Estimating leaf area index from satellite data

A method for estimating leaf area index from visible and near infrared measurements of vegetation above a soil background is applied to a Landsat Thematic Mapper data set. Some constants required for the procedure are inferred from the scattergram of data values. The resulting image illustrates variability of leaf area index over an agricultural area. The mixed-pixel case, corresponding to low-resolution data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) is also discussed, and a vegetation index is suggested for both high- and low-resolution data. Consideration of the two types of data leads to the suggestion that a sampled high spatial resolution sensor (50-100 m) be added to the AVHRR in order to permit accurate inference of vegetation conditions over agricultural areas     

Algorithm for global leaf area index retrieval using satellite imagery

Leaf area index (LAI) is one of the most important Earth surface parameters in modeling ecosystems and their interaction with climate. Based on a geometrical optical model (Four-Scale) and LAI algorithms previously derived for Canada-wide applications, this paper presents a new algorithm for the global retrieval of LAI where the bidirectional reflectance distribution function (BRDF) is considered explicitly in the algorithm and hence removing the need of doing BRDF corrections and normalizations to the input images. The core problem of integrating BRDF into the LAI algorithm is that nonlinear BRDF kernels that are used to relate spectral reflectances to LAI are also LAI dependent, and no analytical solution is found to derive directly LAI from reflectance data. This problem is solved through developing a simple iteration procedure. The relationships between LAI and reflectances of various spectral bands (red, near infrared, and shortwave infrared) are simulated with Four-Scale with a multiple scattering scheme. Based on the model simulations, the key coefficients in the BRDF kernels are fitted with Chebyshev polynomials of the second kind. Spectral indices - the simple ratio and the reduced simple ratio - are used to effectively combine the spectral bands for LAI retrieval. Example regional and global LAI maps are produced. Accuracy assessment on a Canada-wide LAI map is made in comparison with a previously validated 1998 LAI map and ground measurements made in seven Landsat scenes.     

基于数据分割与主成分分析的LAI遥感估算

针对叶面积指数(LAI)经典统计反演模型存在估算效果不理想以及反演效率低等问题,提出了一种基于农学物候的数据分割与主成分分析结合的遥感估算方法.综合了原始光谱和微分(或差分)光谱主成分信息作为自变量,融入了以农学物候为先验的数据分割思想,并引入了多尺度建模方式参与反演过程.以冬小麦为实验对象,进行数值模拟和比较分析.结...     

基于卫星云图的云分类研究

各种类型云的辐射特性以及分布情况,对大气收支平衡以及天气气候都有重大影响,对云进行正确分类是遥感领域的重要应用和研究热点.文章基于对卫星云图进行自动准确识别和分类研究的前提,通过介绍几种特征提取和选择方法,以及介绍无监督、有监督和神经网络3类云分类研究常用分类方法,对国内外近几十年来所做的卫星云图分类研究进行综述介绍.并简要介绍了云分类结果的评价方法,对分类研究的结果进行定性讨论.     

Delay performance of VSAT-based satellite wide area networks

This paper studies the performance of VSAT-based satellite wide area networks. Very small aperture terminals are used as transport gateways to interconnect local area networks through satellites to form a wide area network. This paper concentrates on analysing the delay of the network, which is the time needed to send a packet from a station in the source LAN to a station in the destination LAN. Several access techniques are considered for comparison. It is observed through numerical examples that TDMA on the up-link and TDM on the down-link offers the best delay performance.     

Estimating one-dimensional models from frequency-domainelectromagnetic data using modular neural networks

An artificial neural network interpretation system is being used to interpret data from a frequency-domain electromagnetic (EM) geophysical system in near real time. The interpretation system integrates 45 separate networks in a data visualization shell. The networks produce interpretations at three different transmitter-receiver (Tx-Rx) separations for half-space and layered-Earth interpretations. Modular neural networks (MNNs) were found to be the only paradigm that could successfully perform the layered-Earth interpretations. An MNN with 16 inputs, five local experts, each with seven hidden processing elements, and three outputs was trained on 4795 patterns for 200 epochs. For two-layer models with a resistivity contrast greater than 2:1, resistivity estimates had greater than 96% accuracy for the first-layer resistivity, greater than 98% for the second-layer resistivity, and greater than 96% for the thickness of the first layer. If the contrast is less than 2:1, the resistivity accuracies are unaffected but thickness estimates for layers less than 2 m are unreliable. A Tx-Rx separation of 16 m with maximum depth of penetration of 8 m was assumed for the example cited     

