MINIMAL LEARNING MACHINE IN ANOMALY DETECTION FROM HYPERSPECTRAL IMAGES

I. Pölönen , Kimmo A. Riihiaho , Anna-Maria Hakola , Leevi Annala ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.5194/isprs-archives-xliii-b3-2020-467-2020

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 distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate.

Concepts

Hyperspectral imaging Anomaly detection Computer science Constant false alarm rate Artificial intelligence Data cube Pattern recognition (psychology) Machine learning Data mining

Metadata

Type: article
Language: en
Landing Page: https://doi.org/10.5194/isprs-archives-xliii-b3-2020-467-2020
PDF: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/467/2020/isprs-archives-XLIII-B3-2020-467-2020.pdf
OA Status: diamond
Cited By: 2
References: 15
Related Works: 10
OpenAlex ID: https://openalex.org/W3080512163

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