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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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Rescaling an NDVI or EVI from -1 to 1, to 0 to 1, uses the Rescale function (under Raster, Radiometric). Clipping the top and bottom 0.5% is a percentage linear contrast stretch. To do this in ERDAS 2013, click on Panchromatic, then General Contrast. This brings up the Contrast Adjust window (seen below), and you can choose a variety of different methods ...


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In ENVI go to "Band math" and tape: float(b1)/0.0001 look at http://www.exelisvis.fr/docs/BandMath.html , Actually there is no need for this, the reason why values ​​are multiplied by 0.0001 is only for size decreasing of files. Classification adopted by MODIS is [0.2 to 0] =Water, [0 to 0.2]=No vegetation,sand, spare vegetation ... [0.2 to 1]= Vegetation , ...


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A good documentation can be found here (.pdf). Also look at the Erdas forum here. Another good resource is ERDAS IMAGINE 2014 Python Examples



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