CONTRIBUTION OF TERRASAR-X RADAR IMAGES TEXTURE FOR FOREST MONITORING

被引:3
|
作者
Benelcadi, H. [1 ]
Frison, P. -L. [1 ]
Lardeux, C.
Capel, A. -C.
Routier, J. -B.
Rudant, J. -P. [1 ]
机构
[1] Univ Paris Est Marne La Vallee, Lab ESYCOM, Paris, France
关键词
Texture; Haralick; Variogram; SAR; TerraSAR-X; SVM; REDD; Tropical forest;
D O I
10.1109/IGARSS.2012.6352130
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This study aims to evaluate the texture analysis of high spatial resolution images for mapping tropical forests. More precisely, it evaluates the potential of TerraSAR-X image, with spatial resolution of 0.5 meter for the classification of tropical forests located in southern Cambodia. In particular, the focus is put on the contribution of the analysis of textural information for classification. This latter is apprehended through the analysis of Haralick textural parameters. The retained algorithm of classification is the Support Vector Machine, as it allows taking into account numerous parameters, which can be heterogeneous with respect to their physical dimension. First results show that the addition of Haralick parameters to intensity channel may improve significantly the accuracy of the classification results. However, their performance for classification discrimination strongly depends on the size of the neighborhood from which they are estimated. Preliminary analysis of variograms allows optimizing the choice of the neighborhood size. Best results are obtained with a 25x25 sliding window size, with a classification accuracy improvement higher than 50% is observed.
引用
收藏
页码:6427 / 6430
页数:4
相关论文
共 50 条
  • [31] Study on X-band polarization ratio with TerraSAR-X images
    Ren, Yongzheng
    Lehner, Susanne
    He, Mingxia
    REMOTE SENSING AND MODELING OF THE ATMOSPHERE, OCEANS, AND INTERACTIONS III, 2010, 7856
  • [32] Estimating Forest Biomass From TerraSAR-X Stripmap Radargrammetry
    Solberg, Svein
    Riegler, Gertrud
    Nonin, Philippe
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2015, 53 (01): : 154 - 161
  • [33] Comparative study of ship detection techniques in TerraSAR-X images
    Martin-de-Nicolas, J.
    Mata-Moya, D.
    Jarabo-Amores, M. P.
    del-Rey-Maestre, N.
    Pelaez-Sanchez, V. M.
    2014 44TH EUROPEAN MICROWAVE CONFERENCE (EUMC), 2014, : 1836 - 1839
  • [34] Integration of SSC TerraSAR-X Images into Multisource Rapid Mapping
    Vassilaki, Dimitra I.
    Stamos, Athanassios A.
    Ioannidis, Charalabos
    PHOTOGRAMMETRIC RECORD, 2017, 32 (158): : 160 - 181
  • [35] Ship Classification in TerraSAR-X Images With Convolutional Neural Networks
    Bentes, Carlos
    Velotto, Domenico
    Tings, Bjoern
    IEEE JOURNAL OF OCEANIC ENGINEERING, 2018, 43 (01) : 258 - 266
  • [36] Land Subsidence Detection by PSInSAR™ Based on TerraSAR-X Images
    Jia Hongliang
    Yu Bing
    Zhang Rui
    Sang Mingzhi
    ADVANCED MEASUREMENT AND TEST, PTS 1-3, 2011, 301-303 : 641 - +
  • [37] Comparative study of ship detection techniques in TerraSAR-X images
    Martin-de-Nicolas, J.
    Mata-Moya, D.
    Jarabo-Amores, M. P.
    del-Rey-Maestre, N.
    Pelaez-Sanchez, V. M.
    2014 11TH EUROPEAN RADAR CONFERENCE (EURAD), 2014, : 533 - 536
  • [38] UNSUPERVISED SEGMENTATION OF AGRICULTURAL REGIONS USING TERRASAR-X IMAGES
    Bratsolis, Emmanuel
    2009 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-5, 2009, : 1718 - 1721
  • [39] Differentiating forest types using TerraSAR-X spotlight images based on inferential statistics and multivariate analysis
    Farghaly, Dalia
    Urban, Brigitte
    Soergel, Uwe
    Elba, Emad
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2019, 15
  • [40] Monitoring of Mining Induced Land Subsidence by PALSAR and TerraSAR-X
    Deguchi, Tomonori
    Kutoglu, Hakan
    SAR IMAGE ANALYSIS, MODELING, AND TECHNIQUES XII, 2012, 8536