In this paper, we address the problem of unsupervised detection of anomalies in hyperspectral images. Our proposed method is based on a novel statistical background modeling approach that combines local and global approaches and does not assume Gaussianity. The local-global background model has the ability to adapt to all nuances of the background process, like local models, but avoids overfitting that may result due a too high number of degrees of freedom, producing a high false alarm rate. This is achieved by globally combining the local background models into a “dictionary”, which serves to remove false alarms. Experimental results strongly prove the effectiveness of the proposed algorithm. These results show that the proposed local-global algorithm performs better than several other local or global anomaly detection techniques, such as the well known RX or its Gaussian Mixture version (GMM-RX).
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Non-Gaussian background modeling for anomaly detection in hyperspectral images
Semantic Scholar · Computer Science · 2011
Abstract
In this paper, we address the problem of unsupervised detection of anomalies in hyperspectral images. Our proposed method is based on a novel statistical background modeling approach that combines local and global approaches and does not assume Gaussianity. The local-global background model has the ability to adapt to all nuances of the background process, like local models, but avoids overfitting that may result due a too high number of degrees of freedom, producing a high false alarm rate. This is achieved by globally combining the local background models into a “dictionary”, which serves to remove false alarms. Experimental results strongly prove the effectiveness of the proposed algorithm. These results show that the proposed local-global algorithm performs better than several other local or global anomaly detection techniques, such as the well known RX or its Gaussian Mixture version (GMM-RX).