Object-based change detection method for high-resolution remote sensing image combining shadow compensation and multi-scale fusion |
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Authors: | Chao WANG Xuehong ZHANG Aiye SHI Dan LI Yi SHEN |
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Affiliation: | 1. School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;2. School of Geography and Remote Sensing,Nanjing University of Information Science and Technology,Nanjing 210044,China;3. College of Computer and Information Engineering,Hohai University,Nanjing 211100,China;4. Key Laboratory of Intelligent Industrial Control Technology of Jiangsu Province,Xuzhou Institute of Technology,Xuzhou 221000,China |
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Abstract: | As an interpreting symbol of remote sensing images,shadow,however,brings about “pseudo changes”,which is one of the main sources leading to error detection in high-resolution remote sensing image change detection.For this issue,an object-based high-resolution remote sensing image change detection method was proposed combining with shadow compensation and multi-scale fusion.In the object orientation detection framework,the shadows in the remote sensing images were extracted.Then multi-scale change detection was conducted with shadow compensation.In the process,an objective function was constructed of mutual scale information minimization to realize the adaptive extraction of scale parameters.Based on this,combined with the shadow compensation factor,a multi-scale decision-level fusion strategy built on D-S theory of evidence was designed,and the levels of change intensity were further divided.The experiments show that the method is effective in solving the error detection problem caused by shadow,significantly improving the precision of change detection. |
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Keywords: | high resolution remote sensing image change detection shadow compensation |
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