Journal ArticleDOI
Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data
TLDR
Experimental results show that two different approaches have unique advantages and disadvantages in this classification application of multisource remote sensing and geographic data.Abstract:
Neural network learning procedures and statistical classificaiton methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that two different approaches have unique advantages and disadvantages in this classification application.read more
Citations
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Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
M.W. Gardner,Stephen Dorling +1 more
TL;DR: This paper presents a general introduction and discussion of recent applications of the multilayer perceptron, one type of artificial neural network, in the atmospheric sciences.
Journal ArticleDOI
Digital change detection methods in ecosystem monitoring: a review
TL;DR: This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today.
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Deep learning and process understanding for data-driven Earth system science
Markus Reichstein,Gustau Camps-Valls,Bjorn Stevens,Martin Jung,Joachim Denzler,Nuno Carvalhais,Nuno Carvalhais,Prabhat +7 more
TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.
Journal ArticleDOI
Data fusion
Jens Bleiholder,Felix Naumann +1 more
TL;DR: This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data Fusion.
Journal ArticleDOI
Random Forests for land cover classification
TL;DR: The Random Forest classifier uses bagging, or bootstrap aggregating, to form an ensemble of classification and regression tree (CART)-like classifiers, which is computationally much lighter than methods based on boosting and somewhat lighter than simple bagging.
References
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Journal Article
Techniques for combining Landsat and ancillary data for digital classification improvement
Journal ArticleDOI
Probabilistic and Evidential Approaches for Multisource Data Analysis
TL;DR: Two methods for combining the information contents from multiple sources of remote-sensing image data and spatial data in general are described, including a probabilistic scheme that employs a global membership function that is derived from all available data sources and an evidential calculus based upon Dempster's orthogonal sum combination rule.