{"id":"W2382485842","doi":"","title":"A Subpixel Target Detection Approach Based on Endmember Extraction in Hyperspectral Image","year":2014,"lang":"en","type":"article","venue":"Science Technology and Engineering","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Subpixel rendering; Endmember; Hyperspectral imaging; Principal component analysis; Pattern recognition (psychology); Artificial intelligence; Anomaly detection; Projection (relational algebra); Computer science; Subspace topology; Orthographic projection; Computer vision; Mathematics; Pixel; Algorithm","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":[0.0003499832,0.0009296094,0.0009970878,0.001571923,0.0003873062,0.0006906929,0.000953967,0.0008293889,0.001320529],"category_scores_gemma":[0.0004170298,0.0003969981,0.0007048122,0.0008950977,0.0003640096,0.001413123,0.0008245434,0.0006664377,0.0007777457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002447078,"about_ca_system_score_gemma":0.000566833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00126675,"about_ca_topic_score_gemma":0.002205468,"domain_scores_codex":[0.999585,0.00004028863,0.00001967863,0.0001325832,0.0001795709,0.00004289401],"domain_scores_gemma":[0.9997569,0.00004326084,0.0000257157,0.00003655778,0.0001160949,0.00002142523],"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":[0.0001460373,0.0001781599,0.001821482,0.0001929203,0.0001010998,0.000133713,0.0001043526,0.01127201,0.2754098,0.002935909,0.001693833,0.7060107],"study_design_scores_gemma":[0.00002500955,0.0002345795,0.00536456,0.00001623686,0.00009488196,0.0007237867,0.00007277385,0.8060335,0.1778555,0.00333238,0.006170276,0.0000764877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02111784,0.0002675118,0.9765139,0.00006233835,0.00005523942,0.00004600353,0.00005165011,0.001123604,0.0007618988],"genre_scores_gemma":[0.120373,0.0003323288,0.876058,0.00009675774,0.00005587447,0.00008792971,0.0002054634,0.00009210104,0.002698424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001571923,"threshold_uncertainty_score":0.004417658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00446936630558867,"score_gpt":0.1823999242316531,"score_spread":0.1779305579260644,"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."}}