Remote sensing image thin cloud removal method based on multi-scale collaborative learning convolutional neural network

A convolutional neural network and remote sensing image technology, applied in the field of thin cloud removal in remote sensing images, can solve problems such as information accuracy loss and image quality degradation, and achieve the effect of eliminating color cast, good effect, and maintaining fidelity.

Active Publication Date: 2018-11-30
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to design a high-performance, high-precision thin cloud removal algorithm to obtain clean Cloud-free clear images, improving the utilization of remote sensing images

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  • Remote sensing image thin cloud removal method based on multi-scale collaborative learning convolutional neural network
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  • Remote sensing image thin cloud removal method based on multi-scale collaborative learning convolutional neural network

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Embodiment Construction

[0027] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0028] Step 1: Obtain experimental data. The experimental data used in the present invention are divided into actual data group and simulated data group. The actual data set is two images collected by the remote sensing satellite Landsat8 within the shortest time interval (that is, a revisit period), which belong to the multi-temporal experimental data at different time points in the same area, one with clouds, one with no clouds, and one with no clouds. They are called the actual cloudy image and the actual cloudless image, respectively. Due to the short collection time interval and relatively small changes in surface characteristics, there is no significant difference. This set of multi-temporal data can be used for later training of the network structure. In addition, in order to obtain the simulated data set at the same time at the same place to ensure that...

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Abstract

The invention relates to a remote sensing image thin cloud removal method based on a multi-scale collaborative learning convolutional neural network. According to the method, training data is input into network structures with different scales after being subjected to scale transformation; features under different scales are extracted level by level from coarse granularity to fine granularity sequentially for fusion, so that coarse-to-fine multi-scale learning is realized; and last, a mapping relation between cloudy data and cloudless data is obtained, cloud components are effectively removed,image details are restored, and the purpose of thin cloud removal is achieved. An experiment result indicates that compared with a traditional thin cloud removal method, the method can eliminate a manual trace brought by the traditional method, information of a cloudy region in an image can be accurately restored, the fidelity of a cloudless region can be maintained, the thin cloud removal effectis better, and thin cloud removal precision is higher.

Description

technical field [0001] The invention relates to a thin cloud removal method of a remote sensing image based on a multi-scale collaborative learning convolutional neural network, which belongs to the field of image processing. Background technique [0002] Due to the scattering and absorption of the atmosphere in the radiation transmission process of the remote sensing earth observation system, the observation images obtained by remote sensing satellites not only contain relevant ground object information, but also contain information about clouds in the atmosphere. Due to the spatial uncertainty and time variability of clouds, the imaging quality and information accuracy of remote sensing satellite images are reduced, the image features are difficult to identify, and the visual effect is poor, which seriously affects its later use. Among them, clouds can be divided into thick clouds and thin clouds. Due to the impenetrability of solar radiation, the ground object informatio...

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Application Information

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IPC IPC(8): G06T5/00G06T5/50G06T7/30G06N3/04G06N3/08
CPCG06N3/084G06T5/50G06T7/30G06T2207/10032G06T2207/20081G06T2207/20084G06N3/045G06T5/73
Inventor 李映陈迪李文博
Owner NORTHWESTERN POLYTECHNICAL UNIV
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