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Remote Sensing Image Change Detection Using Superpixel Cosegmentation

by 1,*, 1 and 2
1
School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
2
Beijing Institute of Surveying and Mapping, Beijing 100038, China
*
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Information 2021, 12(2), 94; https://doi.org/10.3390/info12020094
Received: 5 January 2021 / Revised: 15 February 2021 / Accepted: 19 February 2021 / Published: 23 February 2021
(This article belongs to the Special Issue Remote Sensing and Spatial Data Science)
The application of cosegmentation in remote sensing image change detection can effectively overcome the salt and pepper phenomenon and generate multitemporal changing objects with consistent boundaries. Cosegmentation considers the image information, such as spectrum and texture, and mines the spatial neighborhood information between pixels. However, each pixel in the minimum cut/maximum flow algorithm for cosegmentation change detection is regarded as a node in the network flow diagram. This condition leads to a direct correlation between computation times and the number of nodes and edges in the diagram. It requires a large amount of computation and consumes excessive time for change detection of large areas. A superpixel segmentation method is combined into cosegmentation to solve this shortcoming. Simple linear iterative clustering is adopted to group pixels by using the similarity of features among pixels. Two-phase superpixels are overlaid to form the multitemporal consistent superpixel segmentation. Each superpixel block is regarded as a node for cosegmentation change detection, so as to reduce the number of nodes in the network flow diagram constructed by minimum cut/maximum flow. In this study, the Chinese GF-1 and Landsat satellite images are taken as examples, the overall accuracy of the change detection results is above 0.80, and the calculation time is only one-fifth of the original. View Full-Text
Keywords: change detection; cosegmentation; superpixel segmentation; minimum cut/maximum flow change detection; cosegmentation; superpixel segmentation; minimum cut/maximum flow
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MDPI and ACS Style

Zhu, L.; Zhang, J.; Sun, Y. Remote Sensing Image Change Detection Using Superpixel Cosegmentation. Information 2021, 12, 94. https://doi.org/10.3390/info12020094

AMA Style

Zhu L, Zhang J, Sun Y. Remote Sensing Image Change Detection Using Superpixel Cosegmentation. Information. 2021; 12(2):94. https://doi.org/10.3390/info12020094

Chicago/Turabian Style

Zhu, Ling; Zhang, Jingyi; Sun, Yang. 2021. "Remote Sensing Image Change Detection Using Superpixel Cosegmentation" Information 12, no. 2: 94. https://doi.org/10.3390/info12020094

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