MINIMAL LEARNING MACHINE IN ANOMALY DETECTION FROM HYPERSPECTRAL IMAGES

Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel dista...

詳細記述

書誌詳細
主要な著者: I. Pölönen, K. Riihiaho, A.-M. Hakola, L. Annala
フォーマット: 論文
言語:English
出版事項: Copernicus Publications 2020-08-01
シリーズ:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
オンライン・アクセス:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/467/2020/isprs-archives-XLIII-B3-2020-467-2020.pdf