{"id":"W2133078112","doi":"10.1109/36.843010","title":"Vector quantization using spectral index-based multiple subcodebooks for hyperspectral data compression","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Space Agency","funders":"","keywords":"Hyperspectral imaging; Codebook; Vector quantization; Lossy compression; Data compression; Computer science; Quantization (signal processing); Compression ratio; Artificial intelligence; Image compression; Algorithm; Pattern recognition (psychology); Mathematics; Image processing; Physics; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002398302,0.000211491,0.0001986056,0.0002049859,0.0008251028,0.0002003296,0.0006791503,0.00009174347,0.000005872478],"category_scores_gemma":[0.00001518251,0.0001912032,0.00005407885,0.0003783552,0.0001800375,0.001027576,0.00001131797,0.0002079472,0.000002463263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000612634,"about_ca_system_score_gemma":0.000112223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003008818,"about_ca_topic_score_gemma":0.00007305887,"domain_scores_codex":[0.9981104,0.00006855538,0.0002649541,0.0008424325,0.0003133991,0.0004002171],"domain_scores_gemma":[0.9985113,0.0001999544,0.00008645745,0.001005037,0.00006819167,0.0001290244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007986218,0.00005581089,0.000002769016,0.00001490103,0.000004976772,0.000007120238,0.0001257582,0.01209949,0.08054158,0.00003709782,0.00002248185,0.9070082],"study_design_scores_gemma":[0.0004021377,0.00007942792,0.00007189835,0.0001316982,0.00000909254,0.00003620823,0.00001465906,0.8765724,0.1216782,0.0003766172,0.0004050723,0.0002224911],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03921781,0.00002328644,0.9594494,0.000223132,0.0003149969,0.0003638384,0.0000475013,0.0003304264,0.00002957801],"genre_scores_gemma":[0.4426016,0.00001910684,0.5571319,0.0001579657,0.00002389608,2.307685e-7,0.000005939354,0.00001155688,0.00004776862],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9067857,"threshold_uncertainty_score":0.7797041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0543461859741568,"score_gpt":0.3051797222874569,"score_spread":0.2508335363133001,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}