TY - GEN
T1 - Monitoring a fuzzy object
T2 - 2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Multi-Temp 2011
AU - Bijker, Wietske
AU - Hamm, Nicholas A.S.
AU - Ijumulana, Julian
AU - Wole, Misganaw Kebede
PY - 2011
Y1 - 2011
N2 - This study shows two approaches to including uncertainty of the mapped feature in multi-temporal analysis. This is demonstrated on a series of Landsat ETM+ images of Lake Naivasha, Kenya, with fuzzy boundaries resulting from marshes and floating vegetation. The first approach creates image segments, merges these to image objects through object-based classification and calculates the uncertainty for the lake image object in each image. The second approach uses a soft classifier to calculate memberships for lake and land. The lake area is calculated for 6 different thresholds on membership for each "lake" membership image, reflecting thresholds on the uncertainty in the estimate. The method based on image objects and attached uncertainty provided a quick overview and highlights uncertainty related to image quality and time of observation. The method based on thresholding of membership gave more spatial detail, highlighting the effect of fuzzy boundaries.
AB - This study shows two approaches to including uncertainty of the mapped feature in multi-temporal analysis. This is demonstrated on a series of Landsat ETM+ images of Lake Naivasha, Kenya, with fuzzy boundaries resulting from marshes and floating vegetation. The first approach creates image segments, merges these to image objects through object-based classification and calculates the uncertainty for the lake image object in each image. The second approach uses a soft classifier to calculate memberships for lake and land. The lake area is calculated for 6 different thresholds on membership for each "lake" membership image, reflecting thresholds on the uncertainty in the estimate. The method based on image objects and attached uncertainty provided a quick overview and highlights uncertainty related to image quality and time of observation. The method based on thresholding of membership gave more spatial detail, highlighting the effect of fuzzy boundaries.
KW - Objects
KW - change detection
KW - uncertainty
UR - http://www.scopus.com/inward/record.url?scp=80053100706&partnerID=8YFLogxK
U2 - 10.1109/Multi-Temp.2011.6005071
DO - 10.1109/Multi-Temp.2011.6005071
M3 - Conference contribution
AN - SCOPUS:80053100706
SN - 9781457712036
T3 - 2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Multi-Temp 2011 - Proceedings
SP - 153
EP - 156
BT - 2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Multi-Temp 2011 - Proceedings
Y2 - 12 July 2011 through 14 July 2011
ER -