Abstract
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.
Highlights
Several rem ote sensing satellites are circling the E arth, each acquiring a very specific type of im agery
Electrical impulses are passed from receptor cells th a t are located on th e back in n e r surface of the eyes to the visual cortex placed in the brain
Some results obtained by applying the proposed approach and two widely used approaches for texture segmentation, Discrete Cosine Ti'ansform (DOT) and Laws filters are presented. As it is mentioned before in C hapter 2, because of th e fast im plem entation and good results, Discrete cosine transform (DCT) n o t only is used in image compression algorithm s such as JPE G compression standard b u t in image segmentation algorithms such as 3 by 3 DCT that is introduced by Ng et al [37] to extract features from textured images
Summary
Several rem ote sensing satellites are circling the E arth, each acquiring a very specific type of im agery. Created images from reflectance measurements provide extremely accurate re p re se n ta tio n of w h at objects and surface features on th e ground look like to th e naked eye according to shape, s’ze, color and overall visual appearance. This representation is known as the sp atial content of the image. To perform a particular function, a neural network can be trained by adjusting the weights of the connections between elements It is estimated th a t the hum an brain contains over 100 billion (10^^) neurons and lO^'* synapses in th e hum an nervous system. This model leads the works of the other researchers such as Jhon von Neumann, Marvin Minsky, Frank R osenblatt, and many others [40, 41, 42, 43]
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