8 May 2018 Calculation of the optimal segmentation scale in object-based multiresolution segmentation based on the scene complexity of high-resolution remote sensing images
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Abstract
The quality of multiresolution segmentation directly influences the accuracy of high-resolution remote sensing image classification using object-oriented analysis technology. However, a perfect segmentation scale optimization method has not yet been developed. Using the fact that the optimal segmentation scale of high-resolution remote sensing images is closely related to the complexity of the objects on the image, we propose an approach for calculating the optimal segmentation scale based on the scene complexity of an image. First, we calculate the scene complexity of high-resolution remote sensing images using Watson’s vision model. Then, we analyze the relationship between the image scene complexity and the optimal segmentation scale based on the model calculation. Optimal segmentation scales are found to be related to the scene complexity of high-resolution remote sensing images by an exponential function, allowing direct calculation of the optimal segmentation scale based on the fitted formulas and the image scene complexity. Finally, we propose a multilevel segmentation strategy to increase the object targeting in the optimal segmentation scale. The optimal segmentation scale calculation method proposed here is simple to perform and has a broad range of potential applications.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Tianjing Feng, Tianjing Feng, Hairong Ma, Hairong Ma, Xinwen Cheng, Xinwen Cheng, Hongping Zhang, Hongping Zhang, } "Calculation of the optimal segmentation scale in object-based multiresolution segmentation based on the scene complexity of high-resolution remote sensing images," Journal of Applied Remote Sensing 12(2), 025006 (8 May 2018). https://doi.org/10.1117/1.JRS.12.025006 . Submission: Received: 20 December 2017; Accepted: 18 April 2018
Received: 20 December 2017; Accepted: 18 April 2018; Published: 8 May 2018
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