Title :
Robust Autodual Morphological Profiles for the Classification of High-Resolution Satellite Images
Author :
Bin Luo ; Liangpei Zhang
Author_Institution :
State Key Lab. of Inf. Eng. in Surveying, Wuhan Univ., Wuhan, China
Abstract :
Morphological profiles are widely used for the classification of high-resolution remote sensing images. Images are degraded by morphological operators with different sizes. Profiles, such as the variation of intensities of the pixels, are extracted during the degradation. In this paper, two issues with morphological profiles are addressed. First, we propose using the topographic map of the image, which is autodual and does not require any structural element, for the degradation of the image. Second, we propose extracting other profiles that are more robust than the intensity variations. Classification experiments are carried out on two IKONOS remote sensing images with a 1-m resolution and one Pléiades 1-A image with a 2-m resolution. The efficiencies of the new profiles are validated by the experimental results.
Keywords :
artificial satellites; feature extraction; geophysical image processing; image classification; image resolution; remote sensing; IKONOS remote sensing imaging; Pléiades 1-A imaging; high-resolution remote sensing image classification; high-resolution satellite image classification; pixel intensity extraction; robust autodual morphological profile; topographic image mapping; Classification; high resolution; mathematical morphology; remote sensing;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2013.2251468