Geographic applications of remotely sensed data

The application of remote sensing to the study of geography as spatially distributed phenomena began in the late 1800s when German foresters used photography taken from a balloon to produce forest maps. Since this early beginning researchers and professionals in government, industry, and educational institutions have become increasingly aware of the ability of remote sensing to provide geographic information. This is information on the spatial distribution and temporal dynamics of objects and processes in the world around us. Geographic applications of remotely sensed data typically take one of four explanatory forms and are aimed at one or more of three practical objectives. Explanatory forms include: 1) morphometric analysis, 2) cause and effect analysis, 3) temporal analysis, 4) functional and ecological systems analysis. Objectives include: 1) mapping, 2) monitoring, 3) modeling. As we have moved from using manual analysis techniques on black-and-white aerial photography for mapping distributions to machine-assisted analysis of multispectral satellite data in digital form for analyzing complex environmental processes, the techniques and procedures involved in the use of remotely sensed data have become more complex. Remote sensing does offer a tremendous potential to the community involved in geographic applications. To realize this potential will continue to require a balanced program of both basic and applied research.     

Minimum-volume transforms for remotely sensed data

Scatter diagrams for multispectral remote sensing data tend to be triangular, in the two-band case, pyramidal for three bands, and so on. They radiate away from the so-called darkpoint, which represents the scanner's response to an un-illuminated target. A minimum-volume transform may be described (provisionally) as a nonorthogonal linear transformation of the multivariate data to new axes passing through the dark point, with directions chosen such that they (for two bands), or the new coordinate planes (for three bands, etc.) embrace the data cloud as tightly as possible. The reason for the observed shapes of scatter diagrams is to be found in the theory of linear mixing at the subfootprint scale. Thus, suitably defined, minimum-volume transforms can often be used to unmix images into new spatial variables showing the proportions of the different cover types present, a type of enhancement that is not only intense, but physically meaningful. The present paper furnishes details for constructing computer programs to effect this operation. It will serve as a convenient technical source that may be referenced in subsequent, more profusely illustrated publications that address the intended application, the mapping of surface mineralogy     

Measuring trace gases in plumes from hyperspectral remotely sensed data

A method [joint reflectance and gas estimator (JRGE)] is developed to estimate a set of atmospheric gas concentrations in an unknown surface reflectance context from hyperspectral images. It is applicable for clear atmospheres without any aerosol in a spectral range between approximately 800 and 2500 nm. Standard gas by gas methods yield a 6% rms error in H/sub 2/O retrieval from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, reaching several tens percent for a set of widespread ground materials and resulting from an simplifying assumption of linear variations of the reflectance model within gas absorption bands and partial accounting of the gas induced signal. JRGE offers a theoretical framework consisting in a two steps algorithm that accounts for sensor characteristics, assumptions on gas concentrations and reflectance variations. It estimates variations in gas concentrations relatively to a standard atmosphere model. An adaptive cubic smoothing spline like estimation of the reflectance is first performed. Concentrations of several gaseous species are then simultaneously retrieved using a nonlinear procedure based on radiative transfer calculations. Applied to AVIRIS spectra simulated from reflectance databases and sensor characteristics, JRGE reduces the errors in H/sub 2/O retrieval to 2.87%. For an AVIRIS image acquired over the Quinault prescribed fire, far field CO/sub 2/ estimate (348 ppm, about 6% to 7% rms) is in agreement with in situ measurement (345-350 ppm) and aerosols yield an underestimation of total atmospheric CO/sub 2/ content equal to 5.35% about 2 km downwind the fire. JRGE smoothes and interpolates the reflectance for gas estimation but also provides nonsmoothed reflectance spectra. JRGE is shown to preserve various mineral absorption features included in the AVIRIS image of Cuprite Mining District test site.     

