A method for color consistency of remote sensing images based on generative adversarial networks

WANG Yiru; WANG Guanghui; YANG Huachao; LIU Huijie

doi:10.6046/zrzyyg.2021316

2022 Vol. 34, No. 3

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WANG Yiru, WANG Guanghui, YANG Huachao, LIU Huijie. 2022. A method for color consistency of remote sensing images based on generative adversarial networks. Remote Sensing for Natural Resources, 34(3): 65-72. doi: 10.6046/zrzyyg.2021316

Citation:

WANG Yiru, WANG Guanghui, YANG Huachao, LIU Huijie. 2022. A method for color consistency of remote sensing images based on generative adversarial networks. Remote Sensing for Natural Resources, 34(3): 65-72. doi: 10.6046/zrzyyg.2021316

A method for color consistency of remote sensing images based on generative adversarial networks

1. School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China；
2. Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China

Received Date: 27 September 2021

Revised Date: 15 September 2022

Publish Date: 21 September 2022

Abstract

Abstract

Uneven brightness and inconsistent colors are prone to occur inside and between captured images in the process of remote sensing imaging. However, the manual color conditioning combined with image processing software can no longer meet the color matching demand of geometrically increasing remote sensing images. Given this, this study proposed a kind of unsupervised channel-cycle generative adversarial network (CA-CycleGAN) integrated with the attention mechanism suitable for ground objects in complex urban areas with a high land utilization rate. Firstly, the sample data set used for color reference was manually made through histogram adjustment and Photoshop, and the appropriate urban images were selected as the sample set to be corrected. Then, the two kinds of images were cut respectively to obtain the preprocessed image sample sets. Finally, the preprocessed image set to be corrected and the image set for color reference were processed using the CA-CycleGAN. Because the attention mechanism has been added to the generator, the generated focuses can be distributed into key areas using the attention feature map in the training process of the confrontation between the generator and the discriminator, thus improving the image effects and obtaining the color correction model based on urban images and the images after color correction. Both the image correction effect and the loss function diagram show that the proposed method is optimized based on the CycleGAN and that the comprehensive performance of the CycleGAN integrated with the attention mechanism is better than that without the attention mechanism. Compared to conventional methods, the method proposed in this study greatly reduced the time for color correction and achieved more stable image color correction effects than manual color matching. Therefore, the method proposed in this study enjoys significant advantages in the color dodging of remote sensing images and has a good application prospect.
- remote sensing /
- image color correction /
- CycleGAN /
- urban satellite image /
- attention mechanism

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References

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