基于流形学习的彩色遥感图像分维数估算

目的 纹理特征提取一直是遥感图像分析领域研究的热点和难点。现有的纹理特征提取方法主要集中于研究单波段灰色遥感图像,如何提取多波段彩色遥感图像的纹理特征,是多光谱遥感的研究前沿。方法 提出了一种基于流形学习的彩色遥感图像分维数估算方法。该方法利用局部线性嵌入方法,对由颜色属性所组成的5-D欧氏超曲面进行维数简约处理;再将维数简约处理后的颜色属性用于分维数估算。结果 利用Landsat-7遥感卫星数据和GeoEye-1遥感卫星数据进行实验,结果表明,同Peleg法和Sarkar法等其他分维数估算方法相比,本文方法具有较小的拟合误差。其中,其他4种对比方法所获拟合误差E平均值分别是本文方法所获得拟合误差E平均值的26.2倍、5倍、26.3倍、5倍。此外,本文方法不仅可提供具有较好分类特性的分维数,而且还能提供相对于其他4种对比方法更加稳健的分维数。结论 在针对中低分辨率的真彩遥感图像和假彩遥感图像以及高分辨率彩色合成遥感图像方面,本文方法能够利用不同地物所具有颜色属性信息,提取出各类型地物所对应的纹理信息,有效地改善了分维数对不同地物的区分能力。这对后续研究各区域中不同类型地物的分布情况及针对不同类型地物分布特点而制定区域规划及开发具有积极意义。     

浅析遥感光谱特征参量的原理及基本方法

概述了导数光谱、红边参数、光谱吸收特征以及光谱反射特征等遥感光谱特征参量的原理及基本方法,总结和分析了这些参量在植被领域中的应用动态,提出了遥感技术存在的问题及其应用展望,遥感光谱特征参量能够为植被理化信息的提取提供强有力的工具。     

Mapping the effects of water stress on Sphagnum: Preliminary observations using airborne remote sensing

Remote sensing of near-surface hydrological conditions within northern peatlands has the potential to provide important large-scale hydrological information regarding ecological and carbon-balance processes occurring within such systems. This article details how field knowledge of the spectral properties of Sphagnum spp., airborne remote sensing data and a range of image analysis approaches, may be combined to provide a suitable proxy for near-surface wetness. Co-incident field and airborne remote sensing data were acquired in May and September 2002 over an important UK raised bog (Cors Fochno). A combination of laboratory-tested NIR and SWIR water-based and biophysical spectral reflectance indices were applied to field and airborne reflectance spectra of Sphagnum pulchrum to elucidate changes in near-surface moisture conditions. Field results showed significant correlations between water-based indices (moisture stress index (MSI) and floating water band indices (fWBI₉₈₀ and fWBI₁₂₀₀₎)) and measures of both near-surface volumetric moisture content (VMC) and water-table position. Spectral indices formulated from the NIR (fWBI₉₈₀ and fWBI₁₂₀₀) proved to be the most useful for indicating near-surface wetness across the widest range of moisture conditions because of their ability to penetrate deeper into the Sphagnum canopy. Correlations between a biophysical index based upon chlorophyll content and both hydrological measures were not significant, possibly due to relatively high levels of surface wetness at the field site in both May and September. S. pulchrum lawns were successfully located and mapped from airborne imagery using the mixed tuned match filtering (MTMF) algorithm. Importantly, MSI derived from airborne data was significantly correlated with both field moisture and the water-table position. Relationships between measures of near-surface wetness and the MSI for naturally heterogeneous canopies were, however, found to be weaker for airborne imagery than for associated field data. This is likely to be a result of the formulation of the MSI itself and the possible preferential detection of “wetter” pixels within the imagery. This effectively reduced the ability of MSI to detect subtle changes in near-surface wetness under high moisture conditions, but would not impede the use of the index under drier conditions. Results from the field data suggest that indices formulated from the NIR may be more suitable for detailed estimations of near-surface and surface wetness at the landscape-scale although reliable hyperspectral data are required to test fully the performance of such indices. The relative merits of using such an approach to determine near-surface hydrological conditions across entire peatland complexes are also discussed.     

