Multivariate Image Texture by Multivariate Variogram for Multispectral Image Classification

被引:25
|
作者
Li, Peijun [1 ]
Cheng, Tao [2 ]
Guo, Jiancong [1 ]
机构
[1] Peking Univ, Inst Remote Sensing & GIS, Beijing 100871, Peoples R China
[2] Univ Alberta, Dept Earth & Atmospher Sci, Edmonton, AB T6G 2E3, Canada
来源
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING | 2009年 / 75卷 / 02期
关键词
SUPPORT VECTOR MACHINES; LAND-COVER CLASSIFICATION; REMOTELY-SENSED IMAGES; SEGMENTATION; FEATURES; FOREST; INFORMATION;
D O I
10.14358/PERS.75.2.147
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Traditional image texture measure usually allows a texture description of a single band of the spectrum, characterizing the spatial variability of gray-level values within the single-band image. A problem with the approach while applied to multispectral images is that it only uses the texture information from selected bonds. In this paper, we propose a new multivariate texture measure based on the multivariate variogram. The multivariate texture is computed from all bands Of a multispectral image, which characterizes the multivariate spatial autocorrelation among those bands. In order to evaluate the performance of the proposed texture measure, the derived multivariate texture image is combined with the spectral data in image classification. The result is compared to classifications using spectral data alone and plus traditional texture images. A machine learning classifier based on Support Vector Machines (SVMs) is used for image classification. The experimental results demonstrate that the inclusion of multivariate texture information in multispectral image classification significantly improves the overall accuracy, with 5 to 13.5 percent of improvement, compared to the classification with spectral information alone. The results also show that when incorporated in image classification as an additional band, the multivariate texture results in high overall accuracy, which is comparable with or higher than the best results from the existing single-band and two-band texture measures, such as the variogram, cross variogram and Gray-Level Co-occurrence Matrix (GLCM) based texture. Overall, the multivariate texture provides the useful spatial information for land-cover classification, which is different from the traditional single band texture. Moreover, it avoids the band selection procedure which is prerequisite to traditional texture computation and would help to achieve high accuracy in the most classification tasks.
引用
收藏
页码:147 / 157
页数:11
相关论文
共 50 条
  • [1] Multivariate variogram-based multichannel image texture for image classification
    Cheng, T
    Li, PJ
    IGARSS 2005: IEEE International Geoscience and Remote Sensing Symposium, Vols 1-8, Proceedings, 2005, : 3830 - 3832
  • [2] Multivariate Texture Measured by Local Binary Pattern for Multispectral Image Classification
    Song, Cuiyu
    Li, Peijun
    Yang, Fengjie
    2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8, 2006, : 2145 - +
  • [3] Construction method and application of multivariate texture image
    Lu M.
    Wang C.
    Xie Y.-F.
    Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2024, 41 (03): : 555 - 566
  • [4] Image Classification with Multivariate Gaussian Descriptors
    Grana, Costantino
    Serra, Giuseppe
    Manfredi, Marco
    Cucchiara, Rita
    IMAGE ANALYSIS AND PROCESSING (ICIAP 2013), PT II, 2013, 8157 : 111 - 120
  • [5] Algorithm for segmentation of texture image based on image variogram function
    Wu, G.
    Yang, J.A.
    Wang, H.Y.
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2001, 29 (01): : 44 - 47
  • [6] Color texture image retrieval based on Copula multivariate modeling in the
    Etemad, Sadegh
    Amirmazlaghani, Maryam
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2021, 102
  • [7] Multichannel hierarchical image classification using multivariate copulas
    Voisin, Aurelie
    Krylov, Vladimir A.
    Moser, Gabriele
    Serpico, Sebastiano B.
    Zerubia, Josiane
    COMPUTATIONAL IMAGING X, 2012, 8296
  • [8] Multivariate image mining
    Herold, Julia
    Loyek, Christian
    Nattkemper, Tim W.
    WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY, 2011, 1 (01) : 2 - 13
  • [9] Efficient multispectral texture segmentation using multivariate statistics
    Portillo-García, J
    Trueba-Santander, I
    de Miguel-Vela, G
    Alberola-López, C
    IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, 1998, 145 (05): : 357 - 364
  • [10] Efficient multispectral texture segmentation using multivariate statistics
    Sistemas y Radiocomunicaciones of, the Politechnical Univ of Madrid, Madrid, Spain
    IEE Proc Vision Image Signal Proc, 5 (357-364):