SAR (synthetic aperture radar) image change area detection method based on self-paced learning

Abstract

The invention discloses a synthetic-aperture-radar (SAR) image change area detection method of self-paced learning. The method mainly solves the problem that in the prior art, sensitivity to speckle noises of synthetic-aperture-radar (SAR) images is prone to cause a loss of part of texture information of the synthetic-aperture-radar (SAR) images, and includes the following specific steps: (1) reading in the synthetic-aperture-radar (SAR) images; (2) carrying out normalization; (3) constructing a change detection matrix; (4) selecting a training sample set; (5) training a deep belief network; (6) constructing a probability matrix; (7) updating the probability matrix; and (8) obtaining a change detection image. According to the method, local information of the original images and self-learning ability of the deep belief network are effectively utilized, the speckle noises are reduced, the local information of the images is retained, and precision of change detection is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a synthetic aperture radar (Synthetic Aperture Radar, SAR) image change detection method based on self-paced learning in the technical field of remote sensing image change detection. The invention can be used to extract neighborhood pixel information of two synthetic aperture radar SAR images in different time periods in the same area, and use a self-step learning algorithm to learn the extracted pixel information to obtain a final change detection map. Background technique [0002] As an active microwave sensor, synthetic aperture radar has the characteristics of high resolution, all-weather, all-weather work and strong penetrating power, which makes synthetic aperture radar SAR have incomparable advantages over optical remote sensing images. Synthetic aperture radar SAR image change detection technology is to study the regional changes of two or more synthetic ap...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the algorithm contains artificial parameters, which requires multiple tests to obtain the optimal parameter value, and it is not easy to automatically select according to the nature of the image itself.

The disadvantage of this method is that this method does not consider the influence of the speckle noise of the SAR image during the joint classification, and it is easy to cause the loss of part of the texture information of the image.

Images