Generative adversarial network-based remote sensing image thin cloud removal method

A remote sensing image and network technology, applied in image enhancement, image analysis, image data processing, etc., can solve problems such as weak generalization ability, poor performance, and inability to learn the real distribution characteristics of data well

Inactive Publication Date: 2018-08-28
BEIHANG UNIV
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Problems solved by technology

This discriminative model is a regression of existing data, and cannot learn the real distribution characteristics of the data we

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  • Generative adversarial network-based remote sensing image thin cloud removal method
  • Generative adversarial network-based remote sensing image thin cloud removal method
  • Generative adversarial network-based remote sensing image thin cloud removal method

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Embodiment Construction

[0065] In order to better understand the technical solution of the present invention, the embodiments of the present invention will be further described below in conjunction with the accompanying drawings:

[0066] The system schematic diagram of the present invention is as figure 1 As shown, the designed network structure is as follows Figure 2a , Figure 2b shown. The computer configuration adopts: Intel Core i7-4709k processor, Nvidia GeForce GTX1080Ti graphics processor, main frequency 4.0GHz, memory 16GB, operating system is ubuntu 14.04. The training of the network model is based on the Pytorch framework. The present invention is a method for removing thin clouds in remote sensing images based on generative confrontation networks, specifically comprising the following steps:

[0067] Step 1: Building a Thin Cloud Removal System Model

[0068] The invention adopts the remote sensing image collected by the Landsat-8 OLI land imager to remove the thin cloud. The OLI ...

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Abstract

The invention relates to a generative adversarial network-based remote sensing image thin cloud removal method. The method includes the following steps that: step 1, a thin cloud removal system modelis established; step 2, a network model is designed; step 3, an identification criterion function is constructed; step 4, the thin cloud removal system performs identification; and step 5, thin cloudsin a remote sensing image are removed. According to the method of the invention, the generative adversarial network is adopted to construct the model of an image thin cloud removal problem and perform system identification on the model, thereby realizing end-to-end thin cloud removal; and the constructed criterion function combines the errors of data distribution and reconstruction accuracy, so that the system can better learn the characteristics of data, thus, achieving the removal of the thin clouds. The method provided by the invention has a capacity to adaptively remove uneven thin clouds. With the method adopted, a restored image has high color and texture consistency.

Description

Technical field: [0001] The invention relates to a thin cloud removal method of a remote sensing image based on a generative confrontation network, and belongs to the application technical field of remote sensing image processing. Background technique: [0002] Remote sensing images provide rich information for earth science, meteorology, environmental monitoring, military monitoring and other tasks, and have become an increasingly important tool in modern measurement and control methods. However, there are large amounts of water vapor, ice crystals and fine dust condensation nuclei in the atmosphere, which gather in the form of clouds. However, electromagnetic waves are easily affected by clouds during the propagation process, causing the signal to be reflected or scattered, weakening the signal received by the sensor, affecting the imaging quality, resulting in the loss of information in the region of interest, and causing serious damage to the interpretation and interpret...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/001G06T2207/10032
Inventor 谢凤英张蕊姜志国
Owner BEIHANG UNIV
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