document: Noise-constrained hyperspectral data compression Storage and transmission requirements for hyperspectral data sets are significant . To reduce hardware costs , well-designed compression techniques are needed to preserve information content while maximizing compression ratios . Lossless compression techniques maintain data integrity , but yield small compression ratios . We present a slightly lossy compression algorithm that uses the noise statistics of the data to preserve information content while maximizing compression ratios . The adaptive principal components analysis -LRB- APCA -RRB- algorithm uses noise statistics to determine the number of significant principal components and selects only those that are required to represent each pixel to within the noise level . We demonstrate the effectiveness of these methods with airborne visible/infrared spectrometer -LRB- AVIRIS -RRB- , hyperspectral digital imagery collection experiment -LRB- HYDICE -RRB- , hyperspectral mapper -LRB- HYMAP -RRB- , and Hyperion datasets
doc_bio_tags: O O O O O O B I O B I I O O O O O B I O O O O O O O O B I O O B I O B I I O B I O O O O B I O O O O B I I I O O O B I O O O O O B I O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O B O O O B I
extractive_keyphrases: transmission requirements hyperspectral data sets hardware costs information content compression ratios lossless compression techniques data integrity slightly lossy compression algorithm noise statistics noise level hyperspectral mapper hymap hyperion datasets
abstractive_keyphrases: noise-constrained hyperspectral data compression storage requirements adaptive principal components analysis algorithm airborne visible/infrared spectrometer hyperspectral digital imagery collection experiment aviris hydice gaussian statistics