SAR image change detection method based on SIFT feature point

Abstract

The invention discloses an SAR image change detection method based on SIFT feature points, and mainly aims to solve the problem that the conventional detection method is highly influenced by 'spot' noise. The SAR image change detection method comprises the following steps: (1) conducting normalization processing on two SAR images which are rectified and are subjected to radiation correction and geometric correction; (2) acquiring a difference image according to the two normalized images to be detected; (3) extracting SIFT feature points in the difference image by using a scale invariant feature transform method; (4) conducting region growing by taking the SIFT feature points as seed points, thereby obtaining a variation image. The method has the advantage of high 'spot' noise robustness and can be applied to SAR image variation detection under a low signal-to-noise ratio.

Description

technical field [0001] The invention belongs to the technical field of radar and relates to a SAR image detection method, which can be used to detect changing regions of the same scene in different time periods. Background technique [0002] Radar imaging technology was developed in the 1950s, and has been developed by leaps and bounds in the following 60 years. At present, it has been widely used in military, agriculture, forestry, geology, ocean, disaster, mapping and many other fields. [0003] As an active microwave sensor, SAR has the characteristics of all-weather, all-time, high resolution and strong penetrating ability. It has unique advantages in the detection and evaluation of emergencies and natural disasters. Using SAR images for change detection has great potential important meaning. At present, SAR image change detection has been widely used in many fields, such as land analysis, forest harvesting monitoring, disaster estimation, military reconnaissance, and s...

AI Technical Summary

Problems solved by technology

In the existing SAR image change detection methods, the characteristic "speckle" noise of SAR images has a great impact on the detection performance

For example, the detection method based on the statistical model must be filtered before detection, otherwise the detection cannot be carried out. In addition, once the model is mismatched, the detection performance of the method will drop sharply; another example is the detection method based on clustering. When strong, its detection performance will be greatly affected

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