Publication Date:
2019-07-13
Description:
We have classified four different images, under various levels of JPEG compression, using the following classification algorithms: minimum-distance, maximum-likelihood, and neural network. The training site accuracy and percent difference from the original classification were tabulated for each image compression level, with maximum-likelihood showing the poorest results. In general, as compression ratio increased, the classification retained its overall appearance, but much of the pixel-to-pixel detail was eliminated. We also examined the effect of compression on spatial pattern detection using a neural network.
Keywords:
EARTH RESOURCES AND REMOTE SENSING
Type:
NASA-CR-199550
,
NAS 1.26:199550
,
RIACS-TR-95-18
,
NIPS-95-05574
,
Annual IEEE International Geoscience and Remote Sensing Symposium; Jul 20, 1995 - Jul 24, 1995; Florence; Italy
Format:
application/pdf
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