顾及入射角的全波形激光雷达数据校正方法

相比于传统激光雷达,全波形激光雷达以非常小的采样间隔记录激光回波的全部信息并以数字化存储,经过处理可以得到能够反映地物固有特性的潜在特征。在全波形激光雷达回波数据处理中,对波形数据的校正是波形分解和地物目标特征提取的关键步骤。针对激光雷达系统获取数据过程中同类型建筑物目标对不同入射方向激光反射的差异,提出了一种全波形激光雷达数据的后向散射截面校正方法,建立了顾及入射角的波形数据校正模型。采用机载小光斑全波形激光雷达数据对提出的校正方法进行验证,结果表明:该校正方法能够对同类型建筑物目标的波形数据的后向散射截面进行归一化校正,消除不同入射角对该类目标后向散射截面的影响,极大提高后续建筑物目标精细分类和特征提取等应用的精度。     

ATS-1激光雷达实时处理显示系统设计

为提高激光雷达系统探测的实时性,合理开发数据处理显示系统显得尤为必要。从ATS-1激光雷达多通道采集系统特征出发,基于多线程技术,设计激光雷达实时处理显示系统。该系统能够实时跟踪激光雷达探测的回波信号数据,并能自动进行数据处理,将数据结果进行实时显示,解决激光雷达采集数据和处理显示间隔时间长的问题,提高该激光雷达探测的实时性,为其他激光雷达处理显示系统的设计提供了思路。     

偏振激光雷达对地观测遥感

激光雷达遥感可获取地物的三维结构信息,偏振遥感可实现"强光弱化与弱光强化"的信息增强,两者主被动光学遥感组合为下一代对地观测多维光学遥感新模式的研发提供一条可行的途径。事实上偏振激光雷达这一概念提出较早,但其已开展的应用主要集中在大气监测领域,研究重点侧重于激光后向散射去极化技术等方面,进一步发展到探测大气气溶胶含量及针对相关任务的信息增强技术研发等;但在其他领域,偏振激光雷达的遥感应用极其有限,而理论上偏振激光雷达具有推广到复杂地物遥感的可能性。从其两种组成模式的遥感原理角度出发探讨偏振激光雷达对地观测遥感可能实现的机理基础,并分析了其技术发展的趋势,为后续相关遥感技术的研发及其在农业、环境、海洋、空间探测等不同领域中的应用指出了可能的切入点。     

高光谱激光雷达:三维生物物理化学生态测量学

生态测量学为生态学、地理学、林学、野生生物学等提供基础的技术支撑,开发适应上述学科发展的新型生态测量技术日渐重要,尤其随着目前生物地球化学的快速推进,开发三维生物物理化学生态测量技术成为当前的研究热点,然而当前的主流遥感测量技术仍无法做到高效准确。高光谱激光雷达技术的发展为解决该问题提供了可能,这也通过基于连续测量的3种单木高光谱激光雷达数据实现反演光合有效辐射、树冠叶绿素含量与氮含量的三维分布及其昼夜变化趋势得到验证。实验结果预示随着高光谱激光雷达的成熟,三维生物物理化学生态测量学将得以建立,相应的三维生物物理化学生态测量技术将进一步推动地理学、生态学、生物地球化学乃至地球系统科学的发展。     

车载测污激光雷达数据处理软件的设计

数据处理软件是激光雷达系统的重要组成部分.根据车载测污激光雷达采集系统的特性与科研工作的需要设计了车载测污激光雷达数据处理软件.该软件集成了测量物的数据处理与显示功能;能够实时检测到激光雷达的回波信号,并自动进行处理;解决了多种测量物时间、空间分布实时显示的问题.在软件设计过程中,采用了双缓冲技术消除了闪烁现象;利用API函数实现了实时处理功能;在等值线的绘制中使用了不规则三角网对数据进行建模.经过测试,该软件能较好地完成车载测污激光雷达的数据处理工作.     

