Share Email Print

Proceedings Paper

Combining multispectral images and selected textural features from high-resolution images to improve discrimination of forest canopies
Author(s): Luis A. Ruiz; Igor Inan; Juan E. Baridon; Jorge W. Lanfranco
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Discrimination of vegetation canopies for production of forestry and land use thematic cartography from multispectral satellite images requires high spectral and spatial resolutions, usually not available in this type of images. A methodology is proposed to improve a vegetation oriented classification from a Landsat TM image by adding texture information obtained from panchromatic aerial photographs. Multispectral classification was used to create a mask of the forested areas that was applied over the aerial mosaic composition. Further vegetation classes were defined based on textural differences, and eight texture features derived from the gray level co-occurrence matrix, three textural energy indicators and a factor of edgeness were tested. A selection of optimal features and textural parameters such as number of gray levels, window size and distance between pixels was performed using principal components and stepwise discriminant analysis techniques with a set of representative samples from each class. After a texture segmentation of panchromatic aerial imagery using optimal parameters and features was completed, a post-classification process based on morphological operations was applied to avoid the neighboring effect generated by the texture analysis. Overall accuracy in the identification of texture classes using the four best feathers was 86.6%, while the 88% of accuracy was achieved in the classification of the complete image. This method is useful for discrimination of certain vegetation classes with low spectral separability and arranged in small forest units, increasing the classification detail in those areas of particular interest.

Paper Details

Date Published: 4 December 1998
PDF: 11 pages
Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); doi: 10.1117/12.331856
Show Author Affiliations
Luis A. Ruiz, Univ. Politecnica de Valencia (Spain)
Igor Inan, Univ. Politecnica de Valencia (Spain)
Juan E. Baridon, Univ. Nacional de La Plata (Argentina)
Jorge W. Lanfranco, Univ. Nacional de La Plata (Argentina)

Published in SPIE Proceedings Vol. 3500:
Image and Signal Processing for Remote Sensing IV
Sebastiano Bruno Serpico, Editor(s)

© SPIE. Terms of Use
Back to Top