In the field of remote sensing satellite imagery, malfunctions in the available raw data are prominent. Especially in Short-Wave Infrared (SWIR) detectors used in satellite imaging cameras, which suffer from dropouts in pixel and line direction in raw data. With the recent development in generative adversarial networks and its vast application in inpainting the missing data, the possibility to predict and fill in the missing data accurately with contextual attention has become prevalent. This paper presents SN-GANs (SN-generative adversarial networks) which is two-staged architecture, and it is based on the concept of feed forward neural networks with contextual attention layers. While reconstructing the corrupted part of the images, the model takes surrounding pixels into consideration. Moreover, this architecture is adept enough to fill in the multiple lines and pixel dropouts efficiently even in super-resolution satellite images. The available traditional methods fail to address the loss of data that incurs, while inpainting a 16-bit raw image because they are effective enough for 8-bit RGB images. SN-GANs have effectively resolved this issue with a lossless image inpainting method for 16-bit satellite images as it retains the features of non-corrupted data. The performance of the model is evaluated using similarity metrics like structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR) and mean-squared error (MSE). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.