Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment

This is a review of the latest developments in different fields of remote sensing for forest biomass mapping. The main fields of research within the last decade have focused on the use of small footprint airborne laser scanning systems, polarimetric synthetic radar interferometry and hyperspectral data. Parallel developments in the field of digital airborne camera systems, digital photogrammetry and very high resolution multispectral data have taken place and have also proven themselves suitable for forest mapping issues. Forest mapping is a wide field and a variety of forest parameters can be mapped or modelled based on remote sensing information alone or combined with field data. The most common information required about a forest is related to its wood production and environmental aspects. In this paper, we will focus on the potential of advanced remote sensing techniques to assess forest biomass. This information is especially required by the REDD (reducing of emission from avoided deforestation and degradation) process. For this reason, new types of remote sensing data such as fullwave laser scanning data, polarimetric radar interferometry (polarimetric systhetic aperture interferometry, PolInSAR) and hyperspectral data are the focus of the research. In recent times, a few state-of-the-art articles in the field of airborne laser scanning for forest applications have been published. The current paper will provide a state-of-the-art review of remote sensing with a particular focus on biomass estimation, including new findings with fullwave airborne laser scanning, hyperspectral and polarimetric synthetic aperture radar interferometry. A synthesis of the actual findings and an outline of future developments will be presented.     

Estimation and comparison of leaf area index of agricultural crops using irs liss-III and EO-1 hyperion images

Motivated by the increasingly availability and importance of hyperspectral remote sensing data, this study aims to determine whether current generation narrowband hyperspectral remote sensing data could be used to estimate vegetation Leaf Area Index (LAI) accurately than the traditional broadband multispectral data. A comparative study has been carried out to evaluate the performance of the narrowband Normalized Difference Vegetation Index (NDV1) derived from Hyperion hyperspectral sensor with that of derived from IRS LISS-III for the estimation of LAI of some major agricultural crops (e.g. cotton, sugarcane and rice) in part of Guntur district, India. It has been found that the narrowband NDVI derived from Hyperion has shown better results over its counterpart derived from broadband LISS-III. Linear regression models have been used which with selected subsets of individual Hyperion bands performed better to predict LAI than those based on the broadband datasets, although the potential to overfit models using the large number of available Hyperion bands is a concern for further research.     

高光谱遥感技术在植被信息提取中的应用

高光谱遥感技术与传统遥感技术相比,有着图谱合一和光谱连续的优势,能够实现地物的准确识别和精细分类,并且在岩矿信息提取方面取得了显著成果,然而在植被信息提取中的研究尚处于发展阶段。随着农作物估产和土地利用监测等领域对植被精细信息的要求日益提高,高光谱遥感在植被研究中的应用显得越发重要。本文针对高光谱在植被信息提取中的应用进行研究,实现传统多光谱遥感难以完成的准确识别与分类。     

图像分类中基于核映射的光谱匹配度量方法

针对多光谱遥感数据特点利用SSV匹配技术改进高斯核函数得到新的KSSV函数,然后在由KSSV核函数映射得到的高维空间中利用SAM匹配技术代替基于欧氏距离的相似性度量。如此可以充分挖掘多光谱影像中的波谱特征信息并有效利用,提高模式识别方法应用的有效性。将此方法分别应用于非监督分类(k均值)与监督分类(最小距离、SVM)的试验表明,改进度量的分类方法可显著提高地类间的可区分度并有效降低类内的不一致性,更有效针对多光谱遥感影像中的地物类型,获得较好的精度改进。     

