Texture segmentation and analysis is an important aspect of pattern recognition and digital image processing.
Previous approaches to texture analysis and segmentation perform multi-channel filtering by applying a set of filters to the image. In this paper we describe a texture segmentation algorithm based on multi-channel filtering that is optimized using diagonal high frequency residual. Gabor band pass filters with different radial spatial
frequencies and different orientations have optimum resolution in time and frequency domain. The image is
decomposed by a set of Gabor filters into a number of filtered images; each one contains variation of intensity
on a sub-band frequency and orientation. The features extracted by Gabor filters have been applied for image segmentation and analysis. There are some important considerations about filter parameters and filter bank
coverage in frequency domain. This filter bank does not completely cover the corners of the frequency domain
along the diagonals. In our method we optimize the spatial implementation for the Gabor filter bank considering
the diagonal high frequency residual. Segmentation is accomplished by a feedforward backpropagation multi-layer
perceptron that is trained by optimized extracted features. After MLP is trained the input image is segmented and each pixel is assigned to the proper class.
Satellite image segmentation is an important task to generate
classification maps. Land areas are classified and clustered into
groups of similar land cover or land use by segmentation of
satellite images. It may be broad classification such as urban,
forested, open fields and water or may be more specific such as
differentiating corn, soybean, beet and wheat fields. One of the
most important among them is partitioning the urban area to
different regions. On the other hand Multi-Channel filtering is
used widely for texture segmentation by many researchers. This
paper describes a texture segmentation algorithm to segment
satellite images using Gabor filter bank and neural networks. In
the proposed method feature vectors are extracted by multi-channel
decomposition. The spatial/spatial-frequency features of the input
satellite image are extracted by optimized Gabor filter bank. Some
important considerations about filter parameters, filter bank
coverage in frequency domain and the reduction of feature
dimensions are discussed. A competitive network is trained to
extract the best features and to reduce the feature dimension.
Eventually a Multi-Layer Perceptron (MLP) is employed to
accomplish the segmentation task. Our MLP uses the sigmoid
transfer function in all layers and during the training, random
selected feature vectors are assigned to proper classes. After MLP
is trained the optimized extracted features are classified into
sections according to the textured land cover regions.