{'fraction_non_alphanumeric': 0.02483660130718954, 'fraction_numerical': 0.0, 'mean_word_length': 5.330578512396694, '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 urban remote sensing image based on support vector machines

To investigate remote sensing classification approach based on support vector machines (SVMs), we classify a remotely sensed image of urban area of Kunming, China, by SVM-based classifiers with radial basis function (RBF) as kernel function. The best values of parameter gamma (γ) of RBF and penalty parameter C are chosen carefully through training phase. Then, data are classified by the SVM-based classifiers with the best values of parameters. Producer's accuracy and user's accuracy are analyzed and kappa hat coefficient is computed based on an error matrix to evaluate the approach. Our study indicates that the classification approach can yield excellent classification results.