{'fraction_non_alphanumeric': 0.02593440122044241, 'fraction_numerical': 0.0, 'mean_word_length': 5.905263157894737, '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}}
A Sturdy Nonlinear Hyperspectral Unmixing Algorithm Using Iterative Block- Coordinate Descent Algorithm

To depict the hyperspectral data, here a sturdy mixing model is implemented by employing various perfect spectral signatures mixture, which enhances the generally utilized linear mixture model (LMM) by inserting an extra term that describes the potential nonlinear effects (NEs), which are addressed as additive nonlinearities (NLs) those are disseminated without dense. Accompanying the traditional nonnegativity and sum-to-one restraints underlying to the spectral mixing, this proposed model heads to a novel pattern of sturdy nonnegative matrix factorization (S-NMF) with a term named group sparse outlier. The factorization is presented as an issue of optimization which is later dealt by an iterative block-coordinated descent algorithm (IB-CDA) regarding the updates with maximation-minimisation. Moreover, distinctive hyperspectral mixture models also presented by adopting the considerations like NEs, mismodelling effects (MEs) and endmember variability (EV). The extensive simulation analysis by the implementation of proposed models with their estimation approaches tested on both the synthetic and real-time images. Further, it is also shown that the comparative analysis with the conventional