TY - GEN
T1 - Land use and land cover inference in large areas using multi-temporal optical satellite images
AU - Hashimoto, Shutaro
AU - Tadono, Takeo
AU - Onosato, Masahiko
AU - Hori, Masahiro
PY - 2013
Y1 - 2013
N2 - This paper describes a new land use and land cover (LULC) classification method for classifying multi-temporal high-resolution satellite data in large areas. The classification method uses combined value of both reflectance of a pixel and its observation date as an input data, and calculates its probability distribution among all LULC classes via Bayesian inference based on a generative model estimated by kernel density estimation. This method can be easily applied to multi-temporal data to exploit phenological change information of vegetation, even if available multi-temporal data have a seasonal bias. In this paper, we conducted the classification over the entire land mass of Japan, using the multi-temporal data observed by the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) aboard the ALOS, and we evaluated its accuracy in comparison to conventional methods.
AB - This paper describes a new land use and land cover (LULC) classification method for classifying multi-temporal high-resolution satellite data in large areas. The classification method uses combined value of both reflectance of a pixel and its observation date as an input data, and calculates its probability distribution among all LULC classes via Bayesian inference based on a generative model estimated by kernel density estimation. This method can be easily applied to multi-temporal data to exploit phenological change information of vegetation, even if available multi-temporal data have a seasonal bias. In this paper, we conducted the classification over the entire land mass of Japan, using the multi-temporal data observed by the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) aboard the ALOS, and we evaluated its accuracy in comparison to conventional methods.
KW - GPGPU acceleration
KW - Generative model
KW - Kernel density estimation
KW - Land use and land cover classification
KW - Multi-temporal classification
UR - http://www.scopus.com/inward/record.url?scp=84894259164&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2013.6723541
DO - 10.1109/IGARSS.2013.6723541
M3 - 会議への寄与
AN - SCOPUS:84894259164
SN - 9781479911141
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3333
EP - 3336
BT - 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings
T2 - 2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013
Y2 - 21 July 2013 through 26 July 2013
ER -
