Dynamic Negative Sampling Autoencoder for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) aims at detecting the anomalies without any prerequisite information, which gains lots of attention in recent years. Most of existing detectors locate the anomalies by eliminating the background. The background is usually reconstructed by utilizing only the global and local homogenous attribute, no matter the matrix decomposition-based or the deep learning-based methods. In this article, a dynamic negative sampling autoencoder is proposed for the hyperspectral anomaly detection (DNA-HAD). Some pixels are randomly selected and altered as negative samples. Both the rest original pixels and the altered negative samples are sent into the network. An adaptive adjusted loss function is designed to suppress the reconstruction error for the original pixels, and to enlarge the error for the negative samples. Meanwhile, skip connection is designed to ensure features at both shallow levels and deep levels being utilized for the reconstructing process. In this way, by importing some negative samples in the reconstructing process, the proposed DNA-HAD is not only robust to reconstruct the background but also sensitive to detect the anomalies. Experiments on six hyperspectral imagery which are captured by different sensors have demonstrated the effectiveness of the proposed method.