Treelets-based method for detecting remote sensing image changes

Abstract

The invention discloses a treelets-based method for detecting remote sensing image changes and belongs to the field of remote sensing image analysis and processing, aiming to solve the problem that a traditional method for detecting remote sensing image changes has plenty of false change information. The method comprises the steps of: (1) carrying out Treelets filter to an input time-phase remotesensing image 1; (2) carrying out the Treelets cross filter to an input time-phase remote sensing image 2; (3) calculating difference values of corresponding pixel point gray values of the two time-phase remote sensing images subjected to filter to obtain a difference image; (4) carrying out the Treelets filter to the difference image once more to obtain a new difference image; (5) dividing the new difference image into a changed type or an unchanged type by adopting K-means clustering to obtain a final change detection result graph. The method invention can effectively reduce the impacts of unsatisfactory radiation correction and uneven illumination to detection results, improve the precision of the change detection, and can be used for disaster monitoring, land utilization and agricultural investigation.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for detecting changes in remote sensing images based on Treelets, which is suitable for remote sensing image analysis and processing. Background technique [0002] The research on remote sensing image change detection began in the 1970s. With the continuous development of image processing technology, the research on remote sensing image change detection has gradually become a hot spot and has been widely used in many fields of national economy and national defense construction, such as forest resources. Dynamic monitoring of land cover and utilization changes, agricultural resource survey, urban planning layout, environmental monitoring and analysis, natural disaster assessment, geographic data update, and dynamic monitoring of strategic targets such as roads, bridges, and airports in military reconnaissance, etc. [0003] Due to the limitations of the technical...

AI Technical Summary

Problems solved by technology

However, due to factors such as illumination and radiation in different seasons and conditions, there are overall or partial differences in the gray value of the image between different time phases of remote sensing images. Threshold segmentation, there are a lot of pseudo-change information in the obtained change detection results, which makes the accuracy of change detection low

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