%0 Conference Paper %1 alhameedawi2013objectoriented %A Al-Hameedawi, A. %A Buchroithner, Manfred F. %A Prechtel, Nikolas %B Proceedings of the 26th International Cartographic Conference, Dresden, August 25-30 %D 2013 %E Buchroithner, Manfred F. %K imported myown %P 508 %T Object-oriented and Decision tree classifications for LULC using Cosmo-SkyMed, QuickBird and LandSat 7 satellite data: An Example of Erbil/Iraq %X Classification of SAR Data and optical remote sensing data is utilised in this paper to make use of complementary mapping potential of SAR data and optical image data. The landuse/landcover LU/LC mapping of the study area in Iraq has been performed using multi-sensor (SAR, optical) data in a combination with different classification approaches. The results have been evaluated on the basis of an accuracy assessment with a reference to substantial ground truth. The applied techniques include object-oriented classification and decision-tree classification. A good accuracy level has been reached. The results provide help and guidance for decision makers in an area with limited available geo-information. The accuracy assessment of classification is not only dependent on which classification techniques used but also on image Pre-Processing techniques used. Therefore, a set digital image processing is made for SAR Data and Optical Remote Sensing Data. These preprocessing techniques were very important to increase features extracted from images, decrease error in interpretation of images and also in order to effectively identify the spatial distribution characteristics of landcover/landuse classes for the city of Erbil. The Analysing and mapping the trend of LULC dynamics within the study area provide a basis for strategic planning, management and conservation decision making of Erbil City.

@inproceedings{alhameedawi2013objectoriented, abstract = {Classification of SAR Data and optical remote sensing data is utilised in this paper to make use of complementary mapping potential of SAR data and optical image data. The landuse/landcover LU/LC mapping of the study area in Iraq has been performed using multi-sensor (SAR, optical) data in a combination with different classification approaches. The results have been evaluated on the basis of an accuracy assessment with a reference to substantial ground truth. The applied techniques include object-oriented classification and decision-tree classification. A good accuracy level has been reached. The results provide help and guidance for decision makers in an area with limited available geo-information. The accuracy assessment of classification is not only dependent on which classification techniques used but also on image Pre-Processing techniques used. Therefore, a set digital image processing is made for SAR Data and Optical Remote Sensing Data. These preprocessing techniques were very important to increase features extracted from images, decrease error in interpretation of images and also in order to effectively identify the spatial distribution characteristics of landcover/landuse classes for the city of Erbil. The Analysing and mapping the trend of LULC dynamics within the study area provide a basis for strategic planning, management and conservation decision making of Erbil City.}, added-at = {2017-04-25T16:08:11.000+0200}, author = {Al-Hameedawi, A. and Buchroithner, Manfred F. and Prechtel, Nikolas}, biburl = {https://www.bibsonomy.org/bibtex/2c19e005727b126ceb9bb2ea179b9d0e3/nprechtel}, booktitle = {Proceedings of the 26th International Cartographic Conference, Dresden, August 25-30}, editor = {Buchroithner, Manfred F.}, interhash = {c9444b9b4dbd928447b56529a5ffdc73}, intrahash = {c19e005727b126ceb9bb2ea179b9d0e3}, keywords = {imported myown}, month = aug, organization = {ICA}, pages = 508, timestamp = {2017-07-28T16:53:17.000+0200}, title = {Object-oriented and Decision tree classifications for LULC using Cosmo-SkyMed, QuickBird and LandSat 7 satellite data: An Example of Erbil/Iraq}, year = 2013 }