Nighttime sea fog recognition based on remote sensing satellite and deep neural decision tree

Tao Li; Wei Jin; Randi Fu; Gang Li; Caoqian Yin

doi:10.12086/oee.2022.220007

Journals >Opto-Electronic Engineering >Volume 49 >Issue 9 >Page 220007 > Article

Opto-Electronic Engineering
Vol. 49, Issue 9, 220007 (2022)

Nighttime sea fog recognition based on remote sensing satellite and deep neural decision tree

Tao Li, Wei Jin^*, Randi Fu, Gang Li, and Caoqian Yin

Author Affiliations

Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang 315211, China

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DOI: 10.12086/oee.2022.220007 Cite this Article

Tao Li, Wei Jin, Randi Fu, Gang Li, Caoqian Yin. Nighttime sea fog recognition based on remote sensing satellite and deep neural decision tree[J]. Opto-Electronic Engineering, 2022, 49(9): 220007 Copy Citation Text

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Fig. 1. Overall algorithm flow chart

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Fig. 2. An example of the model inference process

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Fig. 3. Sea fog identification result at UTC 18:20 on June 5, 2020 in the Yellow Sea and Bohai Sea

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Fig. 4. The monitoring results of sea fog in the Yellow Sea and Bohai Sea from 15:00 to 20:00 UTC on June 5, 2020

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Method		Middle/high clouds	Stratus	Sea fog	Sea surface	Average
	POD/(%)	89.30	76.71	82.12	92.15	85.07
Three-groups	FAR/(%)	11.07	20.37	21.13	7.42	15.00
	CSI/(%)	80.36	64.13	67.31	85.81	74.40
	POD/(%)	89.09	80.87	86.47	92.84	87.32
Four- groups	FAR/(%)	7.06	18.64	19.14	7.90	13.19
	CSI/(%)	83.44	68.23	71.78	85.99	77.36
	POD/(%)	90.40	78.59	82.00	90.65	85.41
Five- groups	FAR/(%)	11.07	19.82	19.24	7.54	14.42
	CSI/(%)	81.25	65.81	68.60	84.41	75.02
	POD/(%)	91.99	77.60	80.24	91.34	85.29
Six- groups	FAR/(%)	11.67	20.43	17.83	7.05	14.24
	CSI/(%)	82.02	64.71	68.34	85.42	75.12

Table 1. Experimental results of different network layers

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Method		Middle/high clouds	Stratus	Sea fog	Sea surface	Average
	POD/(%)	87.36	76.11	81.76	88.45	83.42
CNN_1D	FAR/(%)	11.60	25.07	23.79	4.84	16.33
	CSI/(%)	78.38	60.66	65.14	84.64	72.20
	POD/(%)	89.09	80.87	86.47	92.84	87.32
CNN_2D	FAR/(%)	7.06	18.64	19.14	7.90	13.19
	CSI/(%)	83.44	68.23	71.78	85.99	77.36

Table 2. Comparison of results of different convolution networks

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Method		Middle/high clouds	Stratus	Sea fog	Sea surface	Average
	POD/(%)	90.40	67.59	73.65	93.30	81.24
ATF	FAR/(%)	11.55	24.72	21.55	18.30	19.03
	CSI/(%)	80.85	55.31	61.25	77.17	68.65
	POD/(%)	89.44	81.96	80.47	89.49	85.34
ATL	FAR/(%)	8.80	20.40	21.92	7.52	14.66
	CSI/(%)	82.34	67.73	65.64	83.42	74.78
	POD/(%)	89.09	80.87	86.47	92.84	87.32
WOA	FAR/(%)	7.06	18.64	19.14	7.90	13.19
	CSI/(%)	83.44	68.23	71.78	85.99	77.36

Table 3. Comparison of ablation results

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True label	Middle/high clouds	Stratus	Sea fog	Sea surface
Middle/high clouds	1290	85	58	15
Stratus	56	816	98	39
Sea fog	36	64	735	15
Sea surface	6	38	18	804

Table 4. Confusion matrix of model

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Method		Middle/high clouds	Stratus	Sea fog	Sea surface	Average
	POD/(%)	85.28	81.71	58.78	91.78	79.39
SVM	FAR/(%)	19.12	21.56	31.02	9.05	20.19
	CSI/(%)	70.97	66.72	46.49	84.10	67.07
	POD/(%)	81.42	64.22	62.71	82.10	72.61
DT	FAR/(%)	19.41	35.01	36.85	18.18	27.36
	CSI/(%)	68.07	47.72	45.91	69.43	57.78
	POD/(%)	89.99	81.17	85.41	93.76	87.58
ResNet	FAR/(%)	8.37	17.94	18.24	6.13	12.67
	CSI/(%)	83.15	68.94	71.74	88.36	78.05
	POD/(%)	89.09	80.87	86.47	92.84	87.32
Ours	FAR/(%)	7.06	18.64	19.14	7.90	13.19
	CSI/(%)	83.44	68.23	71.78	85.99	77.36

Table 5. Classification accuracy of different sea fog recognition methods

Tao Li, Wei Jin, Randi Fu, Gang Li, Caoqian Yin. Nighttime sea fog recognition based on remote sensing satellite and deep neural decision tree[J]. Opto-Electronic Engineering, 2022, 49(9): 220007

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