TY - GEN
T1 - Trading spatial resolution for improved accuracy in remote sensing imagery
T2 - 2017 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2017
AU - Malof, Jordan M.
AU - Chelikani, Sravya
AU - Collins, Leslie M.
AU - Bradbury, Kyle
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - We consider the problem of detecting objects (such as trees, rooftops, roads, or cars) in remote sensing data including, for example, color or hyperspectral imagery. Many detection algorithms applied to this problem operate by assigning a decision statistic to all, or a subset, of spatial locations in the imagery for classification purposes. In this work we investigate a recently proposed method, called Local Averaging for Improved Predictions (LAIP), which can be used for trading off the classification accuracy of detector decision statistics with their spatial precision. We explore the behaviors of LAIP on controlled synthetic data, as we vary several experimental conditions: (a) the difficulty of the detection problem, (b) the spatial area over which LAIP is applied, and (c) how it behaves when the estimated ROC curve of the detector becomes increasingly inaccurate. These results provide basic insights about the conditions under which LAIP is effective.
AB - We consider the problem of detecting objects (such as trees, rooftops, roads, or cars) in remote sensing data including, for example, color or hyperspectral imagery. Many detection algorithms applied to this problem operate by assigning a decision statistic to all, or a subset, of spatial locations in the imagery for classification purposes. In this work we investigate a recently proposed method, called Local Averaging for Improved Predictions (LAIP), which can be used for trading off the classification accuracy of detector decision statistics with their spatial precision. We explore the behaviors of LAIP on controlled synthetic data, as we vary several experimental conditions: (a) the difficulty of the detection problem, (b) the spatial area over which LAIP is applied, and (c) how it behaves when the estimated ROC curve of the detector becomes increasingly inaccurate. These results provide basic insights about the conditions under which LAIP is effective.
KW - image recognition
KW - object detection
KW - photovoltaic
KW - satellite imagery
KW - solar energy
UR - http://www.scopus.com/inward/record.url?scp=85057555547&partnerID=8YFLogxK
U2 - 10.1109/AIPR.2017.8457961
DO - 10.1109/AIPR.2017.8457961
M3 - Conference contribution
AN - SCOPUS:85057555547
T3 - Proceedings - Applied Imagery Pattern Recognition Workshop
BT - 2017 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 October 2017 through 12 October 2017
ER -