2 Xu G H, Ge Q S, Gong P, et al. Societal response to challenges ofglobal change and human sustainable development. Chin Sci Bull, 2013, 58: 3161–3168 [徐冠华, 葛全胜, 宫鹏, 等. 全球变化和人类可持续发展: 挑战与对策. 科学通报, 2013, 58: 2100–2106]
3 Gao F, Feng Y, Hou C M, et al. Development strategy on earth observing technology of the main countries (in Chinese). Remote Sens Technol Appl, 2006, 6: 565–576 [高峰, 冯筠, 侯春梅, 等. 世界主要国家对地观测技术发展策略. 遥感技术与应用, 2006, 6: 565–576]
4 Guo H D. Scientific Satellite for Global Change Research (in Chinese). Beijing: Science Press, 2014 [郭华东. 全球变化科学卫星. 北京: 科学出版社, 2014]
5 Li D R, Tong Q X, Li R X, et al. Current issues in high-resolution Earth observation technology. Sci China Earth Sci, 2012, 55:1043–1051 [李德仁, 童庆禧, 李荣兴, 等. 高分辨率对地观测的若干前沿科学问题. 中国科学: 地球科学, 2012, 42: 805–813]
6 Guo H D, Wang L Z, Chen F, et al. Scientific big data and digital Earth (in Chinese). Chin Sci Bull, 2014, 59: 1047–1054 [郭华东, 王力哲, 陈方, 等. 科学大数据与数字地球. 科学通报, 2014, 59: 1047–1054]
7 Ma Y. A research on the key techniques of parallel computing platform for data-intensive remotesensing image processing (in Chinese). Dissertation for the Doctoral Degree. Beijing: Institute of Electronics Chinese Academy of Sciences, 2013 [马艳. 数据密集型遥感图像并行处理平台关键技术研究. 博士学位论文. 北京: 中国科学院电子学研究所, 2013]
8 Forecast International. The Market for Civil & Commercial Remote Sensing Satellites. Analysis Report. Newtown: Forecast International, 2013. 2013–2022
9 Wei J B, Liu D S, Wang L Z. A general metric and parallel framework for adaptive image fusion in clusters. Concurr Comp-Pract E, 2014, 26: 1375–1387
10 Zhang W F, Wang L Z, Liu D S, et al. Towards building a multi-datacenter infrastructure for massive remote sensing image processing. Concurr Comp-Pract E, 2013,25: 1798–1812
11 Ma Y, Zhao L J, Liu D S. An asynchronous parallelized and scalable image resampling algorithm with parallel I/O. In: Gabrielle A, Jaro-slaw N, Edward S, et al.,eds. Proceedings of ICCS 2009, Part II, LNCS 5545. Heidelberg: Springer, 2009. 357–366
12 Li G, Ma Y, Wang J, et al. Preliminary through-out research onparallel-based remote sensing image processing. In: Vassil N A, Geert D V A, Peter M A S, et al., eds. Proceedings of ICCS 2006, LNCS 3991. Heidelberg: Springer, 2006. 880–883
13 Ma Y, Wang L Z, Liu D S, et al. Distributed data structure templates for data-intensive remote sensing applications. Concurr Comp-Pract E, 2013, 25: 1784–1797
14 Ma Y, Wang L Z, Liu D S, et al. Generic parallel programming for massive remote sensing data processing. In: Cluster Computing (CLUSTER), 2012 IEEE International Conference. Beijing: IEEE, 2012. 420–428
15 Zhang W, Wang L Z, Ma Y, et al. Design and implementation of task scheduling strategies for massive remote sensing data processing across multiple data centers. Software Pract Exper, 2014, 44: 873–886
16 Ma Y, Wang L Z, Albert Y, et al. Task-Tree Based Large-Scale Mosaicking for massive remote sensed imageries with dynamic DAG scheduling. IEEE T Parall Distr, 2014, 25: 2126–2137
17 Wang L Z, Lu K, Liu P, et al. IK-SVD: Dictionary learning for spatial big data via incremental atom update. Comput Sci Eng, 2014, 16: 41–52
18 He G J, Zhang X M, Jiao W L, et al. A study on the data miningstrategy based intelligent information processing technologies for satellite remote sensing (in Chinese). Sci Tech Engng, 2005, 24: 1911–1915 [何国金, 张晓美, 焦伟利, 等. 基于数据挖掘机制的卫星遥感信息智能处理方法研究. 科学技术与工程, 2005, 24: 1911–1915]
19 Liu J B, Yang J, Chen F, et al. Location-based instant satellite image service: Concept and system design. Int J Digit Earth, 2014, doi: 10.1080/17538947.2014.942395
6/6 首页 上一页 4 5 6