Satellite Image Translation Method Based on Attention Residual Network

Type	Procedure	Name	Number	Input size	Output size	Normalization	Activation function	Padding
Encoder	1	Conv	1	3 $\times$ 256 $\times$ 256	64 $\times$ 256 $\times$ 256	In	ReLU	Yes
	2	Conv	1	64 $\times$ 256 $\times$ 256	128 $\times$ 128 $\times$ 128	In	ReLU	No
	3	Conv	1	128 $\times$ 128 $\times$ 128	256 $\times$ 64 $\times$ 64	In	ReLU	No
Converter	4	SeparConv	9	256 $\times$ 64 $\times$ 64	256 $\times$ 64 $\times$ 64	In	ReLU	Yes
Converter	5	SERestnet	9	256 $\times$ 64 $\times$ 64	256 $\times$ 64 $\times$ 64	In	ReLU	No
Decoder	6	DeConv	1	256 $\times$ 64 $\times$ 64	128 $\times$ 128 $\times$ 128	In	ReLU	No
	7	DeConv	1	128 $\times$ 128 $\times$ 128	64 $\times$ 256 $\times$ 256	In	ReLU	No
	8	DeConv	1	64 $\times$ 256 $\times$ 256	3 $\times$ 256 $\times$ 256	In	ReLU	Yes

Table 2. Content of dataset

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Table 2. Content of dataset

Type of translation	Dataset	Terrestrial	DataA	DataB	Data source	Size	Data volume（A）	Data volume（B）
SemanticTranslation	1	City	Maps	Images	CycleGAN^［12］	256	1098	1096
Style Translation	2	Factory	Images	Images	RSI-CB	256	2511	2445
	3	Vegetation	Images	SAR	RSI-CB、GF-3	256	2358	848
	4	Farmland	Images	SAR	RSI-CB、GF-3	256	2491	656

Table 3. Comparison of indicators

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Table 3. Comparison of indicators

method	Parameter /MB	Model size /MB	PSNR		SSIM		FID
method	Parameter /MB	Model size /MB	Map→image	Image→map	Map→imag	Image→map	Map→imag	Image→map
CycleGAN	13.7	43.4	14.02	24.98	0.18	0.6	190.36	257.59
Pixpix	54.4	207	14.96	17.09	0.21	0.51	233.69	322.38
Proposed method	4.8	25.8	14.67	26.68	0.22	0.67	178.44	197.56

Table 4. Objective evaluation of ablation experiments

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Table 4. Objective evaluation of ablation experiments

Algorithm	Parameter /MB	Model size /MB	FID（map→image）	FID（image→map）
CycleGAN	13.7	43.4	190.36	257.59
CycleGAN+A	13.9↑	43.7↑	181.68↓	208.89↓
CycleGAN+B	4.8↓	25.5↓	180.96↓	200.61↓
CycleGAN+C	13.7	43.4	181.74↓	199.74↓
SA-CycleGAN	4.8↓	25.8↓	178.44↓	197.56↓

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Jinyu Wang, Changgong Zhang, Haitao Yang, Bodi Feng, Gaoyuan Li, Yuge Gao. Satellite Image Translation Method Based on Attention Residual Network[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0210017

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Paper Information

Category: Image Processing

Received: Jul. 29, 2021

Accepted: Sep. 2, 2021

Published Online: Dec. 23, 2021

The Author Email: Haitao Yang (767153436@qq.com)

DOI:10.3788/LOP202259.0210017

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology