Patch-based Sparse and Convolutional Autoencoders for Anomaly Detection in Hyperspectral Images

Abstract

Anomaly target detection is one of the major aims of Hyperspectral Image (HSI) processing. Since anomalous pixels Compose a small fraction of the hyperspectral data cube, the use of supervised neural networks presents many complications. The reason is that supervised networks need a large training set to fine-tune the model. In this paper we propose two semi-supervised autoencoder based anomaly detection methods using the reconstruction error of each pixel. The reconstruction error is the mean absolute error between each pixel and its reconstruction by the proposed autoencoder networks. The proposed networks are deep fully-connected sparse autoencoders (SAE) and deep one-dimensional convolutional autoencoders (CAE). In addition, a patch-based anomaly detection method is proposed which takes spatial correlation between neighbouring pixels into account. We use the San Diego airport hyperspectral data to carry out the experiments. The results are compared with some state-of-the-art HSI anomaly detection methods. Quantitative results employing ROC and AUC metrics demonstrate the superior performance of the proposed method compared to several anomaly detectors.