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	Evaluating the Dynamic Changes of Urban Land and Its Fractional Covers in Africa from 2000-2020 Using Time Series of Remotely Sensed Images on the Big Data Platform 期刊论文 OAI收割 REMOTE SENSING, 2021, 卷号: 13, 期号: 21, 页码: 21 作者: Yin, Zherui; Kuang, Wenhui; Bao, Yuhai; Dou, Yinyin; Chi, Wenfeng \| 收藏 \| 浏览/下载：34/0 \| 提交时间：2022/09/21 urban land mapping remotely sensed images impervious surface area fraction vegetation space fraction the whole Africa
	Evaluating the Dynamic Changes of Urban Land and Its Fractional Covers in Africa from 2000-2020 Using Time Series of Remotely Sensed Images on the Big Data Platform 期刊论文 OAI收割 REMOTE SENSING, 2021, 卷号: 13, 期号: 21, 页码: 21 作者: Yin, Zherui; Kuang, Wenhui; Bao, Yuhai; Dou, Yinyin; Chi, Wenfeng \| 收藏 \| 浏览/下载：29/0 \| 提交时间：2022/09/21 urban land mapping remotely sensed images impervious surface area fraction vegetation space fraction the whole Africa
	Super-Resolution Land Cover Mapping Based on Multiscale Spatial Regularization SCI/SSCI论文 OAI收割 2015 作者: Hu J. L.; Ge, Y.; Chen, Y. H.; Li, D. Y. 收藏 \| 浏览/下载：26/0 \| 提交时间：2015/12/09 Fraction images heterogeneity homogeneity multiscale regularization remote sensing spatial dependence super-resolution mapping (SRM) markov-random-field remotely-sensed images hopfield neural-network model identification dependence algorithm map
	新疆荒漠稀疏植被覆盖度信息遥感提取方法比较/Comparison of methods based on MODIS for estimating sparse vegetation fraction across desert in Xinjiang[J] 期刊论文 OAI收割干旱区地理, 2013, 卷号: 36, 期号: 3, 页码: 502-511 作者: 李向婷; 白洁; 李光录; 罗格平; 古丽·加帕尔 \| 收藏 \| 浏览/下载：37/0 \| 提交时间：2017/12/29 新疆荒漠区遥感稀疏植被覆盖度 Modis Desert Of Xinjiang Remote Sensing Fraction Of Sparse Vegetation Modis Images
	On hyperspectral remotely sensed image classification based on MNF and AdaBoosting (EI CONFERENCE) 会议论文 OAI收割 2012 3rd IEEE/IET International Conference on Audio, Language and Image Processing, ICALIP 2012, July 16, 2012 - July 18, 2012, Shanghai, China 作者: Yu P.; Yu P.; Gao X. 收藏 \| 浏览/下载：28/0 \| 提交时间：2013/03/25 As an effective statistical learning tool AdaBoosting has been widely used in the field of pattern recognition. In this paper a new method is proposed to improve the classification performance of hyperspectral images by combining the minimum noise fraction (MNF) and AdaBoosting. Because the hyperspectral imagery has many bands which have strong correlation and high redundancy the hyperspectral data are pre-processed by the minimum noise fraction to reduce the data's dimensionality whilst to remove noise bands simultaneously. Then we use an AdaBoost algorithm to conduct the classification of hyperspectral remotely sensed image. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.
	Investigating the gap frequency of a maize canopy using night TIR data 会议论文 OAI收割 2006 Ieee International Geoscience and Remote Sensing Symposium, Vols 1-8, New York Yu, Shanshan; Yu, Tao; Gu, Xinofa; Li, Li; Zhao, Feng 收藏 \| 浏览/下载：16/0 \| 提交时间：2014/12/07 maize canopy gap fraction GORT model night TIR images