Method for realizing remote sensing image ground object detection based on Mask R-CNN as geographical WPS service

Abstract

The invention provides a method for realizing remote sensing image ground object detection based on Mask R-CNN as a geographic WPS service. Remote online and multi-person sharing application of Mask R-CNN model remote sensing image ground object detection can be realized. According to the invention, the method includes: according to application requirements, calling a Mask R-CNN model to carry outautomatic ground object detection and identification on the remote sensing image data, carrying out space vector polygon automatic conversion and space database storage on a ground object detection result, and transmitting the ground object detection result serving as a standard geocoding format to a complete application service process of a client. The method greatly reduces the manual workloadin the remote sensing image ground object detection process, and has a very good promotion effect on the deep application of the Mask R-CNN model in the aspect of ground object detection.

Description

Technical field [0001] The method involves the field of geographic information system, computer network and deep learning technology, and specifically is a method for realizing remote sensing image feature detection based on Mask R-CNN as geographic WPS service. Background technique [0002] Mask R-CNN is a deep learning model for image recognition and detection. Because of its excellent target detection capabilities, it has also been extended to remote sensing images to detect and recognize specific objects. Compared with traditional remote sensing image object recognition algorithms , And achieved good application effects. [0003] Although the use of Mask R-CNN model for surface detection of remote sensing images has great application advantages, in current specific practice, there are the following difficulties: [0004] (1) Using the Mask R-CNN model to perform faster and better detection operations requires a hardware platform equipped with high-performance CPU and GPU, which ...

AI Technical Summary

Problems solved by technology

[0004] (1) Using the Mask R-CNN model to perform faster and better detection operations requires a hardware platform equipped with high-performance CPU and GPU, which is often not available to ordinary application personnel;

[0005] (2) The size and file size of the remote sensing image far exceeds the image usually detected by the Mask R-CNN model, so the common method now is to crop the remote sensing image into a small image that meets the requirements, and then give it to the Mask R-CNN model separately. Detection, which increases a lot of additional data preprocessing workload;

[0006] (3) The detection result of the Mask R-CNN model is a pixel image without geographic coordinates and has no geographic spatial meaning. It needs to be manually converted into geographic data such as space vectors to have practical application value;

[0007] (4) The ground object detection of the Mask R-CNN model is difficult to integrate with other related geographic processing operations to form a unified remote sensing image ground object detection process

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