{"id":"W2760542128","doi":"10.5194/isprs-archives-xlii-4-w4-91-2017","title":"GRAPH-BASED SEMI-SUPERVISED HYPERSPECTRAL IMAGE CLASSIFICATION USING SPATIAL INFORMATION","year":2017,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Hyperspectral imaging; Pattern recognition (psychology); Computer science; Graph; Spatial analysis; Artificial intelligence; Support vector machine; Contextual image classification; Data mining; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006169948,0.0007180985,0.0008114788,0.001632628,0.000373198,0.0006338324,0.0009468424,0.0005609617,0.001122735],"category_scores_gemma":[0.001438192,0.0002418746,0.0008777413,0.000990413,0.0005912131,0.001105727,0.0006207908,0.0005651957,0.0004609948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005912067,"about_ca_system_score_gemma":0.0006038949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003450036,"about_ca_topic_score_gemma":0.005311351,"domain_scores_codex":[0.9993224,0.0001918685,0.00003196445,0.0001881203,0.0002176825,0.00004809033],"domain_scores_gemma":[0.9988555,0.0003582774,0.0001771071,0.0001538329,0.0004065561,0.00004882044],"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":[0.0003393821,0.0003057098,0.004670633,0.0003041849,0.0002523533,0.0001413846,0.0002017207,0.354573,0.03554929,0.005654078,0.006304772,0.5917036],"study_design_scores_gemma":[0.000005286219,0.00002534152,0.000674098,0.00000493478,0.00001148344,0.00002907551,0.00002139598,0.9938834,0.003161398,0.001759809,0.0004158185,0.000008072321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08421417,0.0003067612,0.9106241,0.0002424302,0.00004639918,0.00009320943,0.0002446538,0.002249142,0.00197913],"genre_scores_gemma":[0.7630115,0.0001950728,0.23226,0.0001881954,0.00005683602,0.0001311756,0.001254459,0.0001530723,0.002749769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003450036,"threshold_uncertainty_score":0.006859899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02749573250728471,"score_gpt":0.2655616282296495,"score_spread":0.2380658957223648,"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."}}