different time instances is proposed by conducting Co-variance Intersection (CI) to perform unsupervised fusion of the final fuzzy partition matrices from the Fuzzy C-Means (FCM) clustering for the feature space by applying compressed sampling to the given remote sensing images. The proposed approach exploits a CI-based data fusion of the membership function matrices, which are obtained by taking the Fuzzy C-Means (FCM) clustering of the fre-quency-domain feature vectors and spatial-domain feature vectors, aimed at enhancing the unsuper-vised change detection performance. Compressed sampling is performed to realize the image local feature sampling, which is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable recovery. The experi-mental results demonstrate that the proposed algorithm has a good change detection results and also performs quite well on denoising purpose.     

基于多谱线插值法和复调制细化法的电力系统谐波分析

非同步采样时采用快速FFT会造成频谱泄露,为了降低频谱泄露的影响,提高谐波和间谐波检测精确度,本文提出了一种基于多谱线插值法和复调制细化法的组合分析方法。首先估算出基波和谐波的长范围泄露并将其消除,然后分析目标频点附近的几条谱线并利用谱线携带的信息,推导出多谱线的插值算法,并采用多项式拟合的方法得到其修正式。对于频率非常相近的电力系统信号,采用基于复解析带通滤波器的复调制细化法对其进行细化分析,得到其较精确的信号参数。仿真和实验结果验证了本文方法的有效性和高精度性。