Hyperspectral Data Compression And Red Edge Indices
The Effect of Lossy Image Compression for Hyperspectral Data in Retrieving Red Edge Indices
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Lossy hyperspectral image compression techniques are widely used to solve the problems of data size. In this book, we evaluate lossy vector quantization and JPEG2000 hyperspectral data compression algorithms using red edge indices as end-products that we want to retrieve from our data. Two airborne hyperspectral data-sets for vegetated areas were tested, one acquired from (AISA) sensor for area in Tambisan, Malaysia and the other data-set acquired from (Hyspex) sensor for agriculture area in Norway. Two red edge products: Vogelmann1 and NDVI red edge indices were retrieved from each original d...
Lossy hyperspectral image compression techniques are widely used to solve the problems of data size. In this book, we evaluate lossy vector quantization and JPEG2000 hyperspectral data compression algorithms using red edge indices as end-products that we want to retrieve from our data. Two airborne hyperspectral data-sets for vegetated areas were tested, one acquired from (AISA) sensor for area in Tambisan, Malaysia and the other data-set acquired from (Hyspex) sensor for agriculture area in Norway. Two red edge products: Vogelmann1 and NDVI red edge indices were retrieved from each original data cube and from their decompressed data cubes. The standard deviation of percentage difference between a product retrieved from an original data cube and that from its decompressed data cube was used as a measure to quantify the impact of compression on end products. The minimum, maximum and average values of the original and compressed data were also used to quantify the differences in the red edge products.
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