An unsupervised change detection method based on two-phase high-resolution remote sensing images

Abstract

The invention discloses a non-supervised change detection method based on two phases of high-resolution remote sensing images. The method includes the following steps: Step 1. The two phases of remote sensing images before the event and after the event in the area to be detected are respectively registered in space, so that Geographic areas at the same location overlap; Step 2: Solve the relative change range image between the two phases of remote sensing images before and after the event; Step 3: Based on the relative change range image, establish the relationship between the minimum value threshold and the maximum value threshold Multi-threshold intervals; step 4, use multi-threshold intervals, and set an increase step S to form multiple binarized change area detection results; step 5, perform fusion processing on multiple binarized change area detection results to obtain optimization Binarized change region detection results of . Using the unsupervised change detection method based on two-stage high-resolution remote sensing images, the threshold setting can be effectively avoided in the process, and the data dependence of the algorithm can be effectively reduced.

Description

technical field [0001] The invention belongs to the technical field of remote sensing images, and in particular relates to a non-supervised change detection method based on two-phase high-resolution remote sensing images. Background technique [0002] With the development of remote sensing technology, the spatiotemporal resolution of remote sensing images in the same area has been greatly improved, making it possible to use two remote sensing images to discover areas of land cover change. Using remote sensing images to quickly obtain land cover change areas is of great significance for post-disaster assessment, land use monitoring, and urban expansion monitoring. Change detection based on remote sensing images can be classified into post-classification change detection and pre-classification change detection methods according to the different monitoring results. Among them, the pre-classification change detection method is also called "unsupervised change detection". The pre...

AI Technical Summary

Problems solved by technology

[0003] Although a series of unsupervised change detection methods have been proposed for change detection in the current research, with the improvement of remote sensing image resolution, these methods have shown certain shortcomings in practical applications: (1) Some methods try to pass sequence Experiment to find a reasonable threshold to accurately extract the region of change

However, due to the complexity of the surface geographical environment, coupled with the influence of nonlinear factors such as weather, sensors, and background noise when the remote sensing image is phased, it is difficult to extract a reliable and reasonable change area with a single threshold

Moreover, in theory, the single threshold value obtained by the sequential experiment method is difficult to be suitable for other images, and the universality is poor.

(2) With the improvement of resolution, the variance within the object category becomes larger. The change detection method used in low-to-medium remote sensing images is challenged in the change detection of high-resolution images, and there are more differences in the detection results. Noise, which greatly reduces the accuracy and reliability of change detection

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