基于遥感和陆表信息集成的广东省土地覆盖分类方法研究

土地覆盖信息是估算地-气间的生物物理过程和能量交换的关键参数,也是区域和全球尺度气候和生态系统过程模型所需要的重要参量。如何高效地利用遥感数据提取土地覆盖信息是当前研究迫切需要解决的问题。面向对象的分类方法不但充分利用了遥感数据的光谱信息,同时也利用了影像的纹理结构信息和更多的地物分布信息关系,在遥感分类中具有较大的潜力。研究基于2010年多时相的环境卫星数据、TM数据以及DEM数据,并结合地表采集的4000多个样点数据,采用面向对象的分类方法对广东省土地覆盖进行分类。经采样验证,广东省土地覆盖平均精度为85%,分类结果精度远高于常规的分类算法,说明结合陆表信息的面向对象分类方法比常规的分类算法更具有优势,可以实现高精度的土地覆盖分类。     

Spectral mixture analysis of agricultural crops: Endmember validation and biophysical estimation in potato plots

The spectral reflectance of agricultural crops is affected significantly by sub-pixel scale spectral contributions of background soils and shadows as viewed by a remote sensing instrument. This has meant the potential of remote sensing imagery has not been fully realized for extracting biophysical information and assessing ecological stress using methods such as vegetation indices (VIs). In this paper, we address this problem explicitly using spectral mixture analysis (SMA) to quantify the area abundance of plants, soils and shadows at sub-pixel scales with the aim of improving extraction of plant biophysical and structural information from remote sensing data. Different measurement strategies were tested in the field for acquiring reference endmember spectra of crop vegetation, soil and shadows using a field spectroradiometer for a set of potato plots in western Canada. Endmember measurements included sunlit and shadowed spectra of in situ crop targets, optically thick stacks and data from excised leaves, as well as cultivated, rough and compacted soils. All possible combinations of crop, soil and shadow endmember spectra were analysed using SMA to derive sets of sub-pixel scale component fractions from radiometer spectra acquired from a boom truck over replicate plot samples with a sensor field of view of 1.05 m. Digital video image frames captured simultaneously with the radiometer data were used to determine ground proportions of crop, soil and shadow for independent validation of the SMA fractions. Endmember fractions derived from excised leaves, cultivated soil and shadowed vegetation spectra showed the best agreement with ground truth data, with differences of only ±3.3%. These sub-pixel scale fractions were used in regression analyses to predict leaf area index, biomass and plant width with an average r2 value of 0.85 from SMA shadow fraction, which was a substantial improvement over the best VI results from NDVI, NGVI and SR (average r2 = 0.53). Perspectives on SMA at different stages in the growing season and for different crop types are provided with a recommendation that further SMA research is warranted for local to regional scale agricultural crop monitoring programmes.     

Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data

Remote sensing has the potential of improving our ability to map and monitor pasture degradation. Pasture degradation is one of the most important problems in the Amazon, yet the manner in which grazing intensity, edaphic conditions and land‐use age impact pasture biophysical properties, and our ability to monitor them using remote sensing is poorly known. We evaluate the connection between field grass biophysical measures and remote sensing, and investigate the impact of grazing intensity on pasture biophysical measures in Rondônia, in the Brazilian Amazon. Above ground biomass, canopy water content and height were measured in different pasture sites during the dry season. Using Landsat Thematic Mapper (TM) data, four spectral vegetation indices and fractions derived from spectral mixture analysis, i.e., Non‐Photosynthetic Vegetation (NPV), Green Vegetation (GV), Soil, Shade, and NPV + Soil, were calculated and compared to field grass measures. For grazed pastures under dry conditions, the Normalized Difference Infrared Index (NDII5 and NDII7), had higher correlations with the biophysical measures than the Normalized Difference Vegetation Index (NDVI) and the Soil‐Adjusted Vegetation Index (SAVI). NPV had the highest correlations with all field measures, suggesting this fraction is a good indicator of pasture characteristics in Rondônia. Pasture height was correlated to the Shade fraction. A conceptual model was built for pasture biophysical change using three fractions, i.e., NPV, Shade and GV to characterize possible pasture degradation processes in Rondônia. Based upon field measures, grazing intensity had the most significant impact on pasture biophysical properties compared to soil order and land‐use age. The impact of grazing on pastures in the dry season could be potentially measured by using remotely sensed measures such as NPV.     