Mobile internet access using satellite networks

Satellites offer a promising alternative for mobile access to the Internet by both pedestrians, and more importantly, from vehicles. As such, satellites provide an essential complement to the cellular radio (UMTS) infrastructure in sparsely populated areas where high bandwidth UMTS cells cannot be economically deployed. In this paper, we analyse various mobile Internet applications in representative urban scenarios for two LEO constellations (one with polar orbits and the other with inclined orbits), as well as for some simple GEO configurations. To this end, we develop a satellite channel propagation model that includes shadowing from surrounding building skylines based on actual data in a built‐up area. Using these tools, we analyse various Internet applications and the performance of various TCP schemes in different topologies. Copyright © 2004 John Wiley & Sons, Ltd.     

利用可见/近红外光谱测定小麦叶面积指数的改进研究

改进了小麦叶面积指数的可见/近红外光谱测定模型。以不同方法实现了小麦冠层反射光谱的预处理,并采用偏最小二乘回归算法（PLS）建立小麦叶面积指数估测模型对其进行比较分析,发现小波除噪结合一阶导数能最有效地消除原始光谱的噪声与背景信息,此时PLS模型校正集与预测集R2分别为0.849与0.835。为进一步优化模型,对经一阶导数结合小波除噪后的光谱采用主成分分析法（PCA）降维,以前4个主成分（含原始光谱84.867%特征信息）为输入变量,采用小二乘支撑向量机回归算法（LS-SVR）建立了小麦叶面积指数估测模型,其校正集与预测集R2分别达0.905与0.883,具有比PLS算法更高的精度。结果表明:以小波除噪结合一阶导数去除小麦冠层反射光谱中的土壤背景信息以提高模型精度是可行的,且LS-SVR是建模的优选方法。     

Geometric correction algorithms for satellite imagery using abi-directional scanning sensor

High-precision geometric correction algorithms for a bidirectionally scanned image that are based on separate geometric models according to the distortion characteristics are described. The modeling accuracy is evaluated through the use of simulated and observed Landsat Thematic Mapper (TM) images. TM images include a discontinuous distortion and a high-frequency distortion due to attitude fluctuations and bidirectional scans. A geometric model consisting of a single-valued-function is used to correct these distortions. The iteration calculation of the mapping cannot be converged, resulting in an enormous amount of calculation and data. Therefore, geometric models consisting of separate geometric models of the bidirectional scans and frequency components are proposed     

Estimating inhomogeneous fields using wireless sensor networks

Sensor networks have emerged as a fundamentally new tool for monitoring spatial phenomena. This paper describes a theory and methodology for estimating inhomogeneous, two-dimensional fields using wireless sensor networks. Inhomogeneous fields are composed of two or more homogeneous (smoothly varying) regions separated by boundaries. The boundaries, which correspond to abrupt spatial changes in the field, are nonparametric one-dimensional curves. The sensors make noisy measurements of the field, and the goal is to obtain an accurate estimate of the field at some desired destination (typically remote from the sensor network). The presence of boundaries makes this problem especially challenging. There are two key questions: 1) Given n sensors, how accurately can the field be estimated? 2) How much energy will be consumed by the communications required to obtain an accurate estimate at the destination? Theoretical upper and lower bounds on the estimation error and energy consumption are given. A practical strategy for estimation and communication is presented. The strategy, based on a hierarchical data-handling and communication architecture, provides a near-optimal balance of accuracy and energy consumption.     

基于SUSAN算法的遥感图像去云

提出了一种基于SUSAN算法自动消去主图像中云区遮挡影响的方法。该方法首先利用SUSAN算法有效提取主图像和参考图像中的角点,然后采用概率松弛法寻找主图像和参考图像间的匹配点,将主图像与参考图像配准,在此基础上搜索主图像的云区,最后进行融合得到去云图像。实验结果表明该方法具有较好的去云效果。     

Predicting cell phone adoption metrics using machine learning and satellite imagery

Approximately half of the global population does not have access to the internet, even though digital connectivity can reduce poverty by revolutionizing economic development opportunities. Due to a lack of data, Mobile Network Operators and governments struggle to effectively determine if infrastructure investments are viable, especially in greenfield areas where demand is unknown. This leads to a lack of investment in network infrastructure, resulting in a phenomenon commonly referred to as the ‘digital divide’. In this paper we present a machine learning method that uses publicly available satellite imagery to predict telecoms demand metrics, including cell phone adoption and spending on mobile services, and apply the method to Malawi and Ethiopia. Our predictive machine learning approach consistently outperforms baseline models which use population density or nightlight luminosity, with an improvement in data variance prediction of at least 40%. The method is a starting point for developing more sophisticated predictive models of infrastructure demand using machine learning and publicly available satellite imagery. The evidence produced can help to better inform infrastructure investment and policy decisions.     

