A cascaded autoencoder unmixing network for Hyperspectral anomaly detection

A cascaded autoencoder unmixing network for Hyperspectral anomaly detection

Blog Article

Hyperspectral anomaly detection (HAD) is challenging especially when anomalies are presented in sub-pixel form.The spectral signatures of anomalies in mixed pixels are mixed with those of background, making anomalies difficult to be distinguished from background.Most existing methods detect sub-pixel targets in abundance space by spectral unmixing.However, since abundance feature extraction and turbo air m3f24-1-n anomaly detection are decoupled, the learned features are not well-suitable for the subsequent detection.Moreover, these methods neglect the negative effect of anomalies on spectral unmixing, which leads to degradation of detection performance.

To tackle these problems, we propose a cascaded autoencoder (AE) unmixing network for HAD.First, based on anomalies have larger spectral reconstruction errors than background, a background estimation approach is proposed to alleviate the negative effect of anomalies on spectral unmixing.Second, a cascaded AE is designed to achieve spectral unmixing from the estimated laguna 3hp dust collector background to simultaneously obtain the endmembers and abundance vectors.Third, a deep Gaussian mixture model is leveraged to estimate the density distributions of spectral features since anomalies usually lie in the low-density areas.In this way, spectral unmixing and detection are jointly optimized to construct a unified detection framework.

Experimental results demonstrate that our method achieves superior detection performance to existing state-of-the-art HAD methods.