高光谱热红外遥感：现状与展望

高光谱热红外数据中蕴含着丰富的长波光谱信息,可以更精细的揭示地气耦合过程导致的辐射变化,反映热红外谱段特有的地物诊断特征,同时高光谱特性也可以为热红外关键特征参数的病态反演问题提供更合理的假设和约束条件,具有重要的研究价值和应用前景。高光谱热红外遥感技术自诞生起,在吸纳多光谱热红外遥感技术的基础上迅速发展,成为热红外遥感领域的重要研究方向和突破点。然而,当前高光谱热红外遥感存在着可用数据不足,处理方法传统,反演精度有限,应用难以有效实施等问题。为进一步明晰高光谱热红外遥感的研究进展和现存挑战,本文在高光谱热红外相关文献深入分析的基础上,梳理了高光谱热红外研究的发展脉络和热点,介绍了现有国内外主要的高光谱热红外传感器,分析了高光谱大气效应校正、地表温度和发射率分离以及地气关键特征参数一体化反演的现状和问题,总结了相关典型行业应用,展望了高光谱热红外的发展方向,以期为未来高光谱热红外研究工作的开展提供借鉴和帮助。     

一种高光谱遥感图像快速谱聚类算法

为获得分类效果更优良的遥感图像分类方式并解决高光谱遥感图像分类运算速度缓慢的问题，集成Lanczos算法与谱聚类算法，探讨了高光谱遥感图像谱聚类算法应用于遥感图像分类的可行性，提出了一种面向高光谱遥感图像的快速谱聚类算法；通过对比美国圣地亚哥机场高光谱遥感图像K-均值算法与谱聚类算法的分类结果，发现面向高光谱遥感图像的谱聚类算法易于识别线性地物，且分类的速度能得到较大提升。     

高光谱遥感影像分类研究进展

随着模式识别、机器学习、遥感技术等相关学科领域的发展,高光谱遥感影像分类研究取得快速进展。本文系统总结和评述了当前高光谱遥感影像分类的相关研究进展,在总结分类策略的基础上,重点从以核方法为代表的新型分类器设计、特征挖掘、空间-光谱分类、基于主动学习和半监督学习的分类、基于稀疏表达的分类、多分类器集成六个方面对高光谱影像像素级分类最新研究进行了综述。针对今后的研究方向,指出高光谱遥感影像分类一方面要适应大数据、智能化高光谱对地观测的发展前沿,继续引入机器学习领域的新理论、新方法,综合利用多源遥感数据、多维特征空间互补的优势,提高分类精度、分类器泛化能力和自动化程度;另一方面要关注高光谱遥感应用的需求,突出高光谱遥感记录精细光谱特征的优势,针对应用需求发展有效的分类方法。     

光学遥感影像智能化处理研究进展

随着对地观测技术的发展,海量遥感影像不断传输到地面。传统的遥感信息处理方法在处理效率、精度上的不足,限制了遥感信息的挖掘及利用,亟需发展智能化方法满足遥感影像处理的需求。受自然界中生物进化机制的启发,基于进化计算的遥感影像智能化处理方法具有以下特点:(1)拥有全局优化能力,对目标函数的优化能力更强;(2)具有自组织、自学习的特点,能够从遥感数据本身学习,不依赖数据分布等先验信息;(3)拥有处理多目标问题的能力,同时考虑多个目标函数而不需要人工确定它们之间的权重。因此,智能化遥感信息处理方法能够在海量遥感影像中有效地提取适用于不同应用目的的信息。本文主要介绍智能化遥感信息处理方法的典型应用包括遥感影像智能化分类(监督分类、聚类)、遥感影像亚像素信息提取(高光谱影像混合像元分解、亚像元制图),并讨论了遥感信息智能化处理方法的发展方向。     

对地观测高光谱激光雷达发展及展望

对目标空间三维—光谱信息的高分辨一体化获取与应用,是对地观测技术发展的前沿科学问题。结合高光谱成像与激光雷达测距的技术优势,对地观测多光谱/高光谱激光雷达遥感技术手段应运而生,并成为遥感技术未来发展的重要方向。本文分3个阶段详细回顾了对地观测高光谱激光雷达系统的发展历程,并针对其独有数据类型阐述了数据处理研究方面的探索研究。最后,重点分析了高光谱激光雷达在测绘领域、农林业领域的重大应用潜力,展望了未来对地观测高光谱激光雷达发展面临的机遇和挑战。     

