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高分辨率遥感影像目标分类与识别研究进展
Review on High Resolution Remote Sensing Image Classification and Recognition

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刘扬 1   付征叶 2   郑逢斌 3 *  
文摘 高分辨率遥感影像的目标分类与识别,是对地观测系统进行图像分析理解,以及自动目标识别系统提取目标信息的重要手段。本文综述了当前国内外在可见光、红外、合成孔径雷达和合成孔径声纳等遥感影像的目标分类与识别的关键技术和最新研究进展。首先,讨论了高分辨率遥感影像的目标分类与识别问题的主要研究层次和内容;其次,深入分析了高分辨率遥感影像目标分类与识别,在滤波降噪、特征提取、目标检测、场景分类、目标分类和目标识别的关键技术及其所存在的问题;最后,结合并行计算、神经计算和认知计算等技术,讨论了目标分类与识别的可行性方案。具体包括:(1)高性能并行计算在高分辨率遥感图像处理的主流技术,并给出了基于Hadoop+OpenMP+CUDA的高分辨率遥感影像混合并行处理架构;(2)深度学习对于提升目标分类和识别精度的应用前景,以及基于深度神经网络的多层次遥感影像目标识别方法;(3)认知计算在解决遥感影像大数据不确定性分析的模型与算法,并讨论了层次主题模型的多尺度遥感影像场景描述方案。此外,根据媒体神经认知计算的相关研究,探讨了遥感影像大数据的目标分类和识别的发展趋势和研究方向。
其他语种文摘 Target classification and recognition (TCR) of high resolution remote sensing image is an important approach of image analysis, for the understanding of earth observation system (EOS), and for extracting information from the automatic target recognition (ATR) system, which has important values in military and civil fields. This paper reviews the latest progress and key technologies between domestic and international remote sensing image TCR in optical, infrared, synthetic aperture radar (SAR) and synthetic aperture sonar (SAS). The main research levels and the contents of high resolution remote sensing image TCR are firstly discussed. Then, the key technologies and their existing problems of high resolution remote sensing image TCR are deeply analyzed, from aspects such as filtering and noise reduction, feature extraction, target detection, scene classification, target classification and target recognition. Finally, combined with the related technologies including parallel computing, neural computing and cognitive computing, the new methods of TCR are discussed. Specifically, the main framework includes three aspects, which are detailed in the following. Firstly, the predominant techniques of high resolution remote sensing image processing are discussed based on high performance parallel computing. And the hybrid parallel architecture of high resolution remote sensing image processing based on Apache Hadoop, open multi-processing (OpenMP) and compute unified device architecture (CUDA) are also presented in this paper. Secondly, application prospects of TCR accuracy promotion are analyzed based on a thorough study of neuromorphic computing, and the method of multi-level remote sensing image target recognition based on the deep neural network (DNN) is introduced. Thirdly, the model and algorithm of big data uncertainty analysis for remote sensing images are discussed based on probabilistic graphical model (PGM) of cognitive computing, and the multi-scale remote sensing image scene description is given based on hierarchical topic model (HTM). Moreover, according to the related research of multi-media neural cognitive computing (MNCC), we discuss the development trend and research direction of TCR for remote sensing images big data in the future.
来源 地球信息科学学报 ,2015,17(9):1080-1091 【核心库】
DOI 10.3724/SP.J.1047.2015.01080
关键词 目标分类与识别 ; 媒体神经认知计算 ; 并行计算 ; 深度学习 ; 主题模型
地址

1. 河南大学环境与规划学院, 开封, 475004  

2. 河南大学软件学院, 开封, 475004  

3. 河南大学空间信息处理实验室, 开封, 475004

语种 中文
文献类型 综述型
ISSN 1560-8999
学科 自动化技术、计算机技术
基金 国家自然科学基金项目 ;  国防科技工业民用专项科学技术研究 ;  河南省教育厅科学技术研究重点项目
文献收藏号 CSCD:5544044

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引证文献 29

1 邵华 基于词对主题模型的中分辨率遥感影像土地利用分类 农业工程学报,2016,32(22):259-265
被引 2

2 周福送 基于强监督部件模型的遥感图像目标检测 计算机应用,2016,36(6):1714-1718,1729
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