{'fraction_non_alphanumeric': 0.009380863039399626, 'fraction_numerical': 0.0, 'mean_word_length': 5.276470588235294, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An Adaptive Spatial and Spectral Neighborhood for the Rx Anomaly Detector

The RX anomaly detector is the first and the most studied anomaly detector for hyperspectral images. Since its proposition by Reed and Yu, it showed promoting results for detecting small distinctive objects of a hyperspectral image. However, there are many issues that still unsolved such as: the high false alarm rate caused by the supposition of the linearity of the background, the contamination of the statistic of the background with the signal of anomalies and the non ability of detecting different sizes anomalies. In this context, this paper proposes a new approach based on the selection of an adaptive spatial and spectral neighborhood allowing the reduction of false alarm caused by the supposition of the background linearity and the prevention of the contamination of the background statistics. The experimental results conducted on real hyperspectral scenes confirm the contribution of the proposed approach to achieve the desired performances over state of the art detectors.