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Improved M-Net-based RGB color remote sensing image cloud detection method and system

A remote sensing image and cloud detection technology, applied in the field of deep learning and image recognition, can solve the problems of poor versatility and low accuracy of cloud detection, and achieve easy training, promotion of forward propagation and back propagation, good generalization and The effect of robustness

Pending Publication Date: 2019-06-25
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

[0004] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a method for cloud detection of RGB color remote sensing images based on improved M-Net, which can solve the problems of low cloud detection accuracy and poor versatility in the prior art. The invention also provides a RGB color remote sensing image cloud detection system based on the improved M-Net

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  • Improved M-Net-based RGB color remote sensing image cloud detection method and system

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Embodiment 1

[0039] The present invention provides a kind of RGB color remote sensing image cloud detection method based on improved M-Net, comprising:

[0040] Training stage: step 1, preprocessing the image;

[0041] Because the training data set is small and the size is too large, considering the limitation of GPU memory, calculation speed and ensuring the timeliness of the segmentation method, the present invention enhances the training data set, mainly through flipping, saturation adjustment, Operations such as brightness adjustment, color adjustment and noise addition;

[0042] Considering the computer memory and calculation speed, the picture is cropped to 256×256 pixels. Calculate the mean value of each image to be detected in the training set in the three dimensions of RGB, and subtract the mean value, which can improve the speed and accuracy of training;

[0043] In the process of making labels, whether each pixel is "cloud", "cloud shadow" or "non-cloud" is represented by 2, 1...

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Abstract

The invention discloses an improved M-Net-based RGB color remote sensing image cloud detection method and system, and belongs to the field of artificial intelligence and image recognition, RM-Net deepsemantic segmentation network is designed combining advantages of a residual error network and M-Net. The method comprises the following steps: firstly, enhancing an original data set, and labeling acorresponding pixel-level tag; multi-scale features of the image are extracted on the premise that information is not lost through pooling of the hollow space pyramid, and the network is not prone todegeneration by combining with a residual unit; and finally, extracting global context information of the image by using an encoder module and a left path, recovering the spatial resolution of the image by using a decoder module and a right path, judging the category probability of each pixel according to the fused characteristics, and inputting the category probability into a classifier for pixel-level cloud and non-cloud segmentation. According to the method, the color image is trained and tested, experimental results show that the cloud edge details can be well detected under different conditions, high-precision cloud shadow detection is obtained, and it is proved that the method has good generalization and robustness.

Description

technical field [0001] The invention relates to the fields of deep learning and image recognition, in particular to a method and system for detecting cloud of RGB color remote sensing images based on improved M-Net. Background technique [0002] With the development of remote sensing technology, remote sensing images have been widely used in meteorological detection, resource utilization and environmental detection and other work fields. The global cloud coverage area accounts for about 68% of the earth's land surface, and cloud detection is also an important part of remote sensing data processing. Correctly separating cloudy pixels and cloudless pixels in remote sensing images has become an important basic work for weather forecasting and natural disaster prediction. Therefore, effective cloud detection is a prerequisite for subsequent analysis and utilization of remote sensing images. [0003] At present, a variety of cloud detection methods have been proposed, including...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCY02A90/10
Inventor 张秀再胡敬锋沈嘉程刘思成蒋闯
Owner NANJING UNIV OF INFORMATION SCI & TECH
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