Journal of Geo-information Science ›› 2015, Vol. 17 ›› Issue (9): 1080-1091.doi: 10.3724/SP.J.1047.2015.01080

• Orginal Article • Previous Articles     Next Articles

Review on High Resolution Remote Sensing Image Classification and Recognition

LIU Yang1,2,3(), FU Zhengye4, ZHENG Fengbin2,3,*()   

  1. 1. College of Environment and Planning, Henan University, Kaifeng 475004, China
    2. Laboratory of Spatial Information Processing, Henan University, Kaifeng 475004, China
    3. College of Computer Science and Information Engineering, Henan University, Kaifeng 475004, China
    4. College of Software, Henan University, Kaifeng 4750041, China
  • Received:2014-12-12 Revised:2015-02-14 Online:2015-09-10 Published:2015-09-07
  • Contact: ZHENG Fengbin E-mail:ly.sci.art@gmail.com;zhengfb@henu.edu.cn
  • About author:

    *The author: SHEN Jingwei, E-mail:jingweigis@163.com

Abstract:

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.

Key words: target classification and recognition, multi-media neural and cognitive computing, parallel computing, deep learning, topic model