{'fraction_non_alphanumeric': 0.029333333333333333, 'fraction_numerical': 0.0, 'mean_word_length': 5.310924369747899, '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 residual convolutional neural network for pan-shaprening

Pan-sharpening has become an important tool in remote sensing, which normally aims at fusing a multi-spectral image with high spectral resolution and a panchromatic image with high spatial resolution. However, some problems, such as spectral distortion, are facing pan-sharpening methods. Inspired by the applications of convolutional neural network (CNN) in many areas, we adopt an effective CNN model to fulfill pan-sharpening. In our method, only the sparse residuals between the interpolated MS and the pan-sharpened image are learned, which achieves fast convergence and high pan-sharpening quality. The experimental results on real-world data validate the effectiveness of the method.