Fully constrained linear spectral unmixing based global shadow compensation for high resolution satellite imagery of urban areas |
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| Institution: | 1. Department of Geography, University of Toronto, 100 St. George Street, Toronto ON M5S 3G3, Canada;2. Department of Geography, University of Toronto Mississauga, 3359 Mississauga Rd North, Mississauga ON L5L 1C6, Canada;3. Faculty of Forestry, University of Toronto, 33 Willcocks Street, Toronto ON M5S 3B3, Canada;1. Department of Surgery, Howard University College of Medicine, Towers Building, Suite 4100B, 2041 Georgia Avenue, NW, Washington, DC 20060, USA;2. Howard University College of Medicine, Washington, DC, USA;1. National Key Laboratory of Science and Technology on Tunable Laser, Institute of Opto-Electronic of Harbin Institute of Technology, Harbin 150080, China;2. College of Applied Technology, Qiqihar University, Qiqihar 161006, China |
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| Abstract: | Shadows commonly exist in high resolution satellite imagery, particularly in urban areas, which is a combined effect of low sun elevation, off-nadir viewing angle, and high-rise buildings. The presence of shadows can negatively affect image processing, including land cover classification, mapping, and object recognition due to the reduction or even total loss of spectral information in shadows. The compensation of spectral information in shadows is thus one of the most important preprocessing steps for the interpretation and exploitation of high resolution satellite imagery in urban areas. In this study, we propose a new approach for global shadow compensation through the utilization of fully constrained linear spectral unmixing. The basic assumption of the proposed method is that the construction of the spectral scatter plot in shadows is analogues to that in non-shadow areas within a two-dimension spectral mixing space. In order to ensure the continuity of land covers, a smooth operator is further used to refine the restored shadow pixels on the edge of non-shadow and shadow areas. The proposed method is validated using the WorldView-2 multispectral imagery collected from downtown Toronto, Ontario, Canada. In comparison with the existing linear-correlation correction method, the proposed method produced the compensated shadows with higher quality. |
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| Keywords: | Global shadow compensation Object-based shadow detection Linear spectral unmixing Spectral mixing space Spectral scatter plot WorldView-2 |
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