@proceedings {Miyazaki2011a,
	title = {High-Resolution Urban Area Map for 3372 Cities of the World},
	year = {2011},
	pages = {PS{\textendash}3},
	address = {Taipei},
	abstract = {We developed an automated method of image selection and urban area mapping for developing high-resolution global urban area maps. The method was successfully implemented and applied to 3372 cities of more than 0.1 million people of the world. As the result, the algorithm of image selection determined 11802 scenes of ASTER/VNIR satellite images and yielded good combinations for more than 60\% of the cities. For the merged satellite images with the determined combinations, we applied the automated method of urban area mapping in high resolution. The method was consist of semi-supervised classifications by a machine learning method, called Learning with Local and Global Consistency (LLGC), and integrating the LLGC-derived maps and existing maps by logistic regression. As the result, we acquired urban area maps of 15-m resolution originated from ASTER/VNIR images, which is much finer than 500-m resolution of existing urban area maps. The method had still much space to be improved, especially in avoiding cloud contaminations. However, the method would contribute to complete high-resolution urban area maps of the world and realizing Global Earth Observation Systems of System (GEOSS).},
	keywords = {ASTER, automated image selection, automated urban area mapping, global urban area map, Learning with Local and Global Consistency, logistic regression, multi-source classification},
	author = {Miyazaki, Hiroyuki and Itabashi, Koichiro and Shao, Xiaowei and Iwao, Koki and Shibasaki, Ryosuke}
}
@proceedings {84,
	title = {A Method for Constructing Urban Extent Map from ALOS/PALSAR Satellite Data},
	year = {2011},
	month = {2011},
	pages = {TS9-1},
	address = {Taipei},
	abstract = {Currently, global urban extent map of high accuracy and high resolution have been constructed mainly using optical sensor including ASTER/VNIR. However, there are some regions where urban areas are not correctly detected due to cloud cover and similar reflectance among land cover classes. To solve the problems, we used microwave sensor images of ALOS/PALSAR, which has an advantage in enabling observation in all weather conditions. This study aims at examining the possibility of using ALOS/PALSAR images as an alternative data resource for constructing urban extent map. Firstly, to determine useful ALOS/PALSAR observation mode, we examined how often ALOS/PALSAR images are taken in the regions for which an existing method using ASTER/VNIR images could not detect urban area correctly. Secondly, we collected ALOS/PALSAR satellite images, and examined effect of local-incident-angle-corrected images of ALOS/PALSAR taken by Fine Resolution Mode which can reduce distortion of pixel values due to local incident angle. We also performed unsupervised classifications on the ALOS/PALSAR and local-incident-angle-corrected images. Finally, we discussed ground truth datasets for image classification.},
	keywords = {ALOS/PALSAR, classification, microwave sensor, urban extent map},
	url = {http://a-a-r-s.org/acrs/index.php/acrs/acrs-overview/proceedings-1?view=publication\&task=show\&id=1005},
	author = {Itabashi, Koichiro and Miyazaki, Hiroyuki and Iwao, Koki and Nakamura, Kazuki and MATSUOKA, Masashi and Shibasaki, Ryosuke}
}
@proceedings {37,
	title = {A method for detecting and mapping Urban Area by ALOS/PALSAR data},
	year = {2010},
	month = {2010},
	address = {Hanoi, Vietnam},
	abstract = {Currently, global urban extent map of high accuracy and resolution have been constructed mainly using optical sensor including ASTER/VNIR. However, there are some regions where urban area is not detected because of cloud cover and similar reflectance among land cover classes. In the present work, by using ALOS/PALSAR, a microwave sensor, we proposed a method for detecting urban area which cannot be detected by ASTER/VNIR optical sensor and developing urban extent map in high accuracy and resolution. We mainly used satellite images taken by Fine Resolution Mode of ALOS/PALSAR. Local-incident-angle corrected images by Fine Resolution Mode were used for this method. The proposed method consists of sampling pixel values and ground truth data at urban and non-urban area from ALOS/PALSAR images; constructing classifier based on the pixel values and ground truth data; and classifying pixels into urban or non-urban area. We compared the results with urban extent map derived from ASTER/VNIR optical sensor images, and evaluated the possibility of using ALOS/PALSAR data for developing urban extent map. In addition, we examined accuracy improvement of detecting urban area using both ASTER/VNIR and ALOS/PALSAR images. The proposed method could classify regions which were misclassified by ASTER/VNIR optical sensor images, and develop urban extent map in high accuracy and resolution.},
	keywords = {ALOS PALSAR urban extent map high resolution},
	author = {Itabashi, Koichiro and Miyazaki, Hiroyuki and Iwao, Koki and Nakamura, Kazuki and Shibasaki, Ryosuke}
}
