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Palabras clave – provistas por la editorial

on-board data compression; CCSDS 123.0-B-2; near-lossless hyperspectral image compression; hyperspectral image coding; graph filterbanks; integer-to-integer transforms; graph signal processing; compact data structure; quadtree; k2-tree; k2-raster; DACs; 3D-CALIC; M-CALIC; hyperspectral images; fully convolutional network; semantic segmentation; spectral image; tensor decomposition; HEVC; intra coding; JPEG 2000; high bit-depth compression; multispectral satellite images; crop classification; Landsat-8; Sentinel-2; Elias codes; Simple9; Simple16; PForDelta; Rice codes; hyperspectral scenes; hyperspectral image; lossy compression; real time; FPGA; PCA; JPEG2000; EBCOT; multispectral; hyperspectral; CCSDS; FAPEC; data compression; transform; hyperspectral imaging; on-board processing; GPU; real-time performance; UAV; parallel computing; remote sensing; image quality; image classification; visual quality metrics; spectral–spatial feature; multispectral image compression; partitioned extraction; group convolution; rate-distortion; compressed sensing; invertible projection; coupled dictionary; singular value; task-driven learning; on board compression; transform coding; learned compression; neural networks; variational autoencoder; complexity; real-time compression; on-board compression; real-time transmission; UAVs; compressive sensing; synthetic aperture sonar; underwater sonar imaging; remote sensing data compression; lossless compression; compression impact; computational complexity