An intelligent divisional green tide detection of adaptive threshold for GF-1 image based on data mining

Wang Rui; Wang Changying; Li Jinhua

doi:10.3969/j.issn.0253-4193.2019.04.012

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Wang Rui, Wang Changying, Li Jinhua. An intelligent divisional green tide detection of adaptive threshold for GF-1 image based on data mining[J]. Haiyang Xuebao, 2019, 41(4): 131-144. doi: 10.3969/j.issn.0253-4193.2019.04.012

Citation:

Wang Rui, Wang Changying, Li Jinhua. An intelligent divisional green tide detection of adaptive threshold for GF-1 image based on data mining[J]. Haiyang Xuebao, 2019, 41(4): 131-144. doi: 10.3969/j.issn.0253-4193.2019.04.012

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An intelligent divisional green tide detection of adaptive threshold for GF-1 image based on data mining

doi: 10.3969/j.issn.0253-4193.2019.04.012

School of Data Science and Software Engineering, Qingdao University, Qingdao 266071, China

Received Date: 2018-04-22
Rev Recd Date: 2018-09-19

Abstract

Abstract

Due to the influence of clouds, the visible light images that can be used effectively for green tide detection are limited, especially when the cloud coverage is serious, which can not be used to detect green tide. Even if the image data is acquired under thin cloud, mist, and cloudless coverage, it is still difficult to use the same threshold for green tide detection because of the large difference in spectral reflectance values. Based on this, in order to improve the utilization of visible light image and realize green tide high-precision automatic detection of the adaptive threshold under different cloud coverage conditions, GF-1 images are selected as data source, firstly, K-means clustering and C4.5 decision tree methods are used to automatically identify cloud coverage type; secondly, a large number of sub-image samples with different cloud coverage are selected (each sub-image sample contains two types of green tide and sea water), and the linear relationship between the classification threshold y and the image spectral difference x (x = band_nir-band_red) is analyzed under different cloud coverage, here, the classification threshold y is the value that can distinguish green tide and sea; then, green tide partition adaptive threshold automatic detection method for GF-1 image is proposed by using the linear relationship analyzed. Finally, in order to verify the effectiveness of the proposed method, NDVI、EVI methods and the adaptive threshold automatic detection method proposed in this paper are used to carry out the green tide extraction experiment. The experimental results show that for the GF-1 satellite remote sensing data, the green tide adaptive threshold partition automatic detection method is better than traditional NDVI and EVI methods, which not only improves the monitoring accuracy of green tide, but also realizes the full automation of green tide extraction.
- GF-1,
- green tide,
- K-means algorithm,
- C4.5 decision tree algorithm,
- adaptive threshold

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References

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