As one of my many coursework projects for the UCLA Master of Applied Geospatial Information Systems and Technologies (MAGIST) program, I practiced different methods of image classification.
The supervised object-based classification result is considerably more accurate than the supervised pixel-based classification result. This is apparent by comparing the confusion matrices: the object-based classification result has consistent higher user and producer accuracy (i.e. fewer false positives and false negatives).
This also makes intuitive sense based on a visual comparison of the two classification results. The pixel-based classification has several other inaccuracies clearly visible just from a visual survey of the map: for example, multiple patches of sand and rock are classified as “developed”, shown on the map as wispy gray patterns distant from any actual settlement. This seems to be a somewhat “organic” error, reflecting real-world similarities as opposed to errors in training dat…
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