激光雷达与高光谱成像技术数据融合研究进展

近年来,为提高地物分类精度,突破单一传感器的技术擎制,弥补单一数据源应用的局限性,多源遥感数据融合的成为了遥感领域众多学者关注的研究热点。高光谱遥感技术的光学影像同激光雷达点云数据的融合技术在技术层面为提升地物识别与分类的精度上提供了一种可行方案,打破了单一传感器的技术上限,为目标三维空间—光谱信息一体化获取提供了一种新的解决途径,同时为高光谱激光雷达成像技术研究奠定基础。本文回顾了激光雷达与高光谱成像数据融合发展历程,论述其在特征级和决策级的主要融合方法和研究进展,将常用特征级融合和决策级融合方法进行详细介绍,并对最新几种研究算法进展进行小结和概述,探讨了其面临的挑战和未来发展与应用前景,最后对激光雷达和高光谱成像数据融合未来发展做出系统展望。     

基于萤火虫算法的高光谱遥感波段选择方法

高光谱图像在遥感领域中的应用越来越广泛,但由于自身的高数据维、波段间的高冗余度等特性给图像处理带来了一定困难,针对这个问题,提出一种基于类间可分性准则的改进萤火虫仿生算法,进行高光谱遥感波段选择。在分析萤火虫算法机理的基础上,阐述了利用该算法进行高光谱波段选择的思路,并构造波段相似性矩阵,选择欧氏距离、JM距离、光谱信息散度和离散度作为可分性准则来设置目标函数,根据目标函数值的优劣选择优势波段。最后,使用HYDICE Washington DC Mall和 HyMap Purdue Campus两个高光谱遥感影像数据进行实验验证,并利用支持向量机分类器对最佳波段组合进行精度评价,证明该算法的可行性和有效性。
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高光谱遥感图像波段选择研究进展综述

高光谱成像遥感技术可获取地物的光谱、辐射和空间信息,在国民经济的各个领域得到广泛的应用.但其狭窄的波段间距带来丰富光谱信息的同时,也带来了信息冗余,增加了数据处理的难度.因此,高光谱遥感数据在进行实际应用前,需要进行波段选择并提取光谱特征,降低数据维数.对高光谱遥感图像的波段选择研究进展进行了综述,在分析、归纳波段选择...     

利用信息熵的高光谱遥感影像降维方法

高光谱遥感影像以其众多的波段数目，为地表观测提供近乎连续的波谱数据；然而海量的高光谱遥感影像存在着大量的信息冗余，为数据的处理带来了挑战。因此在对高光谱遥感影像进行存储、分析及可视化等操作之前，对高光谱遥感影像降维处理成为预处理的关键环节之一。利用信息熵理论，将高光谱遥感影像的各波段抽象为具有相关性的独立个体，设计了高光谱遥感影像的决策表矩阵，进而计算各波段的信息熵，量化各波段的信息量，从而将各波段根据信息增益进行排序。用户可根据高光谱遥感影像应用的精度需求，按排序选择波段组合，从而达到降维目的。以遥感分类结果的精度评价为例，对高光谱遥感降维方法的可行性和优越性进行评价。实验结果表明，该方法相较其他特征选取降维方法，能获得更高的分类精度。     

.一种基于云计算模型的遥感处理服务模式研究与实现*

随着空间遥感技术、对地观测技术的不断发展,一个以多时相、多分辨率、多传感器、多波段为特征的多层、立体、多角度、全方位和全天候遥感对地观测数据获取与处理体系正在形成。该体系必然会带来海量、多源的遥感数据。提出了采用目前商业上成功的云计算模型来实现一个高性能、高可扩展性、高可用的遥感处理服务,并结合原型系统,详细阐述了该处理系统的组成与关键技术。     

Earth Observation-oriented Polarization LiDAR Remote SensingEarth Observation-oriented Polarization LiDAR Remote Sensing

Light Detection and Ranging(LiDAR) Remote Sensing(RS) can map the 3D structures of objects，and polarization RS can implement information retrieval enhancement via strengthening the weak reflectance and weakening the strong reflectance that are inverse scenarios in traditional optical RS.Their combination，theoretically，can open a possible way for developing the next-generation multi-dimensional active optical RS technologies.However，the literature review suggested that the concept of polarization LiDAR was earlier proposed，while its applications mainly occurred in the field of atmospheric monitoring.The focuses were on study of laser backscatter depolarization techniques，e.g.，sensing water content in the air based on the metrics of polarization diversity so as to distinguish the structures and orientations between water vapor and ice crystal clouds in the atmosphere，and further，on investigation of aerosol content in the troposphere and improvement of the related methods.But in other fields，the applications of polarization LiDAR RS have been almost blank.Now，it is time to extend the applications of polarization LiDAR for RS of complex objects.This paper analyzed the mechanisms of the two component modules in terms of their RS principles and listed the fundamental works necessitated for comprising the polarization LiDAR RS technology.Finally，this study proposed the ranges of possibly applying polarization LiDAR RS，such as agronomy，environment，ocean and space exploration.     

