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Stien Heremans Bert Bossyns Herman Eerens Jos Van Orshoven 《International Journal of Applied Earth Observation and Geoinformation》2011
Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications of satellite images. They have successfully been applied for estimating crop areas through sub-pixel classification of medium to low resolution images. Before a network can be used for classification and estimation, however, it has to be trained. The collection of the reference area fractions needed to train an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOT-VGT and MODIS were used as the network inputs. The results showed that accurate regional crop area estimation (R2 > 80%) is possible using only 1% of the entire area for network training, provided that the training samples used are representative for the land use variability present in the study area. Limiting the training samples to a specific subset of the population, either geographically or thematically, significantly decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data from one season to estimate area fractions in another season is not to be recommended. The interannual variability observed in the endmembers’ spectral signatures underlines the importance of using up-to-date training samples. It can thus be concluded that the representativeness of the training samples, both regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored. 相似文献
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Odd Kolbjørnsen Marita Stien Heidi Kjønsberg Bjørn Fjellvoll Petter Abrahamsen 《Mathematical Geosciences》2014,46(2):205-225
A multigrid Markov mesh model for geological facies is formulated by defining a hierarchy of nested grids and defining a Markov mesh model for each of these grids. The facies probabilities in the Markov mesh models are formulated as generalized linear models that combine functions of the grid values in a sequential neighborhood. The parameters in the generalized linear model for each grid are estimated from the training image. During simulation, the coarse patterns are first laid out, and by simulating increasingly finer grids we are able to recreate patterns at different scales. The method is applied to several tests cases and results are compared to the training image and the results of a commercially available snesim algorithm. In each test case, simulation results are compared qualitatively by visual inspection, and quantitatively by using volume fractions, and an upscaled permeability tensor. When compared to the training image, the method produces results that only have a few percent deviation from the values of the training image. When compared with the snesim algorithm the results in general have the same quality. The largest computational cost in the multigrid Markov mesh is the estimation of model parameters from the training image. This is of comparable CPU time to that of creating one snesim realization. The simulation of one realization is typically ten times faster than the estimation. 相似文献
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The spatial continuity of facies is one of the key factors controlling flow in reservoir models. Traditional pixel-based methods
such as truncated Gaussian random fields and indicator simulation are based on only two-point statistics, which is insufficient
to capture complex facies structures. Current methods for multi-point statistics either lack a consistent statistical model
specification or are too computer intensive to be applicable. We propose a Markov mesh model based on generalized linear models
for geological facies modeling. The approach defines a consistent statistical model that is facilitated by efficient estimation
of model parameters and generation of realizations. Our presentation includes a formulation of the general framework, model
specifications in two and three dimensions, and details on how the parameters can be estimated from a training image. We illustrate
the method using multiple training images, including binary and trinary images and simulations in two and three dimensions.
We also do a thorough comparison to the snesim approach. We find that the current model formulation is applicable for multiple training images and compares favorably to
the snesim approach in our test examples. The method is highly memory efficient. 相似文献
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M. Shimoni D. Borghys R. Heremans C. Perneel M. Acheroy 《International Journal of Applied Earth Observation and Geoinformation》2009
The main research goal of this study is to investigate the complementarity and fusion of different frequencies (L- and P-band), polarimetric SAR (PolSAR) and polarimetric interferometric (PolInSAR) data for land cover classification. A large feature set was derived from each of these four modalities and a two-level fusion method was developed: Logistic regression (LR) as ‘feature-level fusion’ and the neural-network (NN) method for higher level fusion. For comparison, a support vector machine (SVM) was also applied. NN and SVM were applied on various combinations of the feature sets. 相似文献
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