Many effective color correction algorithms have been proposed for single remote sensing optical image. However, these methods need prior knowledge or experience which is not feasible for automatic color correction of mass remote sensing images. In this work, a method based on dense convolutional networks named DCN (dense convolutional networks) is proposed for automatic color correction for remote sensing optical images. This model predicts the color correction parameter K for each RGB channel to correct the remote sensing optical images. In our experiment, the model is trained on 3 000 crops of GF-2 remote sensing images on the Tensorflow framework and the loss function is the angle between the predicted 3-channel K and the ground truth. Results show that the corrected image is in very good agreement with the ground truth and DCN outperforms the color correction method based on traditional CNN (convolutional neural networks). This method meets the demand of automatic color correction in large remote sensing datasets.
ZHU Sijie
,
LEI Bin
,
WU Yirong
. Automatic color correction for remote sensing optical image based on dense convolutional networks[J]. Journal of University of Chinese Academy of Sciences, 2019
, 36(1)
: 93
-100
.
DOI: 10.7523/j.issn.2095-6134.2019.01.013
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