Automatic color correction for remote sensing optical image based on dense convolutional networks

doi:10.7523/j.issn.2095-6134.2019.01.013

Abstract

Abstract: 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.

Key words: remote sensing optical image, convolutional neural networks, color correction, automation

CLC Number:

TP751
TP183

ZHU Sijie, LEI Bin, WU Yirong. Automatic color correction for remote sensing optical image based on dense convolutional networks[J]. , 2019, 36(1): 93-100.

References

[1] Wu J, Wang D, Bauer M E. Image-based atmospheric correction of QuickBird imagery of Minnesota cropland[J]. Remote Sensing of Environment, 2005, 99(3):315-325.
[2] Fecker U, Barkowsky M, Kaup A. Histogram-based prefiltering for luminance and chrominance compensation of multiview video[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2008, 18(9):1258-1267.
[3] Kim S J, Pollefeys M. Robust radiometric calibration and vignetting correction[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2008, 30(4):562-576.
[4] Buchsbaum G. A spatial processor model for object colour perception[J]. Journal of the Franklin Institute, 1980, 310(1):1-26.
[5] Van d W J, Gevers T, Gijsenij A. Edge-based color constancy[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2007, 16(9):2207-2214.
[6] Hirakawa K, Chakrabarti A, Zickler T. Color constancy with spatio-spectral statistics[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2012, 34(8):1509-1519.
[7] Cheng D, Prasad D K, Brown M S. Illuminant estimation for color constancy:why spatial-domain methods work and the role of the color distribution[J]. Journal of the Optical Society of America A Optics Image Science & Vision, 2014, 31(5):1049.
[8] Perkins T, Adlergolden S M, Berk A, et al. Speed and accuracy improvements in FLAASH atmospheric correction of hyperspectral imagery[J]. Optical Engineering, 2012, 51(11):1707.
[9] Gao B C, Montes M J, Davis C O, et al. Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean[J]. Remote Sensing of Environment, 2009, 113(9):S17-S24.
[10] Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3):211-252.
[11] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems. Curran Associates Inc. 2012:1097-1105.
[12] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. CoRR, 2014.arXiv:1409.1556.
[13] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Computer Vision and Pattern Recognition. IEEE, 2016:770-778.
[14] Zhu X X, Tuia D, Mou L, et al. Deep learning in remote sensing:a comprehensive review and list of resources[J]. IEEE Geoscience & Remote Sensing Magazine, 2018, 5(4):8-36.
[15] Chen S, Wang H, Xu F, et al. Target classification using the deep convolutional networks for SAR images[J]. IEEE Transactions on Geoscience & Remote Sensing, 2016, 54(8):4806-4817.
[16] Pasolli E, Melgani F, Tuia D, et al. SVM active learning approach for image classification using spatial information[J]. IEEE Transactions on Geoscience & Remote Sensing, 2014, 52(4):2217-2233.
[17] Lary D J, Remer L A, Macneill D, et al. Machine learning and bias correction of MODIS aerosol optical depth[J]. IEEE Geoscience & Remote Sensing Letters, 2009, 6(4):694-698.
[18] Ali I, Greifeneder F, Stamenkovic J, et al. Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data[J]. Remote Sensing, 2015, 7(12):221-236.
[19] Gregor B, Adlergolden S M. Quick atmospheric correction code:algorithm description and recent upgrades[J]. Optical Engineering, 2012, 51(11):1719.
[20] Barron J T. Convolutional color constancy[C]//IEEE International Conference on Computer Vision. IEEE, 2016:379-387.
[21] Barron J T, Tsai Y T. Fast Fourier color constancy[C]//Computer Vision and Pattern Recognition. IEEE, 2017:6950-6958.
[22] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//Computer Vision and Pattern Recognition. IEEE, 2015:3431-3440.
[23] Huang G, Liu Z, Weinberger K Q, et al. Densely connected convolutional networks[C]//Computer Vision and Pattern Recognition. IEEE, 2017:2261-2269.