Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images

02/12/2019

∙

Machine learning has proven to be useful in classification and segmentation of images. In this paper, we evaluate a training methodology for pixel-wise segmentation on high resolution satellite images using progressive growing of generative adversarial networks. We apply our model to segmenting building rooftops and compare these results to conventional methods for rooftop segmentation. We present our findings using the SpaceNet version 2 dataset. Progressive GAN training achieved a test accuracy of 93 traditional GAN training.

READ FULL TEXT

Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images

Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks

Semantic Segmentation of Medium-Resolution Satellite Imagery using Conditional Generative Adversarial Networks

Conditional Progressive Generative Adversarial Network for satellite image generation

Object Segmentation using Pixel-wise Adversarial Loss

Galaxy Image Simulation Using Progressive GANs

Deblending galaxy superpositions with branched generative adversarial networks

TCR-GAN: Predicting tropical cyclone passive microwave rainfall using infrared imagery via generative adversarial networks

Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images

Related Research

Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks

Semantic Segmentation of Medium-Resolution Satellite Imagery using Conditional Generative Adversarial Networks

Conditional Progressive Generative Adversarial Network for satellite image generation

Object Segmentation using Pixel-wise Adversarial Loss

Galaxy Image Simulation Using Progressive GANs

Deblending galaxy superpositions with branched generative adversarial networks

TCR-GAN: Predicting tropical cyclone passive microwave rainfall using infrared imagery via generative adversarial networks