基于高光谱数据的叶面积指数遥感反演

文中耦合叶片辐射传输模型（PROSPECT）和冠层辐射传输模型（SAILH）,基于高光谱载荷通道设置,模拟高光谱冠层反射率数据;利用模拟数据深入分析了不同植被指数与叶面积指数之间的敏感性;通过敏感性分析发现改进型叶绿素吸收植被指数（MCARI2）具备抗土壤背景因素的影响能力,而且对叶面积指数较为敏感,因此该研究建立植被指数MCARI2 与叶面积指数之间的经验统计模型,并用于高光谱数据进行叶面积指数反演;最后利用飞行同步测量的叶面积指数对反演模型进行精度分析。结果表明:相比实测叶面积指数,文中建立的反演模型约低估0.42,该反演模型能够较好的反映出地物真实叶面积指数。     

A method for tracking individual planetary waves in remotely sensed data

We describe a methodology for tracking individual planetary waves in longitude-time plots of satellite data, based on fitting an elementary wave shape model to subsets of the data by maximum likelihood, then reconstructing the trajectory and evolution of every single wave (where for "single wave" we mean an individual positive or negative westward propagating anomaly) by joining the elementary waves according to their similarity. We then illustrate the potential of the methodology with an example at 34/spl deg/N in the Atlantic Ocean and its adaptability to different cases with a second example on eastward-propagating Kelvin waves in the equatorial Pacific. Although the examples given use sea surface height anomaly data, the technique lends itself to be applied to any space-time plot of any dataset displaying propagation and, in particular, to sea surface temperature data.     

云计算环境下多源遥感数据挖掘系统构建

针对多源遥感数据未被充分利用及数据处理效率较低的问题,结合三层四类模式的云计算服务级别系统,以云计算服务形式为前提,创建了多来源数据挖掘体系。首先阐述了多源遥感数据挖掘的技术流程;其次将云计算服务层次体系引入到系统构建中,设计了云计算环境下的多源遥感数据挖掘系统框架;最后详细介绍了该系统的主要特色功能,为进一步研究多源遥感数据挖掘系统奠定了基础。     

Analysis of remotely sensed data: the formative decades and the future

Developments in the field of image understanding in remote sensing over the past four decades are reviewed, with an emphasis, initially, on the contributions of David Landgrebe and his colleagues at the Laboratory for Applications of Remote Sensing, Purdue University. The differences in approach required for multispectral, hyperspectral and radar image data are emphasised, culminating with a commentary on methods commonly adopted for multisource image analysis. The treatment concludes by examining the requirements of an operational multisource thematic mapping process, in which it is suggested that the most practical approach is to analyze each data type separately, by techniques optimized to that data's characteristics, and then to fuse at the label level.     

On the use of remotely sensed data to estimate spatially averaged geophysical variables

This paper examines certain errors that arise when estimating spatially averaged geophysical parameter fields from some satellite and other earth observation measurement systems. Of particular concern is the error that is caused by nonuniform sampling of radiance within the field of view. This effect causes a proportion of the amplitude of surface fluctuations in the parameter to be inherited as uncertainty by the estimated area average. This proportion is small if the correlation length for variations of the surface parameter is either much larger, or much smaller, than the size of the instrument's resolution element (suitably defined). It may be significant when the two length scales are similar. For Gaussian spatial sampling functions, it is shown that the error is minimized if the area over which the simple average is required is about one and a half to two times the square of the projected full width at half maximum of the Gaussian. The results are used to study the problem of estimating surface fractional cover from linear mixture models, where it is confirmed that significant errors can be encountered if the estimated fractions are carelessly assigned to areas defined by the sampling interval of an imaging system.     

Multisource classification of remotely sensed data: fusion ofLandsat TM and SAR images

Proposes a new method for statistical classification of multisource data. The method is suited for land-use classification based on the fusion of remotely sensed images of the same scene captured at different dates from multiple sources. It incorporates a priori information about the likelihood of changes between the acquisition of the different images to be fused. A framework for the fusion of remotely sensed data based on a Bayesian formulation is presented. First, a simple fusion model is given, and then the basic model is extended to take into account the temporal attribute if the different data sources are acquired at different dates. The performance of the model is evaluated by fusing Landsat TM images and ERS-1-SAR images for land-use classification. The fusion model gives significant improvements in the classification error rates compared to the conventional single-source classifiers     

