Skip all navigation and jump to content Jump to site navigation
NASA Logo - Goddard Space Flight Center

+ NASA Homepage

    
Goddard Space Flight Center
About MODIS News Data /images2 Science Team Science Team Science Team

   + Home
ABOUT MODIS
MODIS Publications Link
MODIS Presentations Link
MODIS Biographies Link
MODIS Science Team Meetings Link
 

 

 

Tsaneva, MG, Krezhova, DD, Yanev, TK (2010). Development and testing of a statistical texture model for land cover classification of the Black Sea region with MODIS imagery. ADVANCES IN SPACE RESEARCH, 46(7), 872-878.

Abstract
A statistical model is proposed for analysis of the texture of land cover types for global and regional land cover classification by using texture features extracted by multiresolution image analysis techniques. It consists of four novel indices representing second-order texture, which are calculated after wavelet decomposition of an image and after texture extraction by a new approach that makes use of a four-pixel texture unit. The model was applied to four satellite images of the Black Sea region, obtained by Terra/MODIS and Aqua/MODIS at different spatial resolution. In single texture classification experiments, we used 15 subimages (50 x 50 pixels) of the selected classes of land covers that are present in the satellite images studied. These subimages were subjected to one-level and two-level decompositions by using orthonormal spline and Gabor-like spline wavelets. The texture indices were calculated and used as feature vectors in the supervised classification system with neural networks. The testing of the model was based on the use of two kinds of widely accepted statistical texture quantities: five texture features determined by the co-occurrence matrix (angular second moment, contrast, correlation, inverse difference moment, entropy), and four statistical texture features determined after the wavelet transformation (mean, standard deviation, energy, entropy). The supervised neural network classification was performed and the discrimination ability of the proposed texture indices was found comparable with that for the sets of five GLCM texture features and four wavelet-based texture features. The results obtained from the neural network classifier showed that the proposed texture model yielded an accuracy of 92.86% on average after orthonormal wavelet decomposition and 100% after Gabor-like wavelet decomposition for texture classification of the examined land cover types on satellite images. (c) 2010 COSPAR. Published by Elsevier Ltd. All rights reserved.

DOI:
10.1016/j.asr.2010.05.011

ISSN:
0273-1177

FirstGov logo Privacy Policy and Important Notices NASA logo

Curator: Brandon Maccherone
NASA Official: Shannell Frazier

NASA Home Page Goddard Space Flight Center Home Page