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3. Comparative Analysis of Chinese High-Resolution Satellite Data for Sugarcane Classification Based on U-Net Model

Author

Chen, Chen, Lou, Linjiang, Gao, Xinyuan, Liu, Yu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Wang, Liheng, editor, Wu, Yirong, editor, and Gong, Jianya, editor

Published
2022
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4. Comparative Research on Water Body Extraction Methods Based on SPOT Data

Author

Lou, Linjiang, Chen, Chen, Gao, Xinyuan, Liu, Kun, Li, Minmin, Fu, Yajie, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Wang, Liheng, editor, Wu, Yirong, editor, and Gong, Jianya, editor

Published
2022
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12. Diurnal Variation and Effects of Dilation and Sedation on Intraocular Pressure in Infant Rhesus Monkeys.

Author

Beach, Krista M., Hung, Li-Fang, Lou, Linjiang, and Ostrin, Lisa A.

Subjects
RHESUS monkeys, INTRAOCULAR pressure, INFANTS, INTRACLASS correlation
Abstract

Intraocular pressure (IOP) is an important factor in numerous ocular conditions and research areas, including eye growth and myopia. In infant monkeys, IOP is typically measured under anesthesia. This study aimed to establish a method for awake IOP measurement in infant rhesus monkeys, determine diurnal variation, and assess the effects of dilation and sedation. Awake IOP (iCare TonoVet) was measured every 2 h from 7:30 am to 5:30 pm to assess potential diurnal variations in infant rhesus monkeys (age 3 weeks, n = 11). The following day, and every 2 weeks to age 15 weeks, IOP was measured under three conditions: (1) awake, (2) awake and dilated (tropicamide 0.5%), and (3) sedated (ketamine and acepromazine) and dilated. Intraclass correlation coefficient (ICC) was used to determine intersession repeatability, and repeated measures. ANOVA was used to determine effects of age and condition. At age 3 weeks, mean (±SEM) awake IOP was 15.4 ± 0.6 and 15.2 ± 0.7 mmHg for right and left eyes, respectively (p=.59). The ICC between sessions was 0.63[−0.5 to 0.9], with a mean difference of 2.2 ± 0.3 mmHg. Diurnal IOP from 7:30 am to 5:30 pm showed no significant variation (p=.65). From 3 to 15 weeks of age, there was a significant effect of age (p=.01) and condition (p<.001). Across ages, IOP was 17.8 ± 0.7 mmHg while awake and undilated, 18.4 ± 0.2 mmHg awake and dilated, and 11.0 ± 0.3 mmHg after sedation and dilation. Awake IOP measurement was feasible in young rhesus monkeys. No significant diurnal variations in IOP were observed between 7:30 am and 5:30 pm at age 3 weeks. In awake monkeys, IOP was slightly higher after mydriasis and considerably lower after sedation. Findings show that IOP under ketamine/acepromazine anesthesia is significantly different than awake IOP in young rhesus monkeys. [ABSTRACT FROM AUTHOR]

Published
2023
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16. Sugarcane Planting Area Classification, Extraction and Accuracy Comparison Based on Chinese High-Resolution Remote Sensing Satellite Data: A Case Study of Ningming Sugarcane Demonstration Area, Chongzuo City, Guangxi

Author

Chen Chen, Liu Kun, Li Minmin, Gao Xinyuan, Lou Linjiang, and Han Min

Subjects
Remote sensing satellite, Sowing, Environmental science, High resolution, Extraction (military), Remote sensing
Abstract

Remote sensing techniques are effective in sugarcane extraction and monitoring, but most of the existing research is based on low- and medium-resolution image. Thus, the technical methodology for high-resolution image needs to be improved. Due to the good performances of deep learning algorithms in solving classification problems for the very high resolution (VHR) images, the target mask U-Net model is introduced to research VHR satellite data from China, i. e., the GaoFen-1 (GF-1), GaoFen-2 (GF-2) and ZiYuan-3 (ZY-3). First, a sugarcane area was classified and extracted in the Ningming Sugarcane Demonstration Area in Chongzuo City, Guangxi. Further, we validated and compared the extraction accuracies for different satellite data. The results showed that the extraction accuracies of the GF-1, GF-2 and ZY-3 were 79.97% (Kappa coefficient of 0.19), 94.02% (Kappa coefficient of 0.82) and 81.94% (Kappa coefficient of 0.35), respectively. The spectral and textural information of high-resolution images can effectively guarantee improvements to the accuracy of crop extraction. By comparison of data sources and traditional supervision classification methods, the GF-2 data features the best results for sugarcane extraction. The technical methods and experimental results in this paper not only confirm the feasibility of applying China’s VHR data to monitor sugarcane planting areas, but also provides reference for the relevant future studies.

Published
2020

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