Accurate segmentation of remote sensing cages based on U-Net and voting mechanism

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 光电信息技术研究室

	Accurate segmentation of remote sensing cages based on U-Net and voting mechanism
	Yu C(余创)1; Liu YP(刘云鹏)1; Hu ZH(胡祝华)2; Xia X(夏鑫)1
	2021
会议日期	October 28-31, 2021
会议地点	Shanghai, China
关键词	Remote sensing image segmentation U-Net Voting mechanism Precision agriculture Aquaculture
页码	1-6
英文摘要	In aquaculture, the normal growth of fish is closely related to the density of aquaculture. Therefore, it is of great significance to use remote sensing images to accurately segment the cages in a specific sea area at a macro level. This research proposes an accurate segmentation scheme for remote sensing cages based on U-Net and voting mechanism. Firstly, a remote sensing cage segmentation (RSCS) data set is produced, which includes fifty-three high-resolution cage images with inconsistent resolution. Secondly, by using random cropping and data enhancement operations on the training samples, three training sets with image block sizes of 256×256 pixels, 512×512 pixels, and 1024×1024 pixels are created. And through the introduction of U-Net network, three training sets of different sample sizes are trained separately and three trained models are generated. Then, after reasonably filling the test image, a window sliding overlap cropping method is adopted. The high-resolution remote sensing cage test images are sequentially cut into the image blocks for segmentation, and the segmented image blocks are spliced and combined into the binary segmentation image by the mean method. Finally, for each image, the three binary segmentation images generated by different trained models are used to vote for each pixel. The experimental results show that by testing three remote sensing images of Li'an Port, Xincun Port and Potou Port, the Mean Intersection o© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
源文献作者	Chinese Society for Optical Engineering
产权排序	1
会议录	Seventh Asia Pacific Conference on Optics Manufacture, APCOM 2021
会议录出版者	SPIE
会议录出版地	Bellingham, USA
语种	英语
ISSN号	0277-786X
ISBN号	978-1-5106-5208-8
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/30555]
专题	沈阳自动化研究所_光电信息技术研究室
通讯作者	Hu ZH(胡祝华); Xia X(夏鑫)
作者单位	1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2.School of Information and Communication Engineering, Hainan University, Haikou 570228, China
推荐引用方式 GB/T 7714	Yu C,Liu YP,Hu ZH,et al. Accurate segmentation of remote sensing cages based on U-Net and voting mechanism[C]. 见:. Shanghai, China. October 28-31, 2021.