Multi-temporal SAR (Synthetic Aperture Radar) image change detection method based on change factors

Abstract

The invention discloses a multi-temporal SAR (Synthetic Aperture Radar) image change detection method based on change factors. The multi-temporal SAR image change detection method comprises the specific steps: 1) carrying out radiation correction on SAR data of a plurality of time phases to generate a multi-temporal backward scattering coefficient image; 2) carrying out SAR image filtering treatment on the multi-temporal backward scattering coefficient image to generate a multi-temporal filtering image; 3) carrying out temporal difference calculation on the multi-temporal filtering image to obtain change time of surface features; 4) generating a surface feature change difference image by the multi-temporal filtering image according to a change factor calculation method; 5) carrying out a threshold value division method on the surface feature change difference image to obtain surface feature change information. According to the multi-temporal SAR image change detection method, the change factors are introduced to carry out multi-temporal SAR influence change detection so as to effectively distinguish permanent changes and random changes of a surface feature scattering unit on a timesequence, inhibit random changes caused by natural surface feature season changes, amplify the permanent changes related to human activities and improve the accuracy and stability of change detection.

Description

technical field [0001] The invention relates to the application field of satellite remote sensing, in particular to a multi-temporal synthetic aperture radar image change detection method. Background technique [0002] Remote sensing image change detection is based on a time-phase image as a reference, and detects the difference between other time-phase images relative to the reference image. It is widely used in many fields of national economy and national defense construction, for example, land use and Obtain information on land cover changes, forest and vegetation changes, wetland changes, and urban construction changes, crop growth monitoring, and natural disaster monitoring and assessment of earthquakes, floods, mudslides, deforestation, fires, etc. [0003] Compared with visible light and infrared remote sensing, microwave remote sensing imaging is not affected by time (day and night) and imaging conditions (clouds and fog), and has all-weather and all-day working capa...

AI Technical Summary

Problems solved by technology

Based on the SAR images of the two periods, the change areas can be detected. However, these detected change areas contain a lot of false change information, such as seasonal changes in farmland and vegetation growth. These random changes will greatly increase the false change information. alarm rate, reducing the change detection accuracy

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