多源遥感图像融合发展现状与未来展望

近年来,随着遥感技术的发展,高光谱、红外、雷达等多源遥感成像手段在精准农业、资源调查、环境监测、军事国防等重要领域发挥着越来越重要的作用。同一场景多源遥感图像观测的地物对象相同,但观测的维度不同,图像的空间、光谱与时间分辨率存在差异,提供的信息既具有冗余性,又具有互补性和合作性。多源遥感图像融合能够综合利用不同来源获取的遥感图像信息,实现更精准、更全面的对地观测,是遥感对地观测领域的核心关键技术。本文从多源遥感图像的数据来源出发,综述了多源遥感图像融合的研究现状与未来发展趋势:首先介绍了国内外现有多源遥感图像的主要来源、图像特性与典型应用;然后,对不同类型多源遥感图像融合的研究现状和挑战性难题进行了归纳和总结;最后,对多源遥感图像融合的未来发展趋势进行了展望。     

Application of multispectral LiDAR to automated virtual outcrop geology

Terrestrial laser scanning (TLS) is a valuable tool for creating virtual 3D models of geological outcrops to enable enhanced modeling and analysis of geologic strata. Application of TLS data is typically limited to the geometric point cloud that is used to create the 3D structure of the outcrop model. Digital photography can then be draped onto the 3D model, allowing visual identification and manual spatial delineation of different rock layers. Automation of the rock type identification and delineation is desirable, and recent work has investigated the use of terrestrial hyperspectral photography for this purpose. However, passive photography, whether visible or hyperspectral, presents several complexities, including accurate spatial registration with the TLS point cloud data, reliance on sunlight for illumination, and radiometric calibration to properly extract spectral signatures of the different rock types. As an active remote sensing method, a radiometrically calibrated TLS system offers the potential to directly provide spectral information for each recorded 3D point, independent of solar illumination. Therefore, the practical application of three radiometrically calibrated TLS systems with differing laser wavelengths, thereby achieving a multispectral dataset in conjunction with 3D point cloud data, is investigated using commercially available hardware and software. The radiometric calibration of the TLS intensity values is investigated and the classification performance of the multispectral TLS intensity and calibrated reflectance datasets evaluated and compared to classification performed with passive visible wavelength imagery. Results indicate that rock types can be successfully identified with radiometrically calibrated multispectral TLS data, with enhanced classification performance when fused with passive visible imagery.     

浅水湖泊水生植被遥感监测研究进展

在浅水湖泊中,水生植物具有净化水质、抑制藻类、提供鱼类食物和栖息环境等生态功能,同时,其过度扩张也会加速湖泊淤浅和沼泽化、引起湖泊二次污染等环境负效应.实时动态地掌握湖泊水生植被类群和种群的空间分布及其面积、生物量等指标信息,对湖泊生态修复和评估、水生植被恢复和管理等具有重要现实意义.遥感技术的大面积、实时、动态等特点...     

A Multiple SVM System for Classification of Hyperspectral Remote Sensing Data

With recent technological advances in remote sensing sensors and systems, very high-dimensional hyperspectral data are available for a better discrimination among different complex land-cover classes. However, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon or ‘curse of dimensionality’ in hyperspectral data sets. Moreover, these high numbers of bands are usually highly correlated. Because of these complexities of hyperspectral data, traditional classification strategies have often limited performance in classification of hyperspectral imagery. Referring to the limitation of single classifier in these situations, Multiple Classifier Systems (MCS) may have better performance than single classifier. This paper presents a new method for classification of hyperspectral data based on a band clustering strategy through a multiple Support Vector Machine system. The proposed method uses the band grouping process based on a modified mutual information strategy to split data into few band groups. After the band grouping step, the proposed algorithm aims at benefiting from the capabilities of SVM as classification method. So, the proposed approach applies SVM on each band group that is produced in a previous step. Finally, Naive Bayes (NB) as a classifier fusion method combines decisions of SVM classifiers. Experimental results on two common hyperspectral data sets show that the proposed method improves the classification accuracy in comparison with the standard SVM on entire bands of data and feature selection methods.     

