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4 December 1998 Combining multispectral images and selected textural features from high-resolution images to improve discrimination of forest canopies
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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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Luis A. Ruiz, Igor Inan, Juan E. Baridon, and Jorge W. Lanfranco "Combining multispectral images and selected textural features from high-resolution images to improve discrimination of forest canopies", Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); https://doi.org/10.1117/12.331856
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