A shadow detection method for remote sensing images using VHR hyperspectral and LIDAR data
2011 (English)In: 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE , 2011, 4423-4426 p.Conference paper (Refereed)
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 nonshadow. 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.
Place, publisher, year, edition, pages
IEEE , 2011. 4423-4426 p.
, IEEE International Geoscience and Remote Sensing Symposium proceedings, ISSN 2153-6996
Shadow detection, hyperspectral, LIDAR, DSM, supervised classification, SVM
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-120512DOI: 10.1109/IGARSS.2011.6050213ISBN: 978-1-4577-1003-2OAI: oai:DiVA.org:liu-120512DiVA: diva2:845468
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, BC, Canada, 24-29 July 2011