Deep neural network-based synthetic aperture radar (SAR) image change detection algorithms are affected by coherent speckle noise in the original image. Existing denoising methods have predominantly focused on generating binary images based on the pre-classification of original pixels, which is insufficient in removing interfering noise. Herein, to further reduce the noise points generated in the clustering algorithm, we combined the characteristics of the fuzzy clustering algorithm, demonstrating the obvious advantages of the proposed fast and flexible denoising convolutional neural network (FFDNet-F) method. An FFDNet was used to reduce noise interference in real SAR images and improve the detection accuracy and robustness of the method. Difference operators were then drawn from the weak noise images, and fuzzy local information C-means clustering was applied for analysis to generate the change detection results. The experimental results from two real datasets and the comparative analysis with other network models demonstrated the effectiveness of this method. Simultaneously, Gaofen-3 satellite images were used to verify and analyze surface flood disasters in Zhengzhou, China. The findings of this study demonstrate a significant improvement in detection accuracy using the proposed method compared with that of other algorithms.
The heterogeneity of geospatial information will persist for a long time. As the key to overcome the semantic heterogeneity, categories mapping has gained considerable attention. In previous studies, the existing geographic ontologies cannot support enough multi-semantic extension to conquer semantic heterogeneity effectively. When introduced to explore categories mapping, many semantic similarity measures encountered the problem of subjective weight setting and failed to make full use of the organizational structure information of categories. The rapid development of Artificial Intelligence (AI) and Natural Language Processing (NLP) bring new enlightenment to the semantic analysis and understanding in the geographic information field. Therefore, this paper proposes a new geographic categories mapping method based on ontology attribute characteristics learning, which utilizes ontology attributes and the classification hierarchy of geographic categories. Firstly, a basic semantic framework based on ontology attributes is defined to realize the semantic vectorization descriptions of geographic categories, by extracting semantic knowledge from definitions. Then, a new hierarchical coding method is proposed to describe the classification hierarchy of categories and identify the classification status of each category. After that, a self-learning mapping mechanism based on BP neural network is used to establish the non-linear relationship between ontology attribute eigenvectors and classification states, which can support categories mapping. Finally, some categories mappings are formed by this method to evaluate transition effects, and introduces the category differentiation degree to analyze the influence of classification structure on prediction accuracy. The preliminary results show the feasibility and reliability of the proposed model for automatic semantic mapping.
The automatic identification of overpass structures is of great significance for multi-scale modeling, spatial analysis, and vehicle navigation of road networks. The traditional method of overpass recognition based on vector data relies too heavily on the characteristics of manual design and has poor adaptability to complex scenes. In this paper, a method for overpass identification based on the target detection model Faster R-CNN (Regions with Convolutional Neural Network) is proposed. This method uses a Convolutional Neural Network to learn the deep structural characteristics of data samples, and then automatically identifies and finds accurate positioning of the overpasses. The experimental results show that this method is able to identify overpasses and can accurately determine their positions in a complex road network, avoiding the influence of human intervention on the uncertainty of results. This method also has strong anti-interference abilities
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