Remote sensing image change detection method based on spatial-spectral feature fusion network

Abstract

The invention discloses a remote sensing image change detection method based on a spatial-spectral feature fusion network. The method comprises the following steps: firstly, carrying out preprocessing operations of geometric correction and image registration on a remote sensing image; then inputting the training set into the DESSN network for training; and finally, inputting a test image into the DESSN network model, and outputting a segmentation result of dual-temporal remote sensing image change detection. According to the method, an asymmetric double-convolution module combined with Ghost is used for replacing an original double-convolution module in a U-Net network to enhance the feature learning capability and reduce the parameter quantity, and a difference enhancement module used for suppressing irrelevant changes caused by noise is added behind a feature extraction layer to enhance the attention on a changing target; and finally, a non-local space spectrum information fusion module is designed in a feature fusion stage for enhancing boundary integrity and internal compactness of a change object, high-precision change detection of the remote sensing image is finally realized, the change detection level of the remote sensing image can be effectively improved, and memory consumption is reduced.

Description

technical field [0001] The invention belongs to the field of image processing technology and pattern recognition technology, and relates to theoretical knowledge in the field of deep learning and image segmentation. The invention specifically relates to a remote sensing image change detection method based on a spatial-spectrum feature fusion network. Background technique [0002] Remote sensing image change detection is the process of identifying differences in images in different periods of a certain region. It is an important branch of remote sensing image analysis and is widely used in urban expansion, land exploration, disaster assessment, environmental monitoring and other fields. With the continuous development of optical sensor equipment, it has become more convenient to obtain remote sensing images. The resolution of the obtained remote sensing images has been continuously improved, providing more and more abundant surface information, and breaking through the problem...

AI Technical Summary

Problems solved by technology

First, existing methods cannot effectively construct the relationship between bitemporal images, resulting in unfavorable effects of irrelevant changes on detection results

Second, the boundary integrity and internal compactness of changing objects are not fully considered, resulting in the loss of edge information in the predicted changing graph

Third, the existing dual-branch network expands the scale of the model, increases the computational cost, and easily causes the problem of overfitting

First, existing methods do not sufficiently effectively construct the relationship between bitemporal images, leading to unfavorable effects of irrelevant changes on detection results

Second, the boundary integrity and internal compactness of changing objects are not fully considered, resulting in the loss of edge information in the predicted changing graph

Third, the existing dual-branch network expands the scale of the model, increases the computational cost, and easily causes the problem of overfitting

Images