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A Hybrid Multiresolution Texture Segmentation and its Application to Ikonos Satellite Imagery

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posted on 22.05.2021, 09:28 by Nezamoddin Nezamoddini-Kachouie
In this thesis a method for segmenting textured images using Gabor filters is presented. One of the most recent approaches for texture segmentation and analysis is multi-channel filtering. There are several applicable choices as filter banks which are used for textured images. Gaussian filters modulated by exponential or by sinusoidal filters, known as Gabor filters, have been proven to be very usefyl for texture analysis for the images containing specific frequency and orientation characteristics. Resembling the human visual cortical cells, Gabor function is a popular sub-band filter for multi-channel decompositon. Optimum joint spatial/spatial frequency uncertainty principle and its ability to recognize and pass specific frequencies and orientations are attributes of Gabor filter that make it more attractive. Gabor function with these attributes could simulate the task of simple visual cells in the cortex. Gabor function has several parameters that determine the sub-band Gabor filter and must be determined accurately to extract the features precisely for texture discrimination. A wide selection range for each parameter exists and many combinations of these parameters are possible. Accurate selection and combination of values for the parameters are of crucial importance. Hence a difficult goal is minimizing the number of filters. On the other hand a variety of approaches of texture analysis and recognition have been presented in remote sensing applications, including land cover/land use classification and urban scene segmentation. With the avaiability of very high-resolution commercial satellite imagery such as IKONOS, it is possible to obtain detailed information on urban land use and change detection that are of particular interest to urban and regional planners. In this thesis considering the attributes of human visual system, a hybrid algorithm is implemented using multi-channel decomposition by Gabor filter bank for feature extraction in conjunction with Artificial Neural Networks for both feature reduction and texture segmentation. Three approaches are implemented to optimize Gabor filter bank for image segmentation. Eventually the proposed method is successfully applied for segmentation of IKONOS satellite images.





Master of Applied Science


Electrical and Computer Engineering

Granting Institution

Ryerson University

LAC Thesis Type


Thesis Advisor

Javad Alirezaie Jonathan Li Kaamran Raahemifar