高光谱遥感数据挖掘若干基本问题的研究

面向高光谱遥感信息的特点，分析了高光谱遥感数据挖掘的形成和作用，在构建其框架体系与处理流程的基础上。探讨了可以发现的知识类型和典型的挖掘模式，并分析了一些主要挖掘算法和关键技术。最后对高光谱遥感数据挖掘潜在的应用方向进行了探讨。     

Unmixing-based content retrieval system for remotely sensed hyperspectral imagery on GPUs

This paper presents a new unmixing-based retrieval system for remotely sensed hyperspectral imagery. The need for this kind of system is justified by the exponential growth in the volume and number of remotely sensed data sets from the surface of the Earth. This is particularly the case for hyperspectral images, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels. To deal with the high computational cost of extracting the spectral information needed to catalog new hyperspectral images in our system, we resort to efficient implementations of spectral unmixing algorithms on commodity graphics processing units (GPUs). Spectral unmixing is a very popular approach for interpreting hyperspectral data with sub-pixel precision. This paper particularly focuses on the design of the proposed framework as a web service, as well as on the efficient implementation of the system on GPUs. In addition, we present a comparison of spectral unmixing algorithms available in the system on both CPU and GPU architectures.     

高光谱数据的光谱信息特点及面向对象的特征参数选择——以北京顺义区为例

波段宽度为纳米级的高光谱数据,具有几十乃至几百个光谱通道,它们各有不同的特点。如何根据具体的应用目的,在这众多的波段中选择出最佳波段和特征参数,对于有效地进行高光谱数据的处理、分析及信息提取至关重要。以北京顺义区高光谱数据为例,首先分析了通道间的相关性,根据通道的相关性大小和分组块状结构特点,将其分为若干组;然后全面分析了高光谱数据的光谱信息特征,在综合考虑各波段的信息含量、波段间的相关性以及地物光谱的吸收特性和可分性等因素
的基础上,提出了面向对象的分层多次选择高光谱数据最佳波段和提取特征参数的基本思路和方法;最后用其它地区的成像光谱数据对此方法进行了验证。     

Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index

Remote sensing of forest canopy cover has been widely studied recently, but little attention has been paid to the quality of field validation data. Ecological literature has two different coverage metrics. Vertical canopy cover (VCC) is the vertical projection of tree crowns ignoring within-crown gaps. Angular canopy closure (ACC) is the proportion of covered sky at some angular range around the zenith, and can be measured with a field-of-view instrument, such as a camera. We compared field-measured VCC and ACC at 15° and 75° from the zenith to different LiDAR (Light Detection and Ranging) metrics, using several LiDAR data sets and comprehensive field data. The VCC was estimated to a high precision using a simple proportion of canopy points in first-return data. Confining to a maximum 15° scan zenith angle, the absolute root mean squared error (RMSE) was 3.7-7.0%, with an overestimation of 3.1-4.6%. We showed that grid-based methods are capable of reducing the inherent overestimation of VCC. The low scan angles and low power settings that are typically applied in topographic LiDARs are not suitable for ACC estimation as they measure in wrong geometry and cannot easily detect small within-crown gaps. However, ACC at 0-15° zenith angles could be estimated from LiDAR data with sufficient precision, using also the last returns (RMSE 8.1-11.3%, bias -6.1-+4.6%). The dependency of LiDAR metrics and ACC at 0-75° zenith angles was nonlinear and was modeled from laser pulse proportions with nonlinear regression with a best-case standard error of 4.1%. We also estimated leaf area index from the LiDAR metrics with linear regression with a standard error of 0.38. The results show that correlations between airborne laser metrics and different canopy field characteristics are very high if the field measurements are done with equivalent accuracy.     

Nadir and off‐nadir hyperspectral field data: strengths and limitations in estimating grassland biophysical characteristics

In recent years, hyperspectral and multi‐angular approaches for quantifying biophysical characteristics of vegetation have become more widely used. In fact, as both hyperspectral and multi‐angle reflectance decrease the level of noise on retrieved geophysical parameter values, they increase their reliability by also reducing the saturation problem of the relationships between vegetation indices and biophysical characteristics. To test which is the best methodology in estimating some important biophysical grassland parameters (biomass, total and percent biomass nitrogen content, phytomass and its total and percent nitrogen content), nadir and off‐nadir measurements were carried out, three times during the vegetative period of 2004, in a permanent flat meadow located in the experimental farm of the University of Padua, Italy. The two approaches and the broad band vegetation indices calculated using Landsat bands were compared considering both the best determination coefficients of five vegetation indices, calculated with the two analysis, and through a partial least squares regression using different spectral regions measured at different angles as predictive variables. Using nadir data the red edge region was the most useful for the prediction of biophysical variables, especially phytomass, but also nitrogen content. The off‐nadir data did not provide any significance differences in results to that of data obtained in nadir view but both methods seem to be better adapted to describe biophysical parameters of vegetation than the use of broad band vegetation indices.     

