Classification of Urban Functional Areas by Convolution Neural Network Recognition Combined with Sliding Window and Semantic Reasoning

  Objectives  Although remote sensing image is one of the main data sources for the classification of urban functional areas, it is rare to use remote sensing images to classify urban functional areas and extract their attribute information. At present, the classification methods of urban functional areas based on remote sensing images usually need manual interpretation, point of interest data assistance, information questionnaire survey and so on. This kind of method not only needs a lot of manual operation, but also needs other external information sources except remote sensing images.

  Methods  In order to solve the above problems, a sliding window-reasoning method for urban functional area classification is proposed by using convolutional neural network (CNN) to identify sliding window, extract image semantic tags, and combine semantic reasoning mechanism, which can quickly realize urban functional area classification only by remote sensing images. First, a two-level classification table of urban functional areas is established, and the training samples are marked with the second-level urban functional areas, and the training CNN is used as the recognizer. Second, using the designed overlapping sliding window recognition pattern, the trained re-cognizer is used to identify the types of features in the sliding window area, and to determine the type of urban functional area. Finally, a weighted scoring mechanism is designed as the implementation of semantic reasoning, and the semantic reasoning objects are all the recognition results. And, the type of urban functional areas of each region is determined, and the urban functional areas of large-scale remote sensing images are classified.

  Results  Using simulated images and high-resolution remote sensing image experiments, the total classification accuracy of simulated image experiments based on confusion matrix can reach 94.50%, and the total classification accuracy of real remote sensing image experiments based on confusion matrix can reach 92.04%.

  Conclusions  The purpose of the sliding window-reasoning method is to deal with the multi-semantic label results produced by the sliding window recognition through the semantic reasoning method, and to determine the real urban functional area results of the identified objects through multiple semantic tags. The results show that the method of CNN sliding window recognition combined with semantic reasoning is feasible and effective to classify urban functional areas directly using large-scale remote sensing images without the assistance of other information.