The retrieval of biophysical variables using canopy reflectance models is hampered by the fact that the inverse problem is ill-posed. This leads to unstable and often inaccurate inversion results. In order to regulari...
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The retrieval of biophysical variables using canopy reflectance models is hampered by the fact that the inverse problem is ill-posed. This leads to unstable and often inaccurate inversion results. In order to regularize the model inversion, a novel approach has been developed and tested on synthetic Landsat TM reflectance data. The method takes into account the neighbouring radiometric information of the pixel of interest, named object signature. The neighbourhood data can either be extracted from gliding windows, already segmented images, or using digitized field boundaries. The extracted radiometric data of the neighbourhood pixels are used to calculate 42 descriptive statistical properties that comprehensively characterize the spectral (co)variance of the image object (e.g. mean and standard deviation of the distributions, intercorrelations between spectral bands, etc.). Together with the habitual spectral signature of the pixel being inverted (6 variables), this object signature (42 variables) is used as input in an artificial neural net to estimate simultaneously three important biophysical variables (i.e. leaf area index, leaf chlorophyll, and leaf water content). The use of neural nets for the model inversion avoids time-consuming iterative optimizations and provides a computational effective way to consider simultaneously pixel and object signatures. In order to "learn'' the relation between spectral signatures and biophysical variables, the neural nets were previously trained on large synthetic data sets. The data sets consist of pixel signatures and the corresponding signatures of image objects representing various agricultural fields. The signatures were simulated with the SAILH+PROSPECT canopy reflectance model, assuming largely varying intra-and interfield distributions of the model input parameters. To demonstrate the benefits of the object-based inversion, neural nets were also trained on the pixel signatures alone. For this purpose, the object signat
Numerical reservoir models are extensively used to determine in-place hydrocarbon resources and recoverable reserves. Geostatistical techniques are being increasingly used to construct such numerical models. The secon...
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Numerical reservoir models are extensively used to determine in-place hydrocarbon resources and recoverable reserves. Geostatistical techniques are being increasingly used to construct such numerical models. The second edition of the GSLIB software together with other publicly available software provides the tools needed to construct realistic models of large and complex reservoirs. This paper describes how to model lithofacies, porosity and permeability in sedimentary formations, particularly petroleum reservoirs, with publicly available software. Programs for postprocessing tasks such as scale up and management of multiple realizations are also presented. The relative merits of expensive commercial software are discussed. (C) 1999 Elsevier Science Ltd. All rights reserved.
This paper focuses on information modeling and computing technologies that are most relevant to the emerging software for the Architecture, Engineering, and Construction (AEC) industry. After a brief introduction to t...
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This paper focuses on information modeling and computing technologies that are most relevant to the emerging software for the Architecture, Engineering, and Construction (AEC) industry. After a brief introduction to the AEC industry and its present state of computer-based information sharing and collaboration, a set of requirements for AEC information models are identified. Next, a number of key information modeling and standards initiatives for the AEC domain are briefly discussed followed by a review of the emerging object and Internet technologies. The paper will then present our perspective on the challenges and potential directions for using object-based information models in a new generation of AEC software systems intended to offer component-based open architecture for distributed collaboration.
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