The way urban climates are classified affects both sustainable urban development and environmental planning. Local Climate Zone (LCZ) classification offers a comprehensive framework to classify different urban areas based on their climate-related characteristics. This paper investigates the application of deep learning techniques for LCZ categorization using multispectral Sentinel-2 satellite images. Sentinel-2's capacity to record optical data over a wide range of spectrum bands makes it an invaluable tool for understanding variations in urban climate. This study uses a deep learning model called convolutional neural networks (CNNs) to effectively extract and learn spatial attributes from the multispectral Sentinel images. The work uses a labeled dataset with Sentinel images for training the model and classifications of LCZ. During the training phase, the model parameters are tuned to enhance the interpretability of climate-related patterns in urban environments. Using a validation dataset, classification metrics such as accuracy, precision, recall, and F1 score are used to evaluate the model performance. These conclusions offer useful information to environmental scientists, urban planners, legislators, and those involved in climate-resilient urban design. This demonstrates the efficacy of using multispectral and SAR images for precise LCZ categorization, advancing our understanding of the variability of urban climate and assisting planners in making well-informed decisions regarding urban development strategies.
In recent years, the demand for target detection in Synthetic Aperture Radar (SAR) under low signal-to-noise ratio conditions is increasing. To improve the ability to detect weak moving targets, this paper proposes a moving target detection method based on quadratic rational kernel function for sequential SAR images. This paper begins by presenting background information on weak moving target detection, followed by the simulation model for sequential SAR images and the specific process for detecting moving target in those sequential images using kernel trick. Finally, numerical experiments on detecting moving target in sequential SAR Images are carried out, and those results demonstrate that the effectiveness of the proposed method.
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