星载激光雷达GLAS与TM光学遥感联合反演森林叶面积指数

通过对地球科学激光测高系统(Geoscience Laser Altimeter System,GLAS)波形数据进行高斯分解,提取精确的波形特征信息,计算出GLAS波形数据激光穿透指数(LPI),基于LPI提出GLAS数据反演叶面积指数(LAI)的新方法,建立了GLAS数据反演森林LAI的模型(R2=0.84,RMSE=0.64),并用留一交叉验证法(LOOCV)对反演模型的可靠性进行了验证,结果表明,该模型没有过度拟合,具有很好的泛化能力,最后通过人工神经网络融合GLAS与TM(Thematic Mapper,专题制图仪)遥感数据实现区域尺度森林LAI反演,用25个实测LAI对反演精度进行了验证,研究表明反演LAI与实测值较为接近,精度较高(R2=0.76,RMSE=0.69),为生态环境研究提供精确的输入参数,为GLAS数据大区域高精度LAI反演提供新的方法和思路.     

Existential uncertainty of spatial objects segmented from satellite sensor imagery

This research addresses existential uncertainty of spatial objects derived from satellite sensor imagery. An image segmentation technique is applied at various values of splitting and merging thresholds. We test the hypothesis that objects occurring at many segmentation steps have less existential uncertainty than those occurring at only a few steps.     

Removal of terrain effects from SAR satellite imagery of Arctictundra

Synthetic aperture radar (SAR) images of the Earth's terrestrial surface contain geometric and radiometric image effects which are caused by varying terrain elevation and slope. The radiometric effects tend to mask signal variations caused by other physical variables such as soil moisture and surface vegetation type, which are known to influence SAR backscatter signals. As a result, raw SAR images are of limited use in classifying surface vegetation type or quantifying the spatial distribution of soil moisture in regions of terrain relief, The authors present a technique for removing radiometric terrain effects from SAR images. Image correction was carried out in two steps. First, an existing modeling package was used in combination with digital elevation data in order to map the raw image pixels onto a geodetic coordinate system, thereby removing the geometric portion of the image distortion. Radiometric effects were then removed with the aid of a backscatter model which treats the reflected radiation as a combination of diffuse-Lambertian and specular components. Parameters in the backscatter model were determined by comparing two C-band SAR images of a test area in a region of Arctic tundra which were taken from ascending and descending orbit tracks of the ERS-1 satellite. The ascending and descending images displayed reductions in pixel value variance of 30% and 13%, respectively, after processing. Direct comparison of the two test area images reveals a dramatic improvement in image similarity after processing     

Higher resolution Earth surface features from repeat moderateresolution satellite imagery

This paper demonstrates that a high-resolution reflectivity model used in conjunction with an instrument pointspread function (PSF) can both determine georegistration parameters of coarse resolution sensors and improve the spatial resolution by compositing noncoincident repeat satellite data. To demonstrate this ability, an ideal location is selected and several first principle assumptions are made to simplify the reflectivity model. Twenty-three 1-km advanced very high-resolution radiometer (AVHRR) images are composited by using a Bayesian statistical sampling technique to yield estimates of a simple terrain-based reflectivity model with 180-m resolution. The terrain values are determined from a 90-m resolution digital elevation model (DEM). The Bayesian technique uses the AVHRR data to iteratively determine the most likely values for the model spectral albedos contained within an AVHRR field of view. Model predicted radiances for the repeat AVHRR footprints are computed by integrating model albedo values over the AVHRR PSF. As a first-order verification, simulated AVHRR reflectivities are shown to reconstruct well a smoothly varying prescribed albedo field. Comparisons of the composited real AVHRR image result with Landsat Multi Spectral Scanner (MSS) data show that the model reconstruction resolves surface features, which are not resolved in a single AVHRR image     