遥感大数据研究现状与发展趋势

目的 遥感数据空间分辨率、时间分辨率、光谱分辨率以及辐射分辨率不断提高,数据类型也不断增加,从航天、航空、临近空间等遥感平台所获取的遥感数据量急剧增加,遥感数据已经具有明显的大数据特征。本文旨在从系统应用的角度分析遥感大数据处理中涉及的关键技术与问题,为相关研究人员提供有价值的参考。方法 在参考大量文献的基础上,首先阐明遥感大数据的特点。其次,从GPU硬件加速、集群、网格、云计算、云格、复杂高性能计算等角度介绍了遥感大数据处理系统。再次,从分布式集群化存储技术等,分析了遥感大数据处理的关键技术。最后,从遥感大数据的多类不确定性、信息融合、机器学习、分析平台等出发,说明了目前研究存在的问题;从遥感大数据多类不确定性建模,面向遥感大数据的机器学习方法等角度说明了遥感大数据发展的趋势。结果 本文详细梳理了遥感大数据的特点、典型的处理系统、核心技术,力图总结出在实际应用与学术研究中该领域需要解决的关键问题以及未来的发展趋势。结论 大数据技术为遥感数据挖掘与知识获取带来了机遇与挑战,面向大数据的机器学习、数据统一分析框架、面向大数据的信息深度融合等问题的突破,将促进遥感知识挖掘的进一步发展。     

Aerospatial remote sensing as catalyst of an operational marine fishery (halieutic) science

Abstract

A brief balance of the attainments and limits in the modern halieutic (fishery biology and economy) field is stricken and shows off the gap between the present management structure and the present problems. The surface tuna fishery case is thoroughly studied, considering its specificity and its economical importance. From there, it appears that conclusive strides will be made by halieutic science through the knowledge of the relationship between tuna and its environment. Some tuna environment studies are presented through typical remote sensing examples and introduce the basis of a coherent theory of tuna behaviour which can be schematically summarized by this basic general hypothesis; namely, that if tuna are present in a zone they will be inclined to aggregate close to any anomalies (gradients) of parameters in their environmental sensing field. From there, several models are presented in order to assess or forecast surface tuna stocks. A second part analyses the potential and contribution of the aerospatial remote sensing in the halieutic field through several original examples which point out clearly that this technique is one of the only tools able to visualize some essential concepts in oceanography and halieutic science. Last, we propose a study in several countries within the framework of several activities to integrate remote sensing tools in the fishery management structure, and to establish the data acquisition system in remote sensing satellites. From this study, a true operational halieutic field can be created.     

Detecting near-surface moisture stress in Sphagnum spp.

Estimating near-surface moisture conditions from the reflectance spectra (400-2500 nm) of Sphagnum moss offers great opportunities for the use of remote sensing as a tool for large-scale detailed monitoring of near-surface peatland hydrological conditions. This article investigates the effects of changes in near-surface and surface moisture upon the spectral characteristics of Sphagnum moss. Laboratory-based canopy reflectance data were collected from two common species of Sphagnum subjected to drying and subsequent rewetting. Several spectral indices developed from the near infra-red (NIR) and shortwave infra-red (SWIR) liquid water absorption bands and two biophysical indices (REIP and the chlorophyll index) were correlated with measures of near-surface moisture. All spectral indices tested were significantly correlated with near-surface moisture (with r between 0.27 and 0.94). The strongest correlations were observed using indices developed from the NIR liquid water absorption features (fWBI₉₈₀ and fWBI₁₂₀₀). However, a hysteretic response was observed in both NIR indices when the canopies were re-hydrated, a finding which may have implications for the timing of remote sensing image acquisition. The Moisture Stress Index (MSI), developed from the SWIR liquid water absorption feature also showed strong correlations with near-surface wetness although the range of moisture conditions over which the index was able to detect change was highly dependent on Sphagnum species. Of the biophysical spectral indices tested (REIP and the chlorophyll index), the most significant relationships were observed between the chlorophyll index and near-surface wetness. All spectral indices tested were species specific, and this is attributed to differences in canopy morphology between Sphagnum species. The potential for developing estimations of surface and near-surface hydrological conditions across northern peatlands using remote sensing technology is discussed.     

