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A Google search for image rectification erdas turns up a number of useful looking tutorials, as well as the help file. Image rectification requires: the raw image (in this case, the jpegs) ground control points The raw image doesn't need to have any projection data (indeed, I'd say shouldn't have, because if it's already georeferenced then a projection ...


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The problems you are encountering are likely a result of your MATLAB code used to create NDVI. There should be no need for filtering or resampling to lower resolution. The following code is what I use in MATLAB to create NDVI from 4-band NAIP imagery at both half meter and 1 meter spatial resolution. file = 'F:\input\4211258_se.tif'; [I R] = ...


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I am interpreting your question to ask if you can generate training data from 1 image and apply those statistics to the other 4 images by normalizing the other 4 to the first. This seems dubious to me. While it is possible to normalize atmospheric differences, there are other differences (e.g. phenology, recent weather events) that make normalizing ...


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A quick Google search threw up these papers, I am sure there are? http://edcintl.cr.usgs.gov/ip/macga/docs/Helmer.Ruef.2007_CJRS_33.pdf http://www.abdn.ac.uk/geospatial/ers/PAPERS/radiometric.pdf


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Image segmentation works by applying any of a number of algorithms to group similar pixels into objects. Most image analysis does not end with segmentation. Rather, you need to add another step in you workflow to classify these objects into usable information classes. One method for reclassifying these large tiles in the image would be to set up a size ...


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The task that you are describing is called image classification. There are numerous ways to classify an image--from very basic thresholding to more advanced supervised classification approaches. Image classification using multispectral data like Landsat requires basic radiometric correction, often accomplished using Dark Object Subtraction. Song et al. ...



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