TY - GEN
T1 - A shadow detection method for remote sensing images using VHR hyperspectral and LIDAR data
AU - Tolt, G.
AU - Shimoni, M.
AU - Ahlberg, J.
PY - 2011
Y1 - 2011
N2 - In this paper, a shadow detection method combining hyperspectral and LIDAR data analysis is presented. First, a rough shadow image is computed through line-of-sight analysis on a Digital Surface Model (DSM), using an estimate of the position of the sun at the time of image acquisition. Then, large shadow and non-shadow areas in that image are detected and used for training a supervised classifier (a Support Vector Machine, SVM) that classifies every pixel in the hyperspectral image as shadow or non-shadow. Finally, small holes are filled through image morphological analysis. The method was tested on data including a 24 band hyperspectral image in the VIS/NIR domain (50 cm spatial resolution) and a DSM of 25 cm resolution. The results were in good accordance with visual interpretation. As the line-of-sight analysis step is only used for training, geometric mismatches (about 2 m) between LIDAR and hyperspectral data did not affect the results significantly, nor did uncertainties regarding the position of the sun.
AB - In this paper, a shadow detection method combining hyperspectral and LIDAR data analysis is presented. First, a rough shadow image is computed through line-of-sight analysis on a Digital Surface Model (DSM), using an estimate of the position of the sun at the time of image acquisition. Then, large shadow and non-shadow areas in that image are detected and used for training a supervised classifier (a Support Vector Machine, SVM) that classifies every pixel in the hyperspectral image as shadow or non-shadow. Finally, small holes are filled through image morphological analysis. The method was tested on data including a 24 band hyperspectral image in the VIS/NIR domain (50 cm spatial resolution) and a DSM of 25 cm resolution. The results were in good accordance with visual interpretation. As the line-of-sight analysis step is only used for training, geometric mismatches (about 2 m) between LIDAR and hyperspectral data did not affect the results significantly, nor did uncertainties regarding the position of the sun.
KW - DSM
KW - LIDAR
KW - SVM
KW - Shadow detection
KW - hyperspectral
KW - supervised classification
UR - http://www.scopus.com/inward/record.url?scp=80955141861&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2011.6050213
DO - 10.1109/IGARSS.2011.6050213
M3 - Conference contribution
AN - SCOPUS:80955141861
SN - 9781457710056
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4423
EP - 4426
BT - 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings
T2 - 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011
Y2 - 24 July 2011 through 29 July 2011
ER -