![]() Jeong R, Son SB, Lee HJ, Kim H (2018) On the robustification of the z-test statistic. In: 2019 international conference on electrical, electronics and computer engineering (UPCON), pp 1–4. Springer, īaig MA, Moinuddin AA, Khan E (2019) PSNR of highest distortion region: an effective image quality assessment method. Ignatius DR, Setiadi M (2020) PSNR vs SSIM: imperceptibility quality assessment for image steganography. Sara U, Akter M, Uddin MS (2019) Image quality assessment through FSIM, SSIM, MSE and PSNR-a comparative study. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 701–710 Yuan Y, Liu S, Zhang J, Zhang Y, Dong C, Lin L (2018) Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 1520–1528 Noh H, Hong S, Han B (2015) Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3376–3385 Mostajabi M, Yadollahpour P, Shakhnarovich G (2015) Feedforward semantic segmentation with zoom-out features. Hou B, Liu Q, Wang H, Wang Y (2020) From W-Net to CDGAN: bitemporal change detection via deep learning techniques. In: International conference on medical image computing and computer-assisted intervention. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. Zhang Y, Yin Y, Zimmermann R, Wang G, Varadarajan J, Ng S-K (2020) An enhanced GAN model for automatic satellite-to-map image conversion. Zhang X, Han X, Li C, Tang X, Zhou H, Jiao L (2019) Aerial image road extraction based on an improved generative adversarial network. Shi Q, Liu X, Li X (2018) Road detection from remote sensing images by generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440 Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: IEEE international conference on computer vision (ICCV), pp 2223–2232 Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. IEEE Trans Pattern Anal Mach Intell 39(1):128–140 Pont-Tuset J, Arbelaez P, Barron JT, Marques F, Malik J (2017) Multiscale combinatorial grouping for image segmentation and object proposal generation. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 2849–2857Īndrade HJA, Fernandes BJT (2022) Synthesis of satellite-like urban images from historical maps using conditional GAN. Yi Z, Zhang H, Tan P, Gong M (2017) DualGAN: unsupervised dual learning for image-to-image translation. We have used two types of GANs for this process of conversion of satellite images to human-readable maps and compared the results by using various similarity metrics. A generative adversarial network that is GAN is a good approach for generating maps as it is automatic satellite-to-map image conversion. In recent years, satellite images have become more ubiquitous, and converting them to map-style images has attracted attention because it updates frequently and its cost-effective in nature this can be done by image-to-image translation is a general name for a task where an image from one domain is converted to a corresponding image in another domain, given sufficient training data. ![]() Conventional map generation involves labor-intensive methods as well as time-consuming manual efforts, which can restrict the updating frequency of maps to a few years or even longer. As a lot of applications like Navigation services significantly rely on up-to-date and accurate maps. ![]() Automatically generating maps from satellite images is an important task.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |