Remote sensing image change detection method and system based on mask classification

Abstract

The invention provides a remote sensing image change detection method and system based on mask classification, and the method comprises the steps: 1, the preprocessing of a multiband remote sensing image; 2, the single-band separation of the multiband remote sensing image after preprocessing; 3, the constructing of a single-band difference image of two-phase single-band remote sensing images; 4, the construction of a change mask difference image, and cutting the change mask difference image based on an Otsu method; 5, the multiplication fusion of a change mask with one of the two-phase single-band remote sensing images, thereby obtaining a change region; 6, the clustering of the change region through a fuzzy C mean value method; and 7, determining the change type of the change region through combination of the prior knowledge of the ground object type of the other one of the two-phase single-band remote sensing images according to the clustering results of the change region. The method and system are resistant to nose interference, can alleviate local optimum effectively, can consider the qualitative and quantitative description of an image change process, and is high in change detection precision and reliability.

Description

technical field [0001] The invention belongs to the technical field of photogrammetry and remote sensing image application, in particular to a remote sensing image change detection method and system based on mask classification, which is suitable for real-time, automatic (or semi-automatic) detection of remote sensing image changes. Background technique [0002] Remote sensing image change detection is to detect the time-changing information of the ground objects in the area by analyzing the remote sensing images of the same area in different phases. With the rapid development of aerospace technology, remote sensing observation data has been more and more widely used due to its characteristics of real-time, fast, wide coverage, and high temporal and spatial resolution. How to effectively extract the change information in massive data and use it for the prevention of various natural disasters faced by the environment, agriculture, ecosystems and human beings has become a hot ...

AI Technical Summary

Problems solved by technology

[0003] At present, the commonly used remote sensing image change detection methods include direct comparison method and post-classification comparison method. The former is simple and fast, but it can only quantitatively describe whether the target area has changed, and it is difficult to determine the nature of the change; the latter can provide information about the type of change , but it is necessary to carry out image classification twice and formulate a unified classification standard, and the detection accuracy is affected by the error propagation of separate classification

In addition, there is a phenomenon of mixed pixels in medium and high-resolution remote sensing images, and traditional "hard" classification methods cannot obtain high accuracy. Fuzzy C-means clustering (FCM) is a soft clustering algorithm that uses membership degrees to make classes There is no clear boundary between the cluster and the class, and it is effective to deal with mixed pixels, but there are defects such as over-reliance on the initial cluster center, difficulty in automatically determining the number of categories, and sensitivity to isolated point noise data.

Images