TY - GEN
T1 - Online change detection
T2 - Monitoring land cover from remotely sensed data
AU - Fang, Yi
AU - Ganguly, Auroop R.
AU - Singh, Nagendra
AU - Vijayaraj, Veeraraghavan
AU - Feierabend, Neal
AU - Potere, David T.
PY - 2006
Y1 - 2006
N2 - We present a fast and statistically principled approach for land cover change detection. The approach is illustrated with a geographic application that involves analyzing remotely sensed data to detect changes in the normalized difference vegetation index (NDVI) in near real time. We use the Wal-Mart land cover change data set as a nontraditional way to monitor and validate known cases of NDVI change. A reference distribution has been justified to fit the available data. An adaptive metric based on the exponentially weighted moving average (EWMA) of normal scores derived from p-values is tracked for new or streaming data, leading to alarms for large or sustained changes. A heuristic algorithm based on the property of the metric is proposed for change point detection. The proposed framework performed well on the validation dataset.
AB - We present a fast and statistically principled approach for land cover change detection. The approach is illustrated with a geographic application that involves analyzing remotely sensed data to detect changes in the normalized difference vegetation index (NDVI) in near real time. We use the Wal-Mart land cover change data set as a nontraditional way to monitor and validate known cases of NDVI change. A reference distribution has been justified to fit the available data. An adaptive metric based on the exponentially weighted moving average (EWMA) of normal scores derived from p-values is tracked for new or streaming data, leading to alarms for large or sustained changes. A heuristic algorithm based on the property of the metric is proposed for change point detection. The proposed framework performed well on the validation dataset.
UR - http://www.scopus.com/inward/record.url?scp=62449336922&partnerID=8YFLogxK
U2 - 10.1109/icdmw.2006.125
DO - 10.1109/icdmw.2006.125
M3 - Conference contribution
AN - SCOPUS:62449336922
SN - 0769527027
SN - 9780769527024
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 626
EP - 631
BT - Proceedings - ICDM Workshops 2006 - 6th IEEE International Conference on Data Mining - Workshops
PB - Institute of Electrical and Electronics Engineers Inc.
ER -