The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas

Remote sensing hyperspectral sensors are important and powerful instruments for addressing classification problems in complex forest scenarios, as they allow one a detailed characterization of the spectral behavior of the considered information classes. However, the processing of hyperspectral data is particularly complex both from a theoretical viewpoint [e.g. problems related to the Hughes phenomenon (Hughes, 1968) and from a computational perspective. Despite many previous investigations that have been presented in the literature on feature reduction and feature extraction in hyperspectral data, only a few studies have analyzed the role of spectral resolution on the classification accuracy in different application domains. In this paper, we present an empirical study aimed at understanding the relationship among spectral resolution, classifier complexity, and classification accuracy obtained with hyperspectral sensors for the classification of forest areas. We considered two different test sets characterized by images acquired by an AISA Eagle sensor over 126 bands with a spectral resolution of 4.6 nm, and we subsequently degraded its spectral resolution to 9.2, 13.8, 18.4, 23, 27.6, 32.2 and 36.8 nm. A series of classification experiments were carried out with bands at each of the degraded spectral resolutions, and bands selected with a feature selection algorithm at the highest spectral resolution (4.6 nm). The classification experiments were carried out with three different classifiers: Support Vector Machine, Gaussian Maximum Likelihood with Leave-One-Out-Covariance estimator, and Linear Discriminant Analysis. From the experimental results, important conclusions can be made about the choice of the spectral resolution of hyperspectral sensors as applied to forest areas, also in relation to the complexity of the adopted classification methodology. The outcome of these experiments are also applicable in terms of directing the user towards a more efficient use of the current instruments (e.g. programming of the spectral channels to be acquired) and classification techniques in forest applications, as well as in the design of future hyperspectral sensors.     

Spectral characteristics and feature selection of hyperspectral remote sensing data

Hyperspectral remote sensing data with bandwidth of nanometre (nm) level have tens or even several hundreds of channels and contain abundant spectral information. Different channels have their own properties and show the spectral characteristics of various objects in image. Rational feature selection from the varieties of channels is very important for effective analysis and information extraction of hyperspectral data. This paper, taking Shunyi region of Beijing as a study area, comprehensively analysed the spectral characteristics of hyperspectral data. On the basis of analysing the information quantity of bands, correlation between different bands, spectral absorption characteristics of objects and object separability in bands, a fundamental method of optimum band selection and feature extraction from hyperspectral remote sensing data was proposed.     

不同森林植被的高光谱特征分析

“生态水(层)”富水特征特殊,各信息指标参数难以用常规方法进行量化和反演,高光谱遥感由于其波段多、光谱信息丰富的优点为生态水(层)各信息指标参数的量化反演提供有效的数据源及方法。利用高光谱遥感技术进行植被分析时,其光谱特征的分析和敏感波段提取非常重要。针对“生态水”信息指标植被参数有关量化反演需要,对研究区部分典型植被叶片进行了光谱采集,利用微分方法对光谱数据进行处理,分析了不同植被叶片光谱的原始、一阶微分和二阶微分光谱曲线,从中提取差异大的波段区分不同植被。同时,采用距离统计分析方法对所选择的不同波段进行有效性验证。研究结果表明:虽然3种方法提取的波段有差异,但存在共同点;选择的光谱特征波段可有效地区分不同植被,在近红外波段尤为明显,分别是1 814~1 823 nm,1 874~1 883 nm和1 890~1 899 nm附近。     

基于独立成分分析和神经网络的高光谱遥感数据分类

高光谱遥感数据以数据量大、含混度高、地面样本数据少的特点给分类处理带来了困难。将独立成分分析技术与多层前向神经网络相结合, 得到一种新的分类算法。独立成分分析在提取有效光谱特征的同时, 大大降低了数据的维数。神经网络作为分类器, 分类精度显著高于传统的bayes 分类器。通过对220 波段的高光谱数据进行实验, 得到了良好的效果。