{"id":"W2734499646","doi":"10.22075/mseee.2015.249","title":"Improving the RX Anomaly Detection Algorithm for Hyperspectral Images using FFT","year":2015,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Hyperspectral imaging; Fast Fourier transform; Detector; Computer science; Algorithm; Anomaly detection; Dimensionality reduction; Reduction (mathematics); Artificial intelligence; Imaging spectrometer; Computer vision; Spectrometer; Mathematics; Optics; Telecommunications; Physics","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":[0.0009851763,0.0009489241,0.0008900558,0.001817439,0.0004280762,0.0007195343,0.0009041184,0.000539937,0.001662978],"category_scores_gemma":[0.003069333,0.0002906268,0.0008441424,0.001158301,0.0003781389,0.001531795,0.0006951903,0.001038213,0.001004475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004119547,"about_ca_system_score_gemma":0.0007412201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002569915,"about_ca_topic_score_gemma":0.002270411,"domain_scores_codex":[0.9993489,0.00007807327,0.00005738799,0.0001668985,0.0002982682,0.00005034683],"domain_scores_gemma":[0.9988725,0.0003636323,0.0001320291,0.0001422836,0.0004563736,0.0000331042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001894276,0.0001357315,0.004269951,0.0001384419,0.00008850786,0.00009878135,0.0001274097,0.04011071,0.09074073,0.00292521,0.002680746,0.8584943],"study_design_scores_gemma":[0.0000274889,0.00009180293,0.003565835,0.000009929307,0.00002876894,0.0002813323,0.00004494963,0.9452723,0.04536823,0.001460745,0.003819416,0.00002933386],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03296616,0.0001750368,0.9631956,0.00009356644,0.00004646054,0.00004494606,0.00006848622,0.002789573,0.0006201442],"genre_scores_gemma":[0.1123781,0.000192178,0.885286,0.00004340676,0.0000442306,0.00007190763,0.0003749705,0.0001847813,0.001424431],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002569915,"threshold_uncertainty_score":0.0055632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03392966991321122,"score_gpt":0.2424198738696218,"score_spread":0.2084902039564106,"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."}}