Automatic analysis method and system capable of realizing all-weather remote sensing monitoring

Abstract

The invention relates to the technical field of urban construction, in particular to an all-weather remote sensing monitoring automatic analysis method and system. The method comprises the following steps: S1, obtaining an original image: obtaining original remote sensing images of a land in different periods; s2, geometric registration: carrying out geometric correction on the remote sensing images by using the ground control point data, and carrying out registration between the images; s3, radiation correction: eliminating or correcting radiation distortion or distorted images in the images, and carrying out normalization processing on the images; s4, performing change detection: performing change detection on the image by using a change detection algorithm based on a convolutional neural network and transfer learning, and generating a change result; and S5, result analysis: analyzing the change result, and carrying out early warning processing on the land use condition. Automatic change detection of images in different periods is realized, the change detection precision is improved, the number of manual intervention times is reduced, and rapid change detection result generation is realized.

Description

technical field [0001] The invention relates to the technical field of urban construction, in particular to an automatic analysis method capable of all-weather remote sensing monitoring and a system thereof. Background technique [0002] At present, my country's urban and rural construction is developing rapidly. The dynamic monitoring of urban and rural construction can monitor the land use and spatial changes of urban and rural construction in a timely and effective manner, and provide a scientific basis for the compilation, detection, supervision, review and revision of urban and rural construction. At present, most of the dynamic monitoring work of urban and rural construction uses manual ground inspections, and the method of comparing on-site drawings to obtain evidence. Due to the subjective singleness of supervisors, problems such as inaccurate surveys and lax land supervision are prone to occur. At the same time, due to the large scope of monitoring, it needs to be ...

AI Technical Summary

Problems solved by technology

In addition, scale parameters are very important in image segmentation, but the setting of scale parameters is very uncontrollable, very important and very difficult, and different settings of any single scale parameter may cause "under-segmentation" or "Over-segmentation", in this case, still need to manually modify the segmentation results, manual intervention to reduce detection errors, to improve the accuracy of change detection

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