Analysis of co-occurrence and discrete wavelet transform textures for differentiation of forest and non-forest vegetation in very-high-resolution optical-sensor imagery

The extraction of texture features from high-resolution remote sensing imagery provides a complementary source of data for those applications in which the spectral information is not sufficient for identification or classification of spectrally similar landscape features. This study presents the results of grey-level co-occurrence matrix (GLCM) and wavelet transform (WT) texture analysis for forest and non-forest vegetation types differentiation in QuickBird imagery. Using semivariogram fitting, the optimal GLCM windows for the land cover classes within the scene were determined. These optimal window sizes were then applied to eight GLCM texture measures (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation) for the scene classification. Using wavelet transformation, up to five levels of macro-texture were computed and tested in the classification process. Comparing the classification results, (1) the spectral-only bands classification gave an overall accuracy of 58.69%; (2) the statistically derived 21 x 21 optimal mean texture combined with spectral information gave the best results among the GLCM optimal windows with an accuracy of 73.70%; and (3) the combined optimal WT-texture levels 4 and 5 gave an accuracy of 63.56%. The combined classification of these three optimal results gave an overall accuracy of 77.93%. The results indicate that even though vegetation texture was generally measured better by the GLCM-mean texture (micro-textures) than by WT-derived texture (macro-textures), the results show that the micro-macro texture combination would improve the differentiation and classification of the overall vegetation types. Overall, the results suggests that computer-assisted classification of high-spatial-resolution remotely sensed imagery has a good potential to augment the present ground-based forest inventory methods.