Acta Optica Sinica, Volume. 38, Issue 6, 0628002(2018)
River Detection in Remote Sensing Images Based on Multi-Feature Fusion and Soft Voting
River is a very typical and important geographical target in remote sensing images. The automatic detection of rivers is of great significance in water resources investigation and water conservancy planning. In this paper, a river target detection algorithm based on multi-feature fusion and soft voting method is proposed. The algorithm firstly divides the images into cells and then extracts the local entropy, texture, spectrum, and color feature of the cells. Random forest is used to train and classify. To optimize the rough detection result of machine learning, the morphology operation and the multi-criteria voting method is introduced. For optimized rough detection result, the level set active contour is used to approach the river shoreline. Experiments show that the proposed algorithm has a good detection effect, and the detection accuracy rate of test set reaches 97.44%. In addition, the river can be effectively detected in the complex background.
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Qingchun Zhang, Guofeng Tong, Yong Li, Liwei Gao, Huairong Chen. River Detection in Remote Sensing Images Based on Multi-Feature Fusion and Soft Voting[J]. Acta Optica Sinica, 2018, 38(6): 0628002
Category: Remote Sensing and Sensors
Received: Dec. 3, 2017
Accepted: --
Published Online: Jul. 9, 2018
The Author Email: Tong Guofeng (tongguofeng@ise.neu.edu.cn)