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#Cartographica user guide series#
Using two ArcGIS 10.0 tools in a three-step approach and a series of site visits, the continuity and connectivity analysis identified not only physical barriers but also legal aspects and socio-economic behaviour that are influencing ecological connectivity and playing a major role to conserve wildlife population. BioREGIO applied a multi-disciplinary approach (physical, legal and socio-economic) in order to identify the most influencing barriers regarding connectivity throughout the Carpathians. If not considering the requirements of ecological network, this run-to-development will enhance landscape fragmentation, limit dispersal and genetic exchange of wildlife species. The Carpathian countries are expecting a massive pressure to modernize and extend their road infrastructures. The project is built on the conservation, restoration and valorisation of the Carpathians ecological continuum to enable large herbivores and carnivores to live in coexistence with modern society. BioREGIO Carpathians run for three years (2011–2013) and is a flagship project for the Carpathian Convention (article four dealing with landscape and biological diversity), its Biodiversity Protocol and the Biodiversity Working Group. Experimental results on Quickbird, IKONOS and SPOT-5 multi-spectral images show that the proposed method out performs the compared methods in the accuracy of CD.BioREGIO Carpathians is a transnational cooperation project, co-financed under the second call of the EU South East Europe Transnational Cooperation Programme, priority area “Protection and Improvement of the Environment”. On the basis of the initial pixel-level CD result, the changed and unchanged samples are automatically selected and used to build the classifier model in order to get the final object-level CD result. Afterwards, the spectral features and Gabor features of each super-pixelareextracted and used as feature datasets for the training of RF model. Then the optimal image segmentation result is obtained from the evaluation index of the optimal super-pixel number. Firstly, the entropy rate segmentation algorithm is used to segment the image for the purpose of measuring the homogeneity of super-pixels.
#Cartographica user guide full#
This paper presents a novel RF OBIA method for high resolution remote sensing image CD that makes full use of the advantages of RF and OBIA. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method.
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Their aim has been developing more intelligent interpretation analysis methods in the future. Studies based on object-based image analysis (OBIA) representing the paradigm shift in remote sensing image change detection (CD) have achieved remarkable progress in the last decade. Firstly, the entropy rate segmentation algorithm is used to segment the image for the purpose of measuring the homogeneity of.
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In Cehui Xuebao/Acta Geodaetica et Cartographica Sinica 46 (11).
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Change Detection Method for High Resolution Remote Sensing Images Using Random Forestįeng, Wenqing Sui, Haigang Tu, Jihui Sun, Kaimin and Huang, Weiming LU