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28 May 2004Satellite image classification by narrowband Gabor filters and artificial neural networks
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.
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Nezamoddin Nezamoddini-Kachouie, Javad Alirezaie, "Satellite image classification by narrowband Gabor filters and artificial neural networks," Proc. SPIE 5298, Image Processing: Algorithms and Systems III, (28 May 2004); https://doi.org/10.1117/12.528518