The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 467–472, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-467-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 467–472, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-467-2020

  21 Aug 2020

21 Aug 2020

MINIMAL LEARNING MACHINE IN ANOMALY DETECTION FROM HYPERSPECTRAL IMAGES

I. Pölönen, K. Riihiaho, A.-M. Hakola, and L. Annala I. Pölönen et al.
  • Faculty of Information Technology, University of Jyväskylä, 40100, Jyväskylä, Finland

Keywords: Minimal Learning Machine, Hyperspectral Imaging, Anomaly Detection, Remote Sensing

Abstract. 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.