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Tensor Decomposition for HSI Anomaly Detection by aaryan.sonawane88 is a document available to read on EtoBox.

What is Tensor Decomposition for HSI Anomaly Detection about?

This paper presents a novel anomaly detection algorithm for hyperspectral imagery (HSI) based on tensor decomposition, which simultaneously exploits both spectral and spatial anomalies. The proposed method utilizes Tucker decomposition to analyze the HSI data cube, allowing for better detection performance compared to traditional spectral-only approaches. Experimental results demonstrate that this method achieves higher detection probabilities and lower false alarm rates than existing algorithms.

Author

aaryan.sonawane88

Language

EN