Robust Semi-supervised Kernel-FCM Algorithm Incorporating Local Spatial Information for Remote Sensing Image Classification

Fuzzy c-means (FCM) algorithm is a popular method in image segmentation and image classification. However, the traditional FCM algorithm cannot achieve satisfactory classification results because remote sensing image data are not subjected to Gaussian distribution, contain some types of noise, are nonlinear, and lack labeled data. This paper presents a robust semi-supervised kernel-FCM algorithm incorporating local spatial information (RSSKFCM_S) to solve the aforementioned problems. In the proposed algorithm, insensitivity to noise is enhanced by introducing contextual spatial information. The non-Euclidean structure and the problem in nonlinearity are resolved through kernel methods. Semi-supervised learning technique is utilized to supervise the iterative process to reduce step number and improve classification accuracy. Finally, the performance of the proposed RSSKFCM_S algorithm is tested and compared with several similar approaches. Experimental results for the multispectral remote sensing image show that the RSSKFCM_S algorithm is more effective and efficient.     

Using multi-date high spectral resolution data to assess the physiological status of macroscopically undamaged foliage on a regional scale

Forests play an important role in regulation of the global climate; moreover, they provide human beings with a whole range of ecosystem services. Forest health and ecosystem functioning have been influenced by anthropogenic activities and their consequences, such as air pollution, surface mining, heavy metal contamination, and other biotic and abiotic stress factors, which had an especially serious effect on central Europe. Many aspects of the physiological state of trees are more or less related to the concentrations of two main groups of leaf photosynthetic pigments: chlorophylls and carotenoids. Therefore, their contents can be used as non-specific indicators of the actual tree physiological status, stress and the pre-visible tree damage. Variations in leaf biochemical composition affect foliar optical properties and can be assessed remotely using high spectral resolution data (hyperspectral data). These data were successfully used in earlier studies to detect vegetation stress and damage. However, only a few approaches have dealt with the use of hyperspectral remote sensing to assess vegetation physiological status on a regional scale. Moreover, little or no research has been done on assessing vegetation health while utilizing multi-date hyperspectral images.In this study, the method for assessing forest health conditions using optical indices retrieved from hyperspectral data was applied to the two temporal HyMap date sets acquired in 07/2009 and 08/2010 to detect stress for the Norway spruce forests in Sokolov, NW Bohemia, a region affected by long-term extensive mining. The classification results were validated by ground truth data (total chlorophyll – Cab, carotenoids – Car and carotenoid to chlorophyll ratio – Car/Cab) and were associated with the geochemical conditions of the forest stands. Both biochemical analysis of the sampled foliage and classification of 2009 and 2010 hyperspectral image identified the same sites affected by vegetation stress. In addition to higher Car/Cab, which enabled detection of the stressed trees using hyperspectral image data, these sites showed critically low pH and lower values for the macronutrient parameters in both organic horizons and, in addition, both sites exhibit critically low base cation to aluminum ratios (Bc/Al) for lower organic and top mineral (0–20 cm) soil horizons.The results of this study demonstrate (i) the potential application of hyperspectral remote sensing as a rapid method of identifying tree stress prior to symptom expression, and (ii) the added value of multi-temporal approaches for hyperspectral data and its further potential for monitoring forest ecosystems.     

