Depth learning and SIFT feature-based SAR image change detection method

Abstract

The invention discloses a depth learning and SIFT feature-based SAR image change detection method. The main objective of the invention is to solve the problem of low accuracy of change detection results which is caused by the sensitivity of a method in the prior art to speckle noises of an SAR image. The method of the invention includes the following steps that: (1) SAR images are read in; (2) normalization is carried out; (3) training features are constructed; (4) a deep neural network is trained; (5) logarithmic ratio operation is performed on the two read-in SAR images, so that a logarithmic ratio difference image is obtained; (6) the neighborhood feature sample matrix of the logarithmic ratio difference image is constructed; (7) the logarithmic ratio difference image is detected; and (8) a change detection result graph is outputted. According to the depth learning and SIFT feature-based SAR image change detection method of the invention, the stability of SIFT features to SAR image speckle noises is fully utilized, and therefore, the influence of the SAR image speckle noises can be eliminated, and the accuracy rate of SAR image change detection can be improved.

Description

technical field [0001] The present invention belongs to the technical field of image processing, and further relates to a synthetic aperture radar (SAR) based on deep learning and translation-invariant feature transformation SIFT (Scale-invariant Feature Transform, SIFT) in the field of remote sensing image change detection technology SAR) image change detection method. The invention can be used to detect the changing area of ​​the SAR image in the same area at different time periods. Background technique [0002] Radar imaging technology was developed in the 1950s, and it has achieved leapfrog development so far. At present, this technology has been widely used in military affairs, agriculture, ocean, geology, surveying and mapping, etc. [0003] As an active microwave sensor, synthetic aperture radar has the characteristics of high resolution, all-weather, all-weather work and strong penetration. Therefore, SAR is not affected by atmospheric conditions and cloud cover an...

AI Technical Summary

Problems solved by technology

The disadvantages of this method are, firstly, this method is sensitive to the speckle noise of the SAR image, resulting in low precision of the final change detection

The disadvantage of this method is that the method does not consider the influence of SAR image speckle noise during joint classification, resulting in insufficient reliability of the selected training sample points

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