Variability of Landsat MSS spectral responses of forests in relation to stand and site characteristics

Abstract

The use of field measures of slope angle, slope aspect, cover type, crown size and crown density is evaluated in appraising the variability of Landsat Multispectral Scanner (MSS) spectral responses for 182 sample sites within Crater Lake National Park, Oregon. Multiple linear regression models indicate that 73, 72, 71 and 57 percent of the variation in the mean response of MSS bands 4, 5, 6 and 7, respectively, was explained by the environmental variables entered into the models. In general, crown size and crown density are less important in altering spectral response than terrain orientation. This type of analysis is useful in guiding field work for remote sensing studies into areas that are environmentally diverse and which are, therefore, capable of significantly altering the spectral response of cover types.     

应用遥感影像纹理特征提取线性体的马尔柯夫随机场模型

分析了遥感影像纹理的统计特征,利用马尔柯夫随机场能够合理地描述图像纹理的随机特征,建立了纹理特征马尔柯夫随机场模型,并且对该模型在提取线性体方面作了初步探讨。研究表明该方法在遥感图像线性体信息提取方面有着广阔的应用前景。     

基于遥感数据特征和像元替换技术的边缘提取方法

随着城市化的发展，城市遥感领域在中国发展迅速。高分辨遥感图像的边缘提取是当前城市遥感的重要研究领域。实践中，传统的边缘检测算子主要是通过图像空城特征微分，建立不同结构的模板完成。高分辨率遥感数字图像包括了空间域和光谱域两种信息，因此，借助于图像的光谱和空间域两种特征信息提高提取城市边缘信息精度已经成为当前算法开发的基本思路。该研究通过设计光谱分解和边缘检测的算子模板，综合利用了图像的光谱、空间特征信息。研究结果表明，这种方法有效地提高了城市遥感数据边缘信息的提取精度，同时还具有方法简便、计算速度怏的特点。     

Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data

ABSTRACT

Estimation of natural grassland biomass was carried out in a region located in the Brazilian Pampa, using field and remote sensing data and statistical models. The study was conducted in a grassland with a rotational grazing system, with grazing rest interval based on accumulated thermal sums 375 and 750 Degrees Day (DD). One image of the MSI (MultiSpectral Instrument) sensor aboard the Sentinel-2 satellite was evaluated and calibrated by 57 sampled biomass units collected in the field. Initially, the image was preprocessed, with extraction of the reflectance values of the Sentinel-2 bands, re-sampling of the pixels to 20 metres and calculation of vegetation indices. Data statistical analyses indicated significant correlations between field and remote sensing data. Multiple linear regression analyses were applied at each grazing rest interval using the remote sensing variables as predictors (independent) of the biomass (dependent). Among the variables, it is important to highlight the significant correlation of the red-edge bands with the biomass. The equations for estimating green biomass-presented coefficients of determination (R²) of R² = 0.51 for the rest interval 375 DD and R² = 0.65 for the rest interval 750 DD, while the senescent and total biomass generated adjustments with R² ≤ 0.50 for the two rest intervals. Biomass estimates results were satisfactory, regardless of the interval evaluated. Sampling schemes at different seasons of the year and further spectral and field variables (spectral and biomass) are suggested to improve even more the accuracy of the estimates.     

双卷积池化结构的3D-CNN高光谱遥感影像分类方法

目的 高光谱遥感影像数据包含丰富的空间和光谱信息,但由于信号的高维特性、信息冗余、多种不确定性和地表覆盖的同物异谱及同谱异物现象,导致高光谱数据结构呈高度非线性。3D-CNN（3D convolutional neural network）能够利用高光谱遥感影像数据立方体的特性,实现光谱和空间信息融合,提取影像分类中重要的有判别力的特征。为此,提出了基于双卷积池化结构的3D-CNN高光谱遥感影像分类方法。方法 双卷积池化结构包括两个卷积层、两个BN（batch normalization）层和一个池化层,既考虑到高光谱遥感影像标签数据缺乏的问题,也考虑到高光谱影像高维特性和模型深度之间的平衡问题,模型充分利用空谱联合提供的语义信息,有利于提取小样本和高维特性的高光谱影像特征。基于双卷积池化结构的3D-CNN网络将没有经过特征处理的3D遥感影像作为输入数据,产生的深度学习分类器模型以端到端的方式训练,不需要做复杂的预处理,此外模型使用了BN和Dropout等正则化策略以避免过拟合现象。结果 实验对比了SVM（support vector machine）、SAE（stack autoencoder）以及目前主流的CNN方法,该模型在Indian Pines和Pavia University数据集上最高分别取得了99.65%和99.82%的总体分类精度,有效提高了高光谱遥感影像地物分类精度。结论 讨论了双卷积池化结构的数目、正则化策略、高光谱首层卷积的光谱采样步长、卷积核大小、相邻像素块大小和学习率等6个因素对实验结果的影响,本文提出的双卷积池化结构可以根据数据集特点进行组合复用,与其他深度学习模型相比,需要更少的参数,计算效率更高。     