Ideal observer approximation using Bayesian classification neural networks

It is well understood that the optimal classification decision variable is the likelihood ratio or any monotonic transformation of the likelihood ratio. An automated classifier which maps from an input space to one of the likelihood ratio family of decision variables is an optimal classifier or "ideal observer." Artificial neural networks (ANNs) are frequently used as classifiers for many problems. In the limit of large training sample sizes, an ANN approximates a mapping function which is a monotonic transformation of the likelihood ratio, i.e., it estimates an ideal observer decision variable. A principal disadvantage of conventional ANNs is the potential over-parameterization of the mapping function which results in a poor approximation of an optimal mapping function for smaller training samples. Recently, Bayesian methods have been applied to ANNs in order to regularize training to improve the robustness of the classifier. The goal of training a Bayesian ANN with finite sample sizes is, as with unlimited data, to approximate the ideal observer. We have evaluated the accuracy of Bayesian ANN models of ideal observer decision variables as a function of the number of hidden units used, the signal-to-noise ratio of the data and the number of features or dimensionality of the data. We show that when enough training data are present, excess hidden units do not substantially degrade the accuracy of Bayesian ANNs. However, the minimum number of hidden units required to best model the optimal mapping function varies with the complexity of the data.     

Switching activity estimation of VLSI circuits using Bayesian networks

Switching activity estimation is an important aspect of power estimation at circuit level. Switching activity in a node is temporally correlated with its previous value and is spatially correlated with other nodes in the circuit. It is important to capture the effects of such correlations while estimating the switching activity of a circuit. In this paper, we propose a new switching probability model for combinational circuits that uses a logic-induced directed-acyclic graph (LIDAG) and prove that such a graph corresponds to a Bayesian network (BN), which is guaranteed to map all the dependencies inherent in the circuit. BNs can be used to effectively model complex conditional dependencies over a set of random variables. The BN inference schemes serve as a computational mechanism that transforms the LIDAG into a junction tree of cliques to allow for probability propagation by local message passing. The proposed approach is accurate and fast. Switching activity estimation of ISCAS and MCNC circuits with random and biased input streams yield high accuracy (average mean error=0.002) and low computational time (average elapsed time including CPU, memory access and I/O time for the benchmark circuits=3.93 s).     

Vehicle detection in aerial surveillance using dynamic Bayesian networks

We present an automatic vehicle detection system for aerial surveillance in this paper. In this system, we escape from the stereotype and existing frameworks of vehicle detection in aerial surveillance, which are either region based or sliding window based. We design a pixelwise classification method for vehicle detection. The novelty lies in the fact that, in spite of performing pixelwise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. We consider features including vehicle colors and local features. For vehicle color extraction, we utilize a color transform to separate vehicle colors and nonvehicle colors effectively. For edge detection, we apply moment preserving to adjust the thresholds of the Canny edge detector automatically, which increases the adaptability and the accuracy for detection in various aerial images. Afterward, a dynamic Bayesian network (DBN) is constructed for the classification purpose. We convert regional local features into quantitative observations that can be referenced when applying pixelwise classification via DBN. Experiments were conducted on a wide variety of aerial videos. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging data set with aerial surveillance images taken at different heights and under different camera angles.     

Packet-switched local area networks using wavelength-selectivestation couplers

Many LAN designs have been considered using passive star and passively tapped ring and bus architectures. Similar networks can also be designed using active wavelength-selective coupling devices such as those based on acoustooptics. A major advantage of actively coupled designs is that functions such as station slot synchronization are greatly simplified. In this paper, we consider the use of active couplers as station taps in networks that support packet-switched modes of operation. When used for this purpose, a major source of complexity results from the fact that at a given time, an unknown number of stations may be inserted into a particular wavelength-division multiplexing bus. For this reason, global slot-timing information cannot be derived from any one particular channel. As a result, when conventional protocols are used, the system may suffer from “retuning collision”, where a station destroys transit packets on other channels when the coupler is inadvertently retuned. We investigate protocols that can avoid this problem. In the simplest protocol, the overhead drastically restrict the range of physical parameters over which efficient operation can be achieved. The more sophisticated ones significantly extend this range so that metropolitan coverage is possible at reasonable data rates. In addition, we consider the use of active station taps in multichannel dual-bus networks. Capacity, delay, and power models are also derived, and comparisons are made with previous systems including passively tapped networks