SAR image change region detection method based on neighborhood ratio and self-stepping learning

Abstract

The invention discloses a synthetic aperture radar (SAR) image change region detection method based on neighborhood ratio and self-stepping learning, and mainly solves the problem that the prior art is sensitive to speckle noise of an SAR image, and the problem that part of texture information of the SAR image is lost easily. The method comprises the specific steps of (1) reading an SAR image; (2)normalizing; (3) calculating a neighborhood ratio difference value; (4) constructing a difference value matrix; (5) selecting a training sample set; (6) training a deep belief network; (7) constructing a probability matrix; (8) updating a probability matrix; (9) obtaining a change detection image. According to the method, the local information of an original image and the self-learning capabilityof a deep belief network are effectively utilized, and speckle noise is reduced, the partial image information is kept, and the 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 detection method of a synthetic aperture radar SAR (Synthetic Aperture Radar, SAR) image change region based on neighborhood ratio and self-paced learning in the technical field of remote sensing image change detection. The invention can be used to compare the neighborhood pixel information of two synthetic aperture radar SAR images in different periods in the same area to obtain a change difference map, and use a self-step learning algorithm to perform image segmentation on the difference map to obtain a 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 dete...

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 the error control in the dictionary learning is in the actual operation, which will easily cause the loss of part of the texture information of the image, and increase the false detection rate of the later change detection.

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