面向对象的高光谱遥感影像稀疏表示分类

稀疏表示用于高光谱遥感影像分类多是基于像素层次来处理的。文中提出一种面向对象的高光谱遥感影像稀疏表示分类方法。首先从高光谱影像中提取4个波段组成标准的多波段影像,进行面向对象的影像分割;然后计算各对象在各波段上的光谱均值,并选取少量样本进行训练;最后利用基于Fisher字典学习的稀疏表示进行高光谱遥感影像的分类。实验结果表明,该方法可以利用较少的样本得到较好的分类效果,与基于像素层的稀疏分类相比较,分类精度与效率均有所提高,分类结果更接近真实地物,避免了零碎图斑。     

天宫一号高光谱遥感场景分类数据集及应用

天宫一号高光谱成像仪具有空间分辨率高、光谱分辨率高、图谱合一等特性，在中国航天高光谱领域具有里程碑的意义。针对一般遥感场景分类数据集尺度单一、光谱分辨率较低等问题，本文提出基于天宫一号的多谱段、高空间分辨率、多时相高光谱遥感场景分类数据集(TG1HRSSC)。利用天宫一号高光谱成像仪获取的高质量数据，经过辐射校正、几何校正、空间裁剪、波段筛选、数据质量分析与控制等，制作了一批通用的航天高光谱遥感场景分类数据集，通过载人航天空间应用数据推广服务平台（http://www.msadc.cn[2019-09-10]）进行分发和共享。该数据集包括天宫一号高光谱成像仪获取的城镇、农田、林地、养殖塘、荒漠、湖泊、河流、港口、机场等9个典型地物场景的204个高光谱影像数据，其中5 m分辨率全色谱段1个波段、10 m分辨率可见近红外谱段54个有效波段以及20 m分辨率短波红外谱段52个有效波段。研究利用AlexNet、VGG-VD-16、GoogLeNet等深度学习算法网络对构建的数据集进行场景分类的试验，结果表明该数据集的场景分类应用实现较好效果。由于该数据集具备高分辨、高光谱等特征优势，未来在语义理解、多目标检测等方面有着广泛的应用价值。     

A Multispectral Remote Sensing Data Spectral Unmixing Algorithm Based on Variational Bayesian ICA

Mixed pixel is a key issue in medium to coarse resolution remote sensing image, and it seriously restricts the remote sensing classification. This paper presents an Independent component analysis (ICA) algorithm based on the variational Bayesian (VB) methods, named VBICA, for spectral unmixing in multispectral remote sensing image. The model assumes that the mixed pixels to be separated are given as linear mixtures. The matrixes of linear mixtures are assumed to be unknown. In the Bayesian framework, the endmember and abundance have finally been achieved with Bayesian inference and approximate variational algorithm. The proposed method is evaluated and tested on a numerical simulative image from the noise resistance, area size, pixel purity, estimated number of endmembers and real multispectral remote sensing image of 100?×?100 pixels. Experimental results on simulated image demonstrated that compared to the Fast ICA algorithm, the proposed algorithm can give more accurate results, and the validity of the proposed algorithm is verified by the real multispectral remote sensing image of the similarity on spectral curves, average similarity and ground objects distribution maps.     

Exploiting spectral and spatial information in hyperspectral urban data with high resolution

Very high resolution hyperspectral data should be very useful to provide detailed maps of urban land cover. In order to provide such maps, both accurate and precise classification tools need, however, to be developed. In this letter, new methods for classification of hyperspectral remote sensing data are investigated, with the primary focus on multiple classifications and spatial analysis to improve mapping accuracy in urban areas. In particular, we compare spatial reclassification and mathematical morphology approaches. We show results for classification of DAIS data over the town of Pavia, in northern Italy. Classification maps of two test areas are given, and the overall and individual class accuracies are analyzed with respect to the parameters of the proposed classification procedures.     

高光谱遥感及其在青藏高原的应用潜力分析

高光谱遥感以其波段数目多、光谱分辨率高、带宽窄等特点受到了国内外研究者的广泛关注,它的发展可以说是遥感技术领域的巨大进步。本文主要介绍高光谱遥感的发展,以及针对高光谱影像独有的特点所要进行的数据处理方法。在此基础上,结合青藏高原独特的地理地质环境,探讨高光谱遥感在青藏高原的应用潜力。