{'fraction_non_alphanumeric': 0.034482758620689655, 'fraction_numerical': 0.0, 'mean_word_length': 5.3707317073170735, '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}}
Remote Sensing Image Change Detection Based on Deep Siamese Neural Network with Convolutional Lstm and Channel Attention

With the increase in the amount of remote sensing images (RSIs), deep learning (DL) has been used to the change detection (CD) task in remote sensing field and achieved good results. However, most existing methods do not take full advantage of the temporal dependence of the multi-temporal images. In this paper, we propose a novel method for CD, namely SNN-LSTM (a deep Siamese neural network (SNN) with convolutional Long Short-Term Memory (ConvLSTM) and channel attention module (CAM)), especially for capturing and representing spatial-temporal information effectively. It mainly contains three parts. First, a network based on Siamese convolutional architecture is designed to extract multi-level features. Then, a ConvLSTM block is introduced to further obtain time dependency of multi-temporal RSIs, and spatial information is also extracted simultaneously. Finally, CAM blocks are used to refine the extracted multi-level features, enhance the feature of changes, and eventually generate change map. The experiments are conducted on LEVIR-CD dataset, both visual results and quantitative assessment prove that the proposed method outperforms several state-of-the-art methods.