Cloud mask-oriented non-uniform category sample equalization method

A cloud mask, non-uniform technology, applied in the field of sample equalization of non-uniform categories, can solve problems such as sample imbalance, and achieve the effect of solving sample imbalance, improving accuracy, and enhancing small-category samples

Active Publication Date: 2020-06-16
ZHEJIANG UNIV OF TECH
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  • Abstract
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  • Application Information

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Problems solved by technology

[0008] Aiming at the problem of sample imbalance caused by non-uniform categories, the present invention proposes a sample equalization method, so as to ensure that the classifier can accurately identify different types of clouds

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  • Cloud mask-oriented non-uniform category sample equalization method
  • Cloud mask-oriented non-uniform category sample equalization method
  • Cloud mask-oriented non-uniform category sample equalization method

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Embodiment Construction

[0054] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention will be further described below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, and are not intended to limit the present invention, that is, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Therefore, based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0055] Assuming that an image set to be masked has been acquired, and some areas on the image are covered by clouds, resulting in missing data, the clouds in this area need to be masked. In this embodiment, a total of 24 scenes of Landsat 5TM images, 20 scenes...

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Abstract

The invention discloses a cloud mask-oriented non-uniform category sample equalization method. The method comprises the steps of 1, data preprocessing and sample acquisition; 2, sample grouping; 3, cloud mask model training; 4, classifier masking and evaluating; 5, iterative training; 6, cloud masking; and 7, mask data post-processing. According to the sample equalization method adopted by the invention, sample imbalance caused by uneven cloud categories in the remote sensing image is effectively solved, and effective identification and segmentation of various types of clouds in the image arerealized, so that the cloud mask precision is improved; the samples are selectively input, so that the influence of missing or wrong detection samples can be highlighted, the small-class samples are enhanced, the features extracted by the deep learning model are effectively adjusted, and the problem of missing detection or false detection of small-class clouds is solved.

Description

technical field [0001] The invention relates to a method for equalizing samples of non-uniform categories, in particular to a method for equalizing samples of non-uniform categories in the cloud mask training process based on deep learning. Background technique [0002] With the development of remote sensing technology, remote sensing images are widely used in crop mapping, forest monitoring and other fields. However, due to the influence of clouds in the imaging process of remote sensing images, there is data loss or distortion in the corresponding areas of the acquired images, which affects the accuracy of information extraction results and its application significance. The problem of data loss or distortion caused by clouds is particularly prominent in cloudy and rainy areas such as southern China. Using images partially obscured by clouds can improve data availability and enhance its support capabilities. In applying partially missing imagery, the cloud masking process,...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N20/00
CPCG06N20/00G06F18/241G06F18/214
Inventor 吴炜高星宇范菁沈瑛夏列钢葛炜炜
Owner ZHEJIANG UNIV OF TECH
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