A SAR image change detection method based on complex neural network

Abstract

The invention discloses a SAR image change detection method based on a complex neural network, which inputs two original SAR gray-scale images and uses a preliminary difference map obtained by a traditional method as a preliminary label. According to the preliminary tags, some pixel blocks in the two original images are selected as candidate training samples by confidence detection. A complex network is constructed, which consists of three layers of all connection layers and two layers of multiple batches of normalized layers. The complex training samples are selected from the candidate training samples according to the stochastic proportional method to train the complex network. The training complex network is used to test the test complex samples constructed directly from two original SAR images, and the final change detection results are obtained. The invention not only makes full use of the advantages of the traditional change detection result, but also displays the characteristicsof the original data, so that the neural network can better learn the relationship between the two images, thereby obtaining a better change detection result.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a SAR image change detection method based on a complex neural network. Background technique [0002] Change detection is an important technique to detect regional surface changes by analyzing two images taken at the same location at different times. It has been widely used in land cover change, environmental monitoring and urban sprawl assessment. Change detection is therefore gaining more and more attention in the remote sensing community. Since SAR sensors are independent of sunlight, cloud cover and weather conditions, SAR images are an ideal source for change detection tasks. However, change detection in SAR images is often more difficult than in optical images due to the presence of speckle noise. [0003] With Hinton's layer-by-layer unsupervised pre-training method proposed in 2006, deep learning has gradually attracted people's attention. In the I...

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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a SAR image change detection method based on a complex neural network to solve the problem of low detection accuracy in SAR image change detection and the problem of two original images in change detection. Combination problem, constructing two image data as complex data to improve the quality of change detection in SAR images

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