A cascaded autoencoder unmixing network for Hyperspectral anomaly detection
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.