Conditional GANs for semantic segmentation of multispectral satellite images

Vladimir V. Kniaz

doi:10.1117/12.2325601

9 October 2018 Conditional GANs for semantic segmentation of multispectral satellite images

Vladimir V. Kniaz

Author Affiliations +

Proceedings Volume 10789, Image and Signal Processing for Remote Sensing XXIV; 107890R (2018) https://doi.org/10.1117/12.2325601
Event: SPIE Remote Sensing, 2018, Berlin, Germany

Abstract

Algorithms for automatic semantic segmentation of the satellite images provide an effective approach for the generation of vector maps. Convolutional neural networks (CNN) have achieved the state-of-the-art quality of the output segmentation on the satellite images-to-semantic labels task. However, the generalization ability of such methods is not sufficient to process the satellite images that were captured in the different area or during the different season. Recently, the Generative Adversarial Networks (GAN) were introduced that can overcome the overfitting using the adversarial loss. This paper is focused on the development of the new GAN model for effective semantic segmentation of multispectral satellite images. The pix2pix¹ model is used as the starting point of the research. It is trained in the semi-supervised setting on the aligned pairs of images. The perceptual validation has demonstrated the high quality of the output labels. The evaluation on the independent test dataset has proved the robustness of GANs on the task of semantic segmentation of multispectral satellite images.

Conference Presentation

Citation Download Citation

Vladimir V. Kniaz "Conditional GANs for semantic segmentation of multispectral satellite images", Proc. SPIE 10789, Image and Signal Processing for Remote Sensing XXIV, 107890R (9 October 2018); https://doi.org/10.1117/12.2325601

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