An improved strategy for regression of biophysical variables and Landsat ETM+ data

Empirical models are important tools for relating field-measured biophysical variables to remote sensing data. Regression analysis has been a popular empirical method of linking these two types of data to provide continuous estimates for variables such as biomass, percent woody canopy cover, and leaf area index (LAI). Traditional methods of regression are not sufficient when resulting biophysical surfaces derived from remote sensing are subsequently used to drive ecosystem process models. Most regression analyses in remote sensing rely on a single spectral vegetation index (SVI) based on red and near-infrared reflectance from a single date of imagery. There are compelling reasons for utilizing greater spectral dimensionality, and for including SVIs from multiple dates in a regression analysis. Moreover, when including multiple SVIs and/or dates, it is useful to integrate these into a single index for regression modeling. Selection of an appropriate regression model, use of multiple SVIs from multiple dates of imagery as predictor variables, and employment of canonical correlation analysis (CCA) to integrate these multiple indices into a single index represent a significant strategic improvement over existing uses of regression analysis in remote sensing.To demonstrate this improved strategy, we compared three different types of regression models to predict LAI for an agro-ecosystem and live tree canopy cover for a needleleaf evergreen boreal forest: traditional (Y on X) ordinary least squares (OLS) regression, inverse (X on Y) OLS regression, and an orthogonal regression method called reduced major axis (RMA). Each model incorporated multiple SVIs from multiple dates and CCA was used to integrate these. For a given dataset, the three regression-modeling approaches produced identical coefficients of determination and intercepts, but different slopes, giving rise to divergent predictive characteristics. The traditional approach yielded the lowest root mean square error (RMSE), but the variance in the predictions was lower than the variance in the observed dataset. The inverse method had the highest RMSE and the variance was inflated relative to the variance of the observed dataset. RMA provided an intermediate set of predictions in terms of the RMSE, and the variance in the observations was preserved in the predictions. These results are predictable from regression theory, but that theory has been essentially ignored within the discipline of remote sensing.     

融入邻域作用的高斯混合分割模型及简化求解

目的 基于高斯混合模型（GMM）的图像分割方法易受噪声影响,为此采用马尔可夫随机场（MRF）将像素邻域关系引入GMM,提高算法抗噪性。针对融入邻域作用的高斯混合分割模型结构复杂、参数估计困难,难以获得全局最优分割解等问题,提出一种融入邻域作用的高斯混合分割模型及其简化求解方法。方法 首先,构建融入邻域作用的GMM。为了提高GMM的抗噪性,采用MRF建模混合模型权重系数的先验分布。然后,利用贝叶斯理论建立图像分割模型,即品质函数;由于品质函数中参数较多（包括权重系数,均值,协方差）、函数结构复杂,导致参数求解困难。因此,将品质函数中的均值和协方差定义为权重系数的函数,由此简化模型结构并方便其求解;虽然品质函数中仅包含参数权重系数,但结构比较复杂,难以求得参数的解析式。最后,采用非线性共轭梯度法（CGM）求解参数,该方法仅需利用品质函数值和参数梯度值,降低了参数求解的复杂性,并且收敛快,可以得到全局最优解。结果 为了有效而准确地验证提出的分割方法,分别采用本文算法和对比算法对合成图像和高分辨率遥感图像进行分割实验,并定性和定量地评价和分析了实验结果。实验结果表明本文方法的有效抗噪性,并得到很好的分割结果。从参数估计结果可以看出,本文算法有效简化了模型参数,并获得全局最优解。结论 提出一种融入邻域作用的高斯混合分割模型及其简化求解方法,实验结果表明,本文算法提高了算法的抗噪性,有效地简化了模型参数,并得到全局最优参数解。本文算法对具有噪声的高分辨率遥感影像广泛适用。     

