Deep anomaly detection in hyperspectral images based on membership maps and object area filtering

In this paper, we propose a novel framework for transferred deep learning-based anomaly detection in hyperspectral images. The proposed framework includes four main steps. Firstly, the image2019s spectral dimension is reduced by applying the principal component analysis (PCA) to decrease computational time. Secondly, a deep convolutional neural network (CNN) is trained using only one image to learn the pixels' similarities in a picture. Consequently, a novel and well-designed algorithm entitled object area filtering (OAF) is employed to benefit from this learned similarity for extracting objects in the image. The OAF removes irrelevant objects by comparing their area to an acceptable anomaly area range. Lastly, the final result is obtained by multiplying the network output and binary map of anomalies. The receiver operating characteristic (ROC) is employed to evaluate the proposed framework. Extensive experimental evaluations demonstrate that the proposed framework substantially outperforms a significant number of comparable state-of-the-art methods. Finally, we empirically verify that the deep network exhibits excellent domain adaptability.

Automated summary

This paper proposes a novel framework for transferred deep learning-based anomaly detection in hyperspectral images, which includes PCA dimension reduction, CNN training, and OAF algorithm utilization. The framework outperforms many state-of-the-art methods and demonstrates excellent domain adaptability.