The noise2noise-based despeckling method, capable of training the despeckling deep neural network with only noisy synthetic aperture radar (SAR) image, has presented very good performance in recent research. This method requires a fine-registered multi-temporal dataset with minor time variance and uses similarity estimation to compensate for the time variance. However, constructing such a training dataset is very time-consuming and may not be viable for a certain practitioner. In this article, we propose a novel single-image-capable speckling method that combines the similarity-based block-matching and noise referenced deep learning network. The denoising network designed for this method is an encoder–decoder convolutional neural network and is accommodated to small image patches. This method firstly constructs a large number of noisy pairs as training input by similarity-based block-matching in either one noisy SAR image or multiple images. Then, the method trains the network in a Siamese manner with two parameter-sharing branches. The proposed method demonstrates favorable despeckling performance with both simulated and real SAR data with respect to other state-of-the-art reference filters. It also presents satisfying generalization capability as the trained network can despeckle well the unseen image of the same sensor. The main advantage of the proposed method is its application flexibility. It could be trained with either one noisy image or multiple images. Furthermore, the despeckling could be inferred by either the ad hoc trained network or a pre-trained one of the same sensor.
Classifying a time series is a fundamental task in temporal analysis. This provides valuable insights into the temporal characteristics of data. Although it has been applied to traffic flow and individual-centered accessibility analysis, it has yet to be applied to place-centered accessibility research. In this study, we have proposed an actual isochrone and dynamic time-wrapping distance-based k-medoids method and tested its applicability to a bus accessibility analysis. Using bus floating car data, our method calculated the actual isochrone area as an accessibility measurement and constructs an accessibility time series for each hexagonal geographical unit within the area of interest. We then calculated the dynamic time warp distance between the accessibility time series of pairwise geographical units and used these distances for k-medoid clustering. The optimized class number k was selected by considering the elbow method, silhouette score, and human examination. Our case study in Hefei, China demonstrates the feasibility of our method for accessibility time series classification. We also discovered that the resulting classes follow clear spatial patterns, indicating that different time series classes may be correlated with their spatial location. To our knowledge, this is the first time that such a classification method has been applied to place-centered accessibility time series analysis. Our data-driven method can inform place-centered accessibility in an era in which large quantities of spatiotemporal data like floating car data are available.
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