{"id":"W2902905019","doi":"10.1109/mmsp.2018.8547139","title":"Deep Transfer Learning for Hyperspectral Image Classification","year":2018,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hyperspectral imaging; Transfer of learning; Computer science; Artificial intelligence; Redundancy (engineering); Pattern recognition (psychology); A priori and a posteriori; Contextual image classification; Divergence (linguistics); Machine learning; 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":[0.0001113093,0.000111553,0.00009463944,0.00006973782,0.00009492316,0.00006268112,0.0000794988,0.00007192789,0.00008355052],"category_scores_gemma":[0.00005693583,0.0001127725,0.00005559413,0.000123592,0.00007347048,0.0001838737,0.000002866023,0.0001040025,0.0002315245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008236336,"about_ca_system_score_gemma":0.000008486182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002357894,"about_ca_topic_score_gemma":0.00001100114,"domain_scores_codex":[0.9993377,0.00001456758,0.0001612401,0.0001795772,0.00008552388,0.000221409],"domain_scores_gemma":[0.9995903,0.00004655668,0.000009327838,0.0001809806,0.0001242323,0.00004862006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001044911,0.000009768736,0.00003206112,0.00002349739,0.00001557001,3.032503e-7,0.0003912212,0.0008697459,0.9724354,0.001809757,0.0009652262,0.02343697],"study_design_scores_gemma":[0.0002335856,0.00004701882,0.002703348,0.000005450415,0.00001465486,0.000004467692,0.0002266076,0.8533829,0.1358209,0.0001558481,0.007252714,0.0001524289],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0774077,0.000021089,0.8864012,0.0002619957,0.0002307807,0.0002079331,5.419216e-7,0.0007091672,0.03475959],"genre_scores_gemma":[0.9432205,0.00001156011,0.0555669,0.00002594458,0.0002955064,0.000009702644,0.00001816561,0.00004969226,0.0008020176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8658128,"threshold_uncertainty_score":0.4598728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02270873058183799,"score_gpt":0.2483697569960441,"score_spread":0.2256610264142061,"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."}}