{'fraction_non_alphanumeric': 0.013528748590755355, 'fraction_numerical': 0.0, 'mean_word_length': 5.166666666666667, '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}}
Hyperspectral Target Detection Using Kernel Spectral Matched Filter

In this paper a non-linear matched filter is introduced for target detection in hyperspectral imagery which is implemented by using the ideas in kernel-based learning theory. The proposed non-linear matched filter exploits the notion that performing matched filtering in a non-linear feature space of high dimensionality increases the probability of detection. Defining matched filter in a kernel feature space is equivalent to a non-linear matched filter in the original input space which allows the higher order correlation between the spectral bands to be exploited. It is also shown that the non-linear matched filter can easily be implemented using the ideas of kernel functions. The kernel version of the non-linear matched filter is implemented and simulation results are shown to outperform the linear version.