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Use of a library such as GDAL gives a huge benefit to those writing remote-sensing processing code, as it allows them to ignore the details of the individual file formats they are using (whether they are GeoTIFF files, ENVI files or the very complex Erdas IMAGINE format files) and treat all files in exactly the same way, using a very simple API which allows easy loading and saving of chunks of images to and from numpy arrays. Rather than treating the image as a collection of pixels to be classified on their individual spectral properties, the image pixels can be initially grouped into segments and the object segments can then be classified according to spectral and other criteria, such as shape, size and relationship to neighboring objects.

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