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Texture analysis plays an increasingly important role in computer vision. Since the textural properties of images appear to carry useful information for discrimination purposes, it is important to develop significant features for texture. Various texture feature extraction methods include those based on gray-level values, transforms, auto correlation etc. We have chosen the Gray Level Co occurrence Matrix (GLCM) method for extraction of feature values. Image segmentation is another important problem and occurs frequently in many image processing applications. Although, a number of algorithms exist for this purpose, methods that use the Expectation-Maximization (EM) algorithm are gaining a growing interest. The main feature of this algorithm is that it is capable of estimating the parameters of mixture distribution. This paper presents a novel unsupervised segmentation method based on EM algorithm in which the analysis is applied on vector data rather than the gray level value. [ABSTRACT FROM AUTHOR]

This paper deals with different techniques for registration and fusion of remote sensed images. In this work the lower spatial resolution multispectral and higher resolution panchromatic images of SPOT satellite are used. These images are registered using a registration algorithm that combines a simple yet powerful search strategy based on stochastic gradient with the similarity measure as mutual information, together with a wavelet-based multi-resolution pyramid. The algorithm is found to give sub pixel registration accuracy. The study is limited to pairs of images, which are misaligned by rotation and/or translation. The registered images are subjected to a pixel level multispectral image fusion process using wavelet transform approach. Spectral quality assessments shows that compared to other conventional image fusion techniques, this fusion process using wavelet transform keeps much of the spectral information in the merged image with respect to the original multispectral one. Finally, segmentation is performed on the fused images to validate the algorithms used for registration and fusion and the results show better accuracy for wavelet based methods than the conventional methods. [ABSTRACT FROM AUTHOR]