{'fraction_non_alphanumeric': 0.01440922190201729, 'fraction_numerical': 0.0, 'mean_word_length': 5.165680473372781, '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}}
Classification of time series of SAR images based on generative model

In this paper, a generative model based on methods for classification of time series of SAR images is proposed. For time series image classification, the feature expression of single image is based on parameters and simple texture features, which can't characterize the image well in some cases. Given this, we propose a generative model-LDA topic model on nonlinear compressed sensing method to better characterize the image with the latent semantic learning instead of the original features. Due to the complexity and uncertainty of the model parameters and the sparsity caused by the process of modeling, we further introduce Compressed Sensing (CS) to encode the model parameters for the distinction and stability. Experiments on the first batch of polarimetric SAR data and ESAR data demonstrate the effectiveness of the proposed LDA topic model. And the presented approaches good performance is proved by experiments on classification of time series of SAR images.