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...
| المؤلفون الرئيسيون: | , , , |
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| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
Copernicus Publications
2020-08-01
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| سلاسل: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| الوصول للمادة أونلاين: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/467/2020/isprs-archives-XLIII-B3-2020-467-2020.pdf |
| الملخص: | 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. |
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| تدمد: | 1682-1750 2194-9034 |