{"id":"W4221151155","doi":"10.1109/tgrs.2024.3357589","title":"Hyperspectral Pixel Unmixing With Latent Dirichlet Variational Autoencoder","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Endmember; Hyperspectral imaging; Pixel; Autoencoder; Pattern recognition (psychology); Dirichlet distribution; Artificial intelligence; Computer science; Remote sensing; Abundance estimation; Mixing (physics); Mathematics; Geography; Artificial neural network; Physics; Abundance (ecology)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000151655,0.0001795386,0.0001225876,0.0002437051,0.000268889,0.0002475441,0.0000546954,0.0000714073,0.000005661151],"category_scores_gemma":[0.000003866226,0.0001502036,0.00004496144,0.000553411,0.0001476595,0.0003078118,7.789064e-7,0.0002967087,0.00002710922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001314744,"about_ca_system_score_gemma":0.00005858371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005500975,"about_ca_topic_score_gemma":0.00003064709,"domain_scores_codex":[0.9988894,0.00002127345,0.000167271,0.0003587451,0.0002754281,0.0002879245],"domain_scores_gemma":[0.9995874,0.00008110476,0.00001619614,0.0001767464,0.00004793235,0.00009059229],"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":[0.00001330413,0.00001638952,5.051418e-7,0.00007319218,0.00005437864,0.0000622302,0.00115527,0.1425834,0.1025466,0.0000677631,0.0000451548,0.7533817],"study_design_scores_gemma":[0.0001006471,0.00003564674,0.0002864896,0.0001949552,0.00003636866,0.0002969051,0.00007211313,0.9909059,0.007284628,0.0001356966,0.0004457738,0.0002048822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02643402,0.00009404619,0.9706043,0.0005749415,0.0009122546,0.0001081064,0.00000483724,0.0005431177,0.0007243521],"genre_scores_gemma":[0.6605005,0.000115962,0.3385792,0.00006657722,0.00008178298,1.12923e-7,0.000001666124,0.00003982607,0.0006143979],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8483225,"threshold_uncertainty_score":0.6125126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01264203929887554,"score_gpt":0.2189277843140964,"score_spread":0.2062857450152209,"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."}}