Fuzzy rule-based classification of remotely sensed imagery

The purpose of this paper is to investigate the applicability of fuzzy rule-based modeling to classify a LANDSAT TM scene from 1984 of an area located in the south of Germany. Both a land cover map with four different categories and an image depicting the degree of ambiguity of the classification for each pixel is the expected output. The fuzzy classification algorithm will use a rule system derived from a training set using simulated annealing as an optimization algorithm. The results are then validated and compared with a common classification method in order to judge the effectiveness of the proposed technique. It will also be shown that the proposed method with only nine rules for four different land cover classes performs slightly better than the maximum likelihood classifier (MLC). For error assessment, the traditional error matrix and fuzzy operators have been used     

Badly posed classification of remotely sensed images-an experimental comparison of existing data labeling systems

Although underestimated in practice, the small/unrepresentative sample problem is likely to affect a large segment of real-world remotely sensed (RS) image mapping applications where ground truth knowledge is typically expensive, tedious, or difficult to gather. Starting from this realistic assumption, subjective (weak) but ample evidence of the relative effectiveness of existing unsupervised and supervised data labeling systems is collected in two RS image classification problems. To provide a fair assessment of competing techniques, first the two selected image datasets feature different degrees of image fragmentation and range from poorly to ill-posed. Second, different initialization strategies are tested to pass on to the mapping system at hand the maximally informative representation of prior (ground truth) knowledge. For estimating and comparing the competing systems in terms of learning ability, generalization capability, and computational efficiency when little prior knowledge is available, the recently published data-driven map quality assessment (DAMA) strategy, which is capable of capturing genuine, but small, image details in multiple reference cluster maps, is adopted in combination with a traditional resubstitution method. Collected quantitative results yield conclusions about the potential utility of the alternative techniques that appear to be realistic and useful in practice, in line with theoretical expectations and the qualitative assessment of mapping results by expert photointerpreters.     

Constrained subpixel target detection for remotely sensed imagery

Target detection in remotely sensed images can be conducted spatially, spectrally or both. The difficulty of detecting targets in remotely sensed images with spatial image analysis arises from the fact that the ground sampling distance is generally larger than the size of targets of interest in which case targets are embedded in a single pixel and cannot be detected spatially. Under this circumstance target detection must be carried out at subpixel level and spectral analysis offers a valuable alternative. In this paper, the problem of subpixel spectral detection of targets in remote sensing images is considered, where two constrained target detection approaches are studied and compared. One is a target abundance-constrained approach, referred to as nonnegatively constrained least squares (NCLS) method. It is a constrained least squares spectral mixture analysis method which implements a nonnegativity constraint on the abundance fractions of targets of interest. Another is a target signature-constrained approach, called constrained energy minimization (CEM) method. It constrains the desired target signature with a specific gain while minimizing effects caused by other unknown signatures. A quantitative study is conducted to analyze the advantages and disadvantages of both methods. Some suggestions are further proposed to mitigate their disadvantages     

Use of radar and optical remotely sensed data for soil moisture retrieval over vegetated areas

This work assesses the possibility of obtaining soil moisture maps of vegetated fields using information derived from radar and optical images. The sensor and field data were acquired during the SMEX'02 experiment. The retrieval was obtained by using a Bayesian approach, where the key point is the evaluation of probability density functions (pdfs) based on the knowledge of soil parameter measurements and of the corresponding remotely sensing data. The purpose is to determine a useful parameterization of vegetation backscattering effects through suitable pdfs to be later used in the inversion algorithm. The correlation coefficients between measured and extracted soil moisture values are R=0.68 for C-band and R=0.60 for L-band. The pdf parameters have been found to be correlated to the vegetation water content estimated from a Landsat image with correlation coefficients of R=0.65 and 0.91 for C- and L-bands, respectively. In consideration of these correlations, a second run of the Bayesian procedure has been performed where the pdf parameters are variable with vegetation water content. This second procedure allows the improvement of inversion results for the L-band. The results derived from the Bayesian approach have also been compared with a classical inversion method that is based on a linear relationship between soil moisture and the backscattering coefficients for horizontal and vertical polarizations.     

