Texture feature neural classifier for remote sensing image retrieval systems

Mauricio Pozzobon MartinsLamartine N. Frutuoso GuimaraesLeila Maria Garcia Fonseca

Texture information is useful for image data browsing and retrieval. The goal of this paper is to present a texture classification system for remote sensing images addressed to the administration of great collections of those images. The proposed classifier is a hybrid system composed by an unsupervised neural network and a supervised one. Starting from a small portion of the image (pattern) the system should recognize the most similar class to the pattern in a database as well as to identify the images that contain similar patterns. The texture feature vectors used to characterize the patterns are obtained from the images processed by a bank of Gabor Filters. Some experimental results using textures of the Brodatz album, multi-spectral and radar images have presented here.

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