822 research outputs found
Automatic Adaptive Signature Generalization in R
No full textThe automatic adaptive signature generalization (AASG) algorithm overcomes many of the limitations associated with classification of multitemporal imagery. By locating stable sites between two images and using them to adapt class spectral signatures from a high-quality reference classification to a new image, AASG mitigates the impacts of radiometric and phenological differences between images and ensures that class definitions remain consistent between the two classifications. Here, I provide source code (in the R programming environment), as well as a comprehensive user guide, for the AASG algorithm. See Dannenberg, Hakkenberg and Song (2016) for details of the algorithm.
Dannenberg, MP, CR Hakkenberg, and C Song (2016), Consistent classification of Landsat time series with an improved automatic adaptive signature generalization algorithm, Remote Sensing 8(8): 691
Automatic Adaptive Signature Generalization in R
No full textThe automatic adaptive signature generalization (AASG) algorithm overcomes many of the limitations associated with classification of multitemporal imagery. By locating stable sites between two images and using them to adapt class spectral signatures from a high-quality reference classification to a new image, AASG mitigates the impacts of radiometric and phenological differences between images and ensures that class definitions remain consistent between the two classifications. Here, I provide source code (in the R programming environment), as well as a comprehensive user guide, for the AASG algorithm. See Dannenberg, Hakkenberg and Song (2016) for details of the algorithm.
Dannenberg, MP, CR Hakkenberg, and C Song (2016), Consistent classification of Landsat time series with an improved automatic adaptive signature generalization algorithm, Remote Sensing 8(8): 691
Automatic Adaptive Signature Generalization in R
No full textThe automatic adaptive signature generalization (AASG) algorithm overcomes many of the limitations associated with classification of multitemporal imagery. By locating stable sites between two images and using them to adapt class spectral signatures from a high-quality reference classification to a new image, AASG mitigates the impacts of radiometric and phenological differences between images and ensures that class definitions remain consistent between the two classifications. Here, I provide source code (in the R programming environment), as well as a comprehensive user guide, for the AASG algorithm. See Dannenberg, Hakkenberg and Song (2016) for details of the algorithm.
Dannenberg, MP, CR Hakkenberg, and C Song (2016), Consistent classification of Landsat time series with an improved automatic adaptive signature generalization algorithm, Remote Sensing 8(8): 691
Southern Minnesota Initiative Foundation: Early Childhood Initiative Grant
No full textIncludes bibliographical references
(Supplement zu der im Jahre 1874 erschienenenSammlung) enthaltend die seit 1874 erschienenen Polizei-Vorschriften und Polizei-Gesetze, die hierauf bezüglichen Ausführungs-Anweisungen, ministeriellen Instruktionen und Entscheidungen, sowie die wichtigeren Präjudizien der obersten Gerichtshöfe
No full textDas Baurecht in der Provinz Pommern; eine Sammlung der hierauf bezüglichen Gesetze, Ausführungs-Bestimmungen, Polizei-Verordnungen und Ortsstatuten
No full textSammlung der für den Bezirk der Königlichen Regierung zu Stettin gültigen Polizei-Vorschriften
No full textSammlung der für den Bezirk der Königlichen Regierung zu Stettin gültigen Polizei-Vorschriften; unter Benutzung amtlicher Quellen zusammengestellt
No full textStochastic Income Statement Planning and Emissions Trading
No full textSince the introduction of the European CO2 emissions trading system (EU ETS), the development of CO2 allowance prices is a new risk factor for enterprises taking part in this system. In this paper, we analyze how risk emerging from emissions trading can be considered in the stochastic profit and loss planning of corporations. Therefore we explore which planned figures are affected by emissions trading. Moreover, we show a way to model these positions in a planned profit and loss account accounting for uncertainties and dependencies. Consequently, this model provides a basis for risk assessment and investment decisions in the uncertain environment of CO2 emissions trading.CO2, emissions trading, EU ETS, risk, stochastic business planning
- …
