Lunar Terrain Auto Identification Based on DEM Topographic Factor and Texture Feature Analysis

被引:0
|
作者
Wang, Jian [1 ]
Wang, Junlin [1 ]
Jiang, Hongkun [1 ]
Tian, Xiaolin [2 ]
Xu, Aoao [2 ]
机构
[1] Macau Univ Sci & Technol, Fac Informat & Technol, Macau 999078, Peoples R China
[2] Macau Univ Sci & Technol, Space Sci Inst, Lunar & Planetary Sci Lab, Macau 999078, Peoples R China
来源
PROCEEDINGS OF 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION (ICICTA 2015) | 2015年
关键词
Auto Identification; DEM; Lunar Terrain; Texture Feature; Topographic Factor;
D O I
10.1109/ICICTA.2015.137
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A new auto method to identify the lunar terrain of LROC DEM data has been proposed. The new method combined topographic factors and the texture feature parameter together to form feature vectors for descripting the different lunar terrains of DEM. Then the new method will normalize these feature vectors for getting the better classifying results. Normalized feature vectors would be clustered to two categories, which are lunar mare and lunar highland. The new method has been tested by near 1000 different terrain samples of DEM data and the testing results were satisfied compared with known methods; especially for lunar mare areas, the correct recognition rates of the new method were more than 88.29%, and the overall correct recognition rates of the new method were up to 95%.
引用
收藏
页码:534 / 537
页数:4
相关论文
共 13 条
  • [1] Automatic detection of lunar craters based on DEM data with the terrain analysis method
    Zhou, Yi
    Zhao, Hao
    Che, Min
    Tu, Jie
    Yan, Long
    PLANETARY AND SPACE SCIENCE, 2018, 160 : 1 - 11
  • [2] Conceptual Model of Terrain Texture in Loess Plateau based on DEM
    Jiang S.
    Tang G.
    Yang X.
    Xiong L.
    Qian C.
    Journal of Geo-Information Science, 2021, 23 (06) : 959 - 968
  • [3] The Sensitivity Analysis of DEM Terrain Texture Characteristics Based on Grey Level Co-occurrence Matrix
    Wang, Chun
    Tao, Yang
    Liu, Kai
    Jiang, Ling
    2014 22ND INTERNATIONAL CONFERENCE ON GEOINFORMATICS (GEOINFORMATICS 2014), 2014,
  • [4] DEM-based Watershed Topographic Attributes Extraction and Analysis
    Yuan Lifeng
    Zhou Qigang
    Zhang Qingfeng
    Li Wenwen
    Jiang Weiguo
    2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8, 2006, : 911 - +
  • [6] Identification of Malignant Masses on Digital Mammogram Images based on Texture Feature and Correlation based Feature Selection
    Nugroho, Hanung Adi
    Faisal, N.
    Soesanti, Indah
    Choridah, Lina
    2014 6TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND ELECTRICAL ENGINEERING (ICITEE), 2014, : 96 - 101
  • [7] TEXTURE FEATURE-BASED LANGUAGE IDENTIFICATION USING GABOR AND MDLC FEATURES
    Jang, Ick Hoon
    Kim, Nam Chul
    Park, Min Ho
    2011 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME), 2011,
  • [8] Research on DEM geomorphic factor terrain recognition algorithm using probabilistic neural networks based on tactile systems
    Xing, Meichao
    Du, Qiaoling
    Bi, Zhenlong
    TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2024, 46 (11) : 2174 - 2185
  • [9] A new lunar global DEM derived from Chang'E-1 Laser Altimeter data based on crossover adjustment with local topographic constraint
    Hu, Wenmin
    Di, Kaichang
    Liu, Zhaoqin
    Ping, Jinsong
    PLANETARY AND SPACE SCIENCE, 2013, 87 : 173 - 182
  • [10] Contrast Enhancement Analysis to Detect Glaucoma Based on Texture Feature in Retinal Fundus Image
    SatyaNugraha, Gibran
    Soesanti, Indah
    SunuWibirama
    ADVANCED SCIENCE LETTERS, 2017, 23 (03) : 2326 - 2328