OBJECT-BASED ANALYSIS FOR URBAN LAND COVER MAPPING USING THE INTERIMAGE AND THE SIPINA FREE SOFTWARE PACKAGES

被引:0
|
作者
Antunes, Rodrigo Rodrigues [1 ]
Bias, Edilson de Souza [1 ]
Ostwald Pedro da Costa, Gilson Alexandre [2 ]
Brites, Ricardo Seixas [1 ]
机构
[1] Univ Brasilia, Inst Geociencias, Brasilia, DF, Brazil
[2] Univ Estado Rio de Janeiro, Rio De Janeiro, Brazil
来源
BOLETIM DE CIENCIAS GEODESICAS | 2018年 / 24卷 / 01期
关键词
Object-Based Image Analysis; Data Mining; InterIMAGE; SIPINA;
D O I
10.1590/S1982-21702018000100001
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
In this work we introduce an object-based method, applied to urban land cover mapping. The method is implemented with two open-source tools: SIPINA, a data mining software package; and InterIMAGE, an object-based image analysis system. Initially, segmentation, feature extraction and sample selection procedures are performed with InterIMAGE. In order to reduce the time and subjectivity involved to develop the decision rules in InterIMAGE, a data mining step is then carried out with SIPINA. In sequence, the decision trees delivered by SIPINA are analysed and encoded into InterIMAGE decision rules for the final classification step. Experiments were conducted using a subset of a GeoEye image, acquired in January 01, 2013, covering the urban portion of the municipality of Goianesia, Brazil. Five decision tree induction algorithms, available in SIPINA, were tested: ID3, C45, GID3, Assistant86 and CHAID. The TAU and Kappa coefficients were used to evaluate the results. The TAU values obtained were in the range of 0.66 and 0.70, while those for Kappa varied from 0.65 to 0.69.
引用
收藏
页码:1 / 17
页数:17
相关论文
共 50 条
  • [41] An Object-Based Approach for Urban Land Cover Classification: Integrating LiDAR Height and Intensity Data
    Zhou, Weiqi
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2013, 10 (04) : 928 - 931
  • [42] Object-based urban detailed land cover classification with high spatial resolution IKONOS imagery
    Pu, Ruiliang
    Landry, Shawn
    Yu, Qian
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2011, 32 (12) : 3285 - 3308
  • [43] Use of Binary Partition Tree and energy minimization for object-based classification of urban land cover
    Li, Mengmeng
    Bijker, Wietske
    Stein, Alfred
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2015, 102 : 48 - 61
  • [44] Object-Based Land Cover Mapping using Adaptive Scale Segmentation from ZY-3 Satellite images
    Zhou, Ya'nan
    Feng, Li
    Chen, Yuehong
    Li, Jun
    2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 63 - 66
  • [45] DISCRETIZATION OF OBJECT-BASED LIDAR FEATURES FOR LAND COVER CLASSIFICATION
    Lin, Yu-Ching
    Lin, Chun-Lin
    Tsai, Ming-Da
    Chou, Lin-Sun
    2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 1768 - 1771
  • [46] Exploring the synergistic use of multi-scale image object metrics for land-use/land-cover mapping using an object-based approach
    Han, Ning
    Du, Huaqiang
    Zhou, Guomo
    Xu, Xiaojun
    Ge, Hongli
    Liu, Lijuan
    Gao, Guolong
    Sun, Shaobo
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2015, 36 (13) : 3544 - 3562
  • [47] Object-Based Land Cover Classification Using Airborne Lidar and Different Spectral Images
    Teo, Tee-Ann
    Huang, Chun-Hsuan
    TERRESTRIAL ATMOSPHERIC AND OCEANIC SCIENCES, 2016, 27 (04): : 491 - 504
  • [48] Combining per-pixel and object-based classifications for mapping land cover over large areas
    Costa, Hugo
    Carrao, Hugo
    Bacao, Fernando
    Caetano, Mario
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2014, 35 (02) : 738 - 753
  • [49] Stratifying land use/land cover for spatial analysis of disease ecology and risk: an example using object-based classification techniques
    Koch, David E.
    Mohler, Rhett L.
    Goodin, Douglas G.
    GEOSPATIAL HEALTH, 2007, 2 (01) : 15 - 28
  • [50] An object-based temporal inversion approach to urban land use change analysis
    Toure, Sory
    Stow, Douglas
    Shih, Hsiang-chien
    Coulter, Lloyd
    Weeks, John
    Engstrom, Ryan
    Sandborn, Avery
    REMOTE SENSING LETTERS, 2016, 7 (05) : 503 - 512