TY - GEN
T1 - Trading spatial resolution for improved accuracy when using detection algorithms on remote sensing imagery
AU - Qian, Shengxin
AU - Chelikani, Sravya
AU - Wang, Patrick
AU - Collins, Leslie M.
AU - Bradbury, Kyle
AU - Malof, Jordan M.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - In this work, we consider the problem of detecting target objects in remote sensing imagery; such as detecting rooftops, trees, or cars in color/hyperspectral imagery. Many detection algorithms for this problem work by assigning a decision statistic (or 'confidence') to all, or a subset, of spatial locations in the data. A threshold is then applied to the statistics to identify detections. The detection theory underpinning this general approach assumes that a yes/no decision must be made, individually, for each location. In some applications, however, the precise location of the detected objects may be less important than knowing how many total objects there are. In this work we propose two methods that can permit a generic detection algorithm to gradually lower the spatial certainty, or resolution, of its detections in order to improve the accuracy of the overall number of detected objects. We validate the proposed methods on a controlled synthetic dataset as well as a real dataset from previously published work on solar photovoltaic array detection in color aerial imagery.
AB - In this work, we consider the problem of detecting target objects in remote sensing imagery; such as detecting rooftops, trees, or cars in color/hyperspectral imagery. Many detection algorithms for this problem work by assigning a decision statistic (or 'confidence') to all, or a subset, of spatial locations in the data. A threshold is then applied to the statistics to identify detections. The detection theory underpinning this general approach assumes that a yes/no decision must be made, individually, for each location. In some applications, however, the precise location of the detected objects may be less important than knowing how many total objects there are. In this work we propose two methods that can permit a generic detection algorithm to gradually lower the spatial certainty, or resolution, of its detections in order to improve the accuracy of the overall number of detected objects. We validate the proposed methods on a controlled synthetic dataset as well as a real dataset from previously published work on solar photovoltaic array detection in color aerial imagery.
KW - Detection
KW - Estimation
KW - Object recognition
KW - Remote sensing
KW - Satellite imagery
KW - Solar panels
UR - http://www.scopus.com/inward/record.url?scp=85041835571&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2017.8127806
DO - 10.1109/IGARSS.2017.8127806
M3 - Conference contribution
AN - SCOPUS:85041835571
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3716
EP - 3719
BT - 2017 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Y2 - 23 July 2017 through 28 July 2017
ER -