  # LAKSHMI

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• Gender
Female
• Location
India
• Interests
Image processing
1. I'm trying to do a fuzzy land cover classification using maximum likelihood classification. I need to get the probability of each pixel to fall in a particular class. Some equations have been formed based on gaussian distribution to calculate the fuzzy membership value corresponding to each pixel. To calculate this value each pixels are to be considered individually and substituted in the following equation: D = (X-M)^T * (cov)^-1 * (X-M) where X is a 4*1 matrix representing the DN for a particular pixel in 4 bands. I'm trying to do this using erdas imagine model maker, but failed to call each pixel separately. How can I do this ? Can I use Spatial modeler language to modify the model ? How can we initialize a pixel in "while loop" of spatial modeler language ?
2. can anyone please help me to analyse each pixel of a raster image using while loop in erdas imagine spatial modeler language ? I have a 4 layered raster image. i need to consider each pixel as a column matrix whose number of rows is equal to the number of layers of the raster image. then the transpose of each pixel matrix is to be find. how to do this in erdas imagine ?
3. I am doing my masters in geoinformatics. My thesis topic is MODELING OF SPATIAL OBJECTS FOR LAND COVER CLASSIFICATION USING FUZZY TOPOLOGY. I need to calculate a measure of the distance of a pixel from its mean pixel. for that I need to calculate (x-m)T* (cov©)-1 * (x-m) where x- pixel vector m-mean vector of the sample (signeture for supervised classification T-transpose function cov©-covariance matrix of the sample I have a raster image from which x can be taken, mean and covariance matrices. I made a model to calculate (x-m) and got a result as a raster image. can anyone help me to proceed further ?
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