Remote sensing image cloud detection method based on Gabor transformation and attention

Abstract

The invention provides a deep learning remote sensing cloud detection method based on Gabor transformation and an attention mechanism, and solves the problem of insufficient feature extraction in remote sensing image cloud detection. The method comprises the following implementation steps: establishing a remote sensing image database and a corresponding mask map; constructing a convolutional neural network comprising a Gabor transformation module and an attention module; determining a loss function of the network; inputting a training sample in the training image library into the convolutionalneural network, and iteratively updating the loss function through a gradient descent method until the loss function converges to obtain a trained convolutional neural network; and inputting the datain the test database into a convolutional neural network to obtain a detection result of the cloud area. According to the invention, the image feature extraction technology based on Gabor transformation and an attention mechanism is adopted, a deep learning method is used for cloud detection of the remote sensing image, feature extraction is sufficient, detection precision is high, and the methodis used for the preprocessing process of the remote sensing image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and mainly relates to cloud detection of remote sensing images, specifically a cloud detection method of remote sensing images based on Gabor transformation and attention mechanism, which can be used in the preprocessing process of remote sensing images to realize remote sensing image cloud detection Eliminate, classify. Background technique [0002] With the rapid development of science and technology, satellite remote sensing technology has entered a new stage that can quickly and timely obtain earth observation information. According to the global cloud cover data provided by the International Satellite Cloud Climate Project ISCCP (International Satellite Cloud Climatology Project), more than 60% of the world is often covered by clouds. Therefore, when remote sensing satellites acquire satellite images, especially when acquiring large-scale remote sensing images, there will be clouds...

AI Technical Summary

Problems solved by technology

[0004] Although the existing convolutional neural network can achieve the task of image semantic segmentation, due to the variety of cloud types, different types of clouds have different image eigenvalues, and the distribution of eigenvalues ​​is not concentrated. The types are also complicated, and there is no feature or combination of features that can clearly distinguish clouds from ground objects. The detection accuracy of the existing convolutional neural network for remote sensing image cloud detection still needs to be improved.

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