BockNet: Blind-Block Reconstruction Network With a Guard Window for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) aims to identify anomalous targets that deviate from the surrounding background in unlabeled hyperspectral images (HSIs). Most existing deep networks that exploit reconstruction errors to detect anomalies are prone to fit anomalous pixels, thus yielding small reconstruction errors for anomalies, which is not favorable for separating targets from HSIs. In order to achieve a superior background reconstruction network for HAD purposes, this paper proposes a self-supervised blind-block network (termed BockNet) with a guard window. BockNet creates a blind-block (guard window) in the center of the network’s receptive field, rendering it unable to see the information inside the…

• NN
  National Natural Science Foundation of China

  Awards: 42001287, 62161160336
• MD
  Ministerio de Ciencia e Innovación

  Award: PID2019-110315RB-I00