Spectral–Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest
Version 2 2024-06-05, 11:56
Version 1 2021-08-27, 15:40
journal contribution
posted on 2024-06-05, 11:56 authored by X Song, Sunil AryalSunil Aryal, KM Ting, Z Liu, B HeSpectral–Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest
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Journal
IEEE Transactions on Geoscience and Remote SensingVolume
60Location
Piscataway, N.J.ISSN
0196-2892eISSN
1558-0644Language
EnglishNotes
Early Access ArticlePublication classification
C1 Refereed article in a scholarly journalPublisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCUsage metrics
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Keywords
Science & TechnologyPhysical SciencesTechnologyGeochemistry & GeophysicsEngineering, Electrical & ElectronicRemote SensingImaging Science & Photographic TechnologyEngineeringDetectorsHyperspectral imagingAnomaly detectionForestryFeature extractionVegetationTensorshyperspectral image (HSI)isolation forest (iForest)spectral-spatial informationLOW-RANKRX-ALGORITHMREPRESENTATIONFEATURES080109 Pattern Recognition and Data Mining080106 Image Processing4603 Computer vision and multimedia computation
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