Method for post-processing remote sensing image change detection based on multi-scale segmentation-maximum expected

Abstract

The invention discloses a multi-scale segmentation method, which is for post-processing the maximum expected remote sensing image change detection and comprises the following steps of: step a, carrying out spatial position registration on the front image and the rear image of the landslide in the landslide area to obtain the changed image; step b, carrying out multi-scale segmentation on the basisof the rear image of the landslide, and acquiring the multi-scale segmented image object set S, and step c, acquiring the ith object Oi in the S, performing spatial stacking analysis with the initialchange detection result in step a and respectively counting the number of changing pixels and unchanged pixels in the object Oi; step d, refining the pixel attribute in the object Oi by utilizing themaximum expected algorithm; step e, let j=i+1; if j is less than or equal to n, taking the j object Oj in the S, and sequentially executing the step c and the step d, until j) n; Step f: obtaining afinal change detection result. The method has strong universality and obvious effect, and discloses a multi-scale segmentation method, which is for post-processing the maximum expected remote sensingimage change detection.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, in particular to a remote sensing image change detection post-processing method based on multi-scale segmentation-maximum expectation. Background technique [0002] Human activities and natural disasters are constantly changing the surface information of the earth, and timely and effective acquisition of information on changes in the earth's surface is of great significance to many aspects such as environmental monitoring, urban management, and disaster emergency response. Although with the rapid development of satellite and aerial remote sensing technology, the time resolution and spatial resolution of images have been greatly improved, making it possible to quickly and effectively obtain information on land cover changes. However, the detection results obtained by traditional change detection methods still have a lot of noise, which greatly reduces the reliability and de...

AI Technical Summary

Problems solved by technology

However, the detection results obtained by the traditional change detection method still have a lot of noise, which greatly reduces the reliability and detection accuracy of the detection results.

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