那曲典型草地植被光谱特征分析

遥感是大尺度生态研究的重要工具之一,而地面植物群落特征与其光谱特征之间的关系是解译遥感影像的关键。地面实测数据由于其高空间分辨率和高光谱分辨率,能够准确反映地物光谱信息,可以用来指导卫星遥感解译工作,同时为遥感监测草地退化、草地模型建立等提供数据支持。选取西藏那曲地区的优势植被类型作为研究对象,利用ASD FieldSpec 3便携式光谱仪测定优势种的冠层光谱并进行比较,并取其中一种优势种测量其在不同覆盖度和不同生长期的光谱反射特点。研究结果表明:①不同植被群落冠层光谱具有特殊的光谱曲线,可见光波段光谱反射率依次是紫花针茅、小嵩草和藏北嵩草,近红外波段光谱反射率则依次是小嵩草、藏北嵩草和紫花针茅;红边位置可以识别藏北嵩草,但是不能区分小嵩草和紫花针茅;②不同覆盖度的小嵩草红边、“绿峰”位置不随覆盖度的变化而发生变化;连续统去除后得到吸收深度随覆盖度的增加而变大,吸收峰面积随覆盖度的增加而增加;③小嵩草衰退期内,在可见光波段和红边波段,冠层光谱反射率随着叶绿素含量的减少而下降,出现“红边蓝移,绿峰下降”的现象。     

Spectral discrimination of phytoplankton colour groups: The effect of suspended particulate matter and sensor spectral resolution

Remote sensing has been used extensively to provide quantitative information on the distribution of phytoplankton in inland waters through the surrogate mapping of chlorophyll a, but as chlorophyll a is common to almost all species of phytoplankton it cannot provide any information on the taxonomic composition of phytoplankton communities. However, the varied optical properties of phytoplankton taxa may present a means to their discrimination via remote sensing data. This paper presents the results of an experimental study in which the spectral dissimilarities of brown, green, blue-green and red algae were examined with a view to establishing a basis upon which broad changes in phytoplankton communities might be monitored through remote sensing. Pseudo phytoplankton communities were simulated in a series of mesocosm experiments from which spectral reflectance measurements were acquired. The results demonstrated that the phytoplankton colour groups examined were indeed spectrally dissimilar. The spectral distinction between colour groups was noted to be greatest at high concentrations of chlorophyll a and between pseudo-communities dominated by a single species; spectral differences were lower in mixed pseudo-communities with co-dominant species. Moreover, it proved possible to quantify the concentration of two potential biomarker pigments, fucoxanthin and C-phycocyanin, through the derivation of simple spectral indices. The coincidental presence of varying concentrations of SPM (SPIM and SPOM) caused significant attenuation of the spectral response of the pseudo-communities and affected the accuracy of biomarker pigment estimation. It is considered that the realisation of a remote sensing technique for the discrimination of phytoplankton taxa in inland waters would be an extremely useful tool for limnological research and water resource management and thus the future application of this research to inland waters is also discussed.     

Learned societies,learned journals and other publications

Abstract

This paper summarizes those activities which have occurred in Europe as part of what might be described loosely as academic initiatives in remote sensing related to the dissemination of scientific results in the field of remote sensing; it does not attempt to discuss education and training. The various national societies devoted to remote sensing are listed and some information is given about their sizes. A number of journals and information source books originating within Europe are also described.     

Ground truth An underview †

Abstract

In an ideal world (for remote sensing) the nature of the surface would be completely specified by the spectral signature. In the real world, however, the compexity of natural surfaces, effects of the atmosphere and ambiguity of the spectral signatures act to limit remote sensing without ground truth to applications that demand little from the radiometric quantities in the data. Ground surveys are complementary to the synoptic overview provided by satellites, helping to link the image data to the surface context. This paper reviews the purposes and problems of such ground surveys and examines in particular the nature of the relationship between the object of inquiry and the spectral signature. The investigator must select object variables that are both appropriate to the application and well matched to the spectral signature. This problem is discussed in the context of vegetation canopies.