{"id":"W2620830647","doi":"10.1109/jstars.2017.2706190","title":"Spectral–Spatial Semisupervised Hyperspectral Classification Using Adaptive Neighborhood","year":2017,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Università degli Studi di Pavia","keywords":"Hyperspectral imaging; Pattern recognition (psychology); Spatial analysis; Artificial intelligence; Pixel; Computer science; Graph; Support vector machine; Contextual image classification; Mathematics; Computer vision; Image (mathematics); Statistics","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":[0.000277816,0.0002108178,0.0003302424,0.0002421922,0.0003277496,0.0002264118,0.0001607021,0.0001752132,0.000002154909],"category_scores_gemma":[0.0001371408,0.0002176813,0.00005637183,0.0002656436,0.0001036325,0.0002857056,0.00001431789,0.0005665151,9.200534e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001958941,"about_ca_system_score_gemma":0.0001422187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007943674,"about_ca_topic_score_gemma":0.0001677997,"domain_scores_codex":[0.9985861,0.00003839005,0.0005986117,0.000200002,0.0002894916,0.0002873646],"domain_scores_gemma":[0.9987149,0.00005616785,0.0003676542,0.0003446995,0.0004197292,0.00009690015],"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":[0.0000394335,0.00001974638,0.0004235731,0.00003243674,0.00006420289,0.00002442782,0.0004761063,0.01131723,0.8403044,0.0002706224,0.00002286528,0.1470049],"study_design_scores_gemma":[0.0006759886,0.00003045234,0.1093822,0.0001438458,0.00004522252,0.0001114023,0.0001507451,0.8608237,0.02736883,0.0009494144,0.0001009256,0.0002173144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8280904,0.00008575686,0.1692483,0.0003329674,0.0005090898,0.0001800515,0.000002460291,0.00005093201,0.00150011],"genre_scores_gemma":[0.8534353,0.0001845935,0.145721,0.00002073383,0.0005822578,3.757094e-8,0.000002816683,0.00003407685,0.00001917773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8495065,"threshold_uncertainty_score":0.8876787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05358431907304996,"score_gpt":0.2523560080379189,"score_spread":0.1987716889648689,"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."}}