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Abstract
In recent years, image processing methods based on convolutional neural networks (CNNs) have achieved very good results. At the same time, many branch techniques have been proposed to improve accuracy. Aiming at the change detection task of remote sensing images, we propose a new network based on U-Net in this paper. The attention mechanism is cleverly applied in the change detection task, and the data-dependent upsampling (DUpsampling) method is used at the same time, so that the network shows improvement in accuracy, and the calculation amount is greatly reduced. The experimental results show that, in the two-phase images of Yinchuan City, the proposed network has a better antinoise ability and can avoid false detection to a certain extent.
Highlights
Change detection in remote sensing images is a critical and challenging task, and its specific work refers to the quantitative analysis of multiple temporal remote sensing images for the same target area, determining the features and scope of surface changes and detecting the changed and unchanged parts [1]
We propose a new network based on U-Net. e network consists of encoder and decoder
Different from the current mainstream of change detection method based on the convolution neural network, we improve the residual attention mechanism, and we proposed a new way to generate attention mask and apply the mask to change detection task
Summary
Change detection in remote sensing images is a critical and challenging task, and its specific work refers to the quantitative analysis of multiple temporal remote sensing images for the same target area, determining the features and scope of surface changes and detecting the changed and unchanged parts [1]. Because of the increasing amount of data from remote sensing images and the increasing demand in this direction, manual comparison and analysis of the change area appear time-consuming and laborious. Due to factors such as seasons and solar illumination, imaging styles of different phases have huge differences [3], which make it difficult to solve the change detection task by computer vision.
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