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A Target-coupled Multiagent Reinforcement Learning Approach for Teams of Mobile Sensing Robots 会议论文
Shenyang, China, November 8-11, 2021
作者:  Wang X(王鑫);  Zang CZ(臧传治);  Xu SQ(许书卿);  Zeng P(曾鹏)
收藏  |  浏览/下载：12/0  |  提交时间：2021/12/13
REMOTELY SENSED LAND COVER CHANGE IN CENTRAL ASIA (1990-2010) 会议论文
MLCCM 2015, 长沙
作者:  Sun Bo;  Liu Ping;  Chen Huijuan;  Liu Jun;  Zhang Qingling
收藏  |  浏览/下载：14/0  |  提交时间：2016/01/27
ESTIMATION OF GRASS YIELD IN LARGE REGION ON GEOGRAPHICALLY WEIGHTED REGRESSION MODEL 会议论文
36th International Symposium on Remote Sensing of Environment, MAY 11-15, 2015
作者:  Luo Chengfeng;  Yu Xiujuan;  Liu Caijuan;  Du Yingkun
收藏  |  浏览/下载：3/0  |  提交时间：2019/12/31
Monitoring Glacier Changes of Recent 50 Years in the Upper Reaches of Heihe River Basin Based on Remotely-Sensed Data 会议论文
35th International Symposium on Remote Sensing of Environment (ISRSE35), Beijing, PEOPLES R CHINA, APR 22-26, 2013
作者:  Bie, Q;  He, L;  Zhao, CY
收藏  |  浏览/下载：2/0  |  提交时间：2017/01/18
Coastline remote sensing monitoring and change analysis of Laizhou Bay from 1978 to 2009 会议论文
Nanjing, China, 7 26, 2013 - 7 28, 2013
作者:  Sun, Weifu;  Zhang, Jie;  Ren, Guangbo;  Ma, Yi;  Ni, Shaoqi
收藏  |  浏览/下载：3/0  |  提交时间：2018/12/12
Urban heat island monitoring and analysis based on remotely sensed data 会议论文
2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013, July 21, 2013 - July 26, 2013, Melbourne, VIC, Australia
作者:  Xie Gaodi;  Liu Aimin
收藏  |  浏览/下载：19/0  |  提交时间：2014/12/30
Evaluation of land ecological environment in Zhoushan Islands by remotely sensed impervious surfaces 会议论文
作者:  Zhang, Xiaoping;  Pan, Delu;  Chen, Jianyu;  Chen, Peng;  Jia, Yonghong
收藏  |  浏览/下载：9/0  |  提交时间：2019/12/05
An improved hyperspectral classification algorithm based on back-propagation neural networks (EI CONFERENCE) 会议论文
2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China
Mao W.; Yu P.; Guo B.; Xu Y.; Chen H.
收藏  |  浏览/下载：19/0  |  提交时间：2013/03/25
In this paper  a new method is proposed to improve the classification performance of hyperspectral images by combining the principal component analysis (PCA)  genetic algorithm (GA)  and artificial neural networks (ANNs). First  some characteristics of the hyperspectral remotely sensed data  such as high correlation  high redundancy  etc.  are investigated. Based on the above analysis  we propose to use the principal component analysis to capture the main information existing in the hyperspectral images and reduce its dimensionality consequently. Next  we use neural networks to classify the reduced hyperspectral data. Since the back-propagation neural network we used is easy to suffer from the local minimum problem  we adopt a genetic algorithm to optimize the BP network's weights and the threshold. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.  
Remote sensed mountain forest FVC and its seasonal variability analysis 会议论文
2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, Nanjing, China, June 1, 2012 - June 3,2012
Yang, Guijun; Huang, Wenjiang; Wang, Jihua; Zhao, Chunjiang
收藏  |  浏览/下载：16/0  |  提交时间：2014/12/07
A preliminary study on the application of remotely sensed SST in locating evaporation duct height 会议论文
Remote Sensing of the Marine Environment II, Kyoto, Japan, October 31, 2012 - November 1,2012
Baig, Muhammad Hasan Ali; Wang, Zhenhui; Zhang, Lifu; Yang, Lu
收藏  |  浏览/下载：16/0  |  提交时间：2014/12/07

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