Texture classification of landsat TM imagery using Bayes point machine

ABSTRACT

The spatial information in a remotely sensed image is often characterized by the texture features, which have been regarded as an important visual primitive to search through large collections of natural visually similar patterns in the image. This paper presents an automated process of extract and classify the texture patterns observed in the remote sensing images such as Landsat TM multispectral images. After the principal component analysis, the first component image, which preserves the largest percentage of the variance, is divided into sub regions. The feature vectors representing the textures of each region are computed by Gabor wavelets and classified using a Bayes Point Machine classifier. The preliminary results show the effectiveness of the process and its potentials in practical remote sensing applications.

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Index Terms

• Applied computing

  • Physical sciences and engineering

    • Earth and atmospheric sciences