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Proceedings Paper

Crop identification using UAV image segmentation
Author(s): Xiaohai Shen; Yan Teng; Han Fu; Zhida Wan; Xuewu Zhang
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Paper Abstract

Crop identification is important to precise agriculture, traditional survey is laboring intensive and classic remote sensing image processing methods are highly dependent on expertise, aiming at this problem, a U-Net based deep segmentation network is proposed for crop identification in UAV remote sensing image segmentation. First, we preprocess the UAV remote images of dataset for training. Then, we replace the transposed convolutional layer in U-Net upsampling block with sub pixel convolutional layer, to solve the problem that edge structure information lost and the low segmentation accuracy. The experiment result shows that, compared with original U-Net and other state of art segmentation model, the proposed method can improve the edge smoothness and pixel wise accuracy. The method uses sub pixel convolution layer for upsampling can be applied to other segmentation model, and the way uses segmentation model to identify the crop species can be extended to other remote sensing image processing filed.

Paper Details

Date Published: 31 January 2020
PDF: 5 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 1142720 (31 January 2020); doi: 10.1117/12.2552195
Show Author Affiliations
Xiaohai Shen, Hohai Univ. (China)
Yan Teng, Hohai Univ. (China)
Han Fu, Hohai Univ. (China)
Zhida Wan, Hohai Univ. (China)
Xuewu Zhang, Hohai Univ. (China)

Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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