Estimation of subpixel target size for remotely sensed imagery

One of the challenges in remote sensing image processing is subpixel detection where the target size is smaller than the ground sampling distance, therefore, embedded in a single pixel. Under such a circumstance, these targets can be only detected spectrally at the subpixel level, not spatially as ordinarily conducted by classical image processing techniques. This paper investigates a more challenging issue than subpixel detection, which is the estimation of target size at the subpixel level. More specifically, when a subpixel target is detected, we would like to know "what is the size of this particular target within the pixel?". The proposed approach is to estimate the abundance fraction of a subpixel target present in a pixel, then find what portion it contributes to the pixel that can be used to determine the size of the subpixel target by multiplying the ground sampling distance. In order to make our idea work, the subpixel target abundance fraction must be accurately estimated to truly reflect the portion of a subpixel target occupied within a pixel. So, a fully constrained linear unmixing method is required to reliably estimate the abundance fractions of a subpixel target for its size estimation. In this paper, a recently developed fully constrained least squares linear unmixing is used for this purpose. Experiments are conducted to demonstrate the utility of the proposed method in comparison with an unconstrained linear unmixing method, unconstrained least squares method, two partially constrained least square linear unmixing methods, sum-to-one constrained least squares, and nonnegativity constrained least squares.     

Comparison of the multilayer perceptron with neuro-fuzzy techniquesin the estimation of cover class mixture in remotely sensed data

Mixed pixels are a major source of inconvenience in the classification of remotely sensed data. This paper compares MLP with so-called neuro-fuzzy algorithms in the estimation of pixel component cover classes. Two neuro-fuzzy networks are selected from the literature as representatives of soft classifiers featuring different combinations of fuzzy set-theoretic principles with neural network learning mechanisms. These networks are: 1) the fuzzy multilayer perceptron (FMLP) and 2) a two-stage hybrid (TSH) learning neural network whose unsupervised first stage consists of the fully self-organizing simplified adaptive resonance theory (FOSART) clustering model, FMLP, TSH, and MLP are compared on CLASSITEST, a standard set of synthetic images where per-pixel proportions of cover class mixtures are known a priori. Results are assessed by means of evaluation tools specifically developed for the comparison of soft classifiers. Experimental results show that classification accuracies of FMLP and TSH are comparable, whereas TSH is faster to train than FMLP. On the other hand, FMLP and TSW outperform MLP when little prior knowledge is available for training the network, i.e., when no fuzzy training sites, describing intermediate label assignments, are available     

Classification of remotely sensed images using decimal coded morphological profiles

In this paper, we propose a novel method for pixel classification of remotely sensed images. The proposed method exploits the spatial information of image pixels using morphological profiles produced by structuring elements of different sizes and shapes. Morphological profiles produced by multiple structuring elements are combined into a single feature by decimal coding. The advantage of proposed feature is that it can effectively utilize the potential of multiple morphological profiles without increasing the complexity of feature space. The proposed approach was tested on remotely sensed images with known ground truths, and performance was improved up to 27 % in the overall accuracy results over existing techniques.     

Design of neural networks for classification of remotely sensed imagery

As currently planned, future Earth remote sensing platforms (i.e., Earth Observing System [EOS]) will be capable of generating data at a rate of over 50 Megabits per second. To address this issue the Intelligent Data Management (IDM) project at NASA/GSFC has prototyped an Intelligent Information Fusion System (IIFS) that uses backpropagation neural networks for the classification of remotely sensed imagery. This is part of the IDM strategy of providing archived data to a researcher through a variety of discipline-specific indices. In this paper we discuss classification accuracies of a backpropagation neural network and compare it with a maximum likelihood classifier (MLC) with multivariate normal class models. We have found that, because of its nonparametric nature, the neural network outperforms the MLC in this area. In addition, we discuss techniques for constructing optimal neural nets on parallel hardware like the MasPar MP-1 currently at NASA/GSFC. Other important discussions are centered around training and classification times of the two methods, and sensitivity to the training data. Finally we discuss future work in the area of classification and neural nets.