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The rising availability of hyperspectral data has increased the attention of anomaly detection for various applications. Anomaly detection aims to find a small number of pixels in the hyperspectral data for which the spectral signatures differ significantly from the background. However, for anomalies like camouflage objects in a rural area, the spectral signatures distinguish only by small features. For this purpose, we use a 1D-Convolutional Autoencoder, which extracts the background spectra’s most specific features to reconstruct the spectral signature by minimizing the loss function’s error. The difference between the original and the reconstructed data can be exploited for anomaly detection. Since the loss function is minimized based on predominant background spectra, areas with anomalies exhibit higher error values. The proposed anomaly detection method’s performance is tested on hyperspectral data in the range of 1000 to 2500 nm. The data was recorded with a drone-based Headwall sensor at approximately 80 m over a rural area near Greding, Germany. The anomalies consist mainly of camouflage materials and vehicles. We compare the performance of a 1D-Convolutional Autoencoder trained on a data set without the target anomalies for different models. This is done to quantify the number of anomalies in the data set before they inhibit the detection process. Additionally, the detection results are compared to the state-of-the-art Reed-Xiaoli anomaly detector. We present the results by counting the correct detections in relation to the false positives with the receiver operating characteristic and discuss more suitable evaluation approaches for small targets. We show that the 1D-CAE outperforms the Reed-Xiaoli anomaly detector for a false alarm rate of 0.1% by reconstructing the background with a low error and the anomalies with a higher error. The 1D-CAE is suitable for camouflage anomaly detection.
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