text: 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 compared to 89 for traditional GAN training.
  @cite_26: Road detection with high-precision from very high resolution remote sensing imagery is very important in a huge variety of applications. However, most existing approaches do not automatically extract the road with a smooth appearance and accurate boundaries. To address this problem, we proposed a novel end-to-end generative adversarial network. In particular, we construct a convolutional network based on adversarial training that could discriminate between segmentation maps coming either from the ground truth or generated by the segmentation model. The proposed method could improve the segmentation result by finding and correcting the difference between ground truth and result output by the segmentation model. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods greatly on the performance of segmentation map.
  @cite_14: Three-dimensional realistic representations of buildings in urban environments have been increasingly applied as data sources in a growing number of remote sensing fields such as urban planning and city management, navigation, environmental simulation (i.e. flood, earthquake, air pollution), 3D change detection after events like natural disasters or conflicts, etc. With recent technological developments, it becomes possible to acquire high-quality 3D input data. There are two main ways to obtain elevation information: from active remote sensing systems, such as light detection and ranging (LIDAR), and from passive remote sensing systems, such as optical images, which allow the acquisition of stereo images for automatic digital surface models (DSMs) generation. Although airborne laser scanning provides very accurate DSMs, it is a costly method. On the other hand, the DSMs from stereo satellite imagery show a large coverage and lower costs. However, they are not as accurate as LIDAR DSMs. With respect to automatic 3D information extraction, the availability of accurate and detailed DSMs is a crucial issue for automatic 3D building model reconstruction. We present a novel methodology for generating a better-quality stereo DSM with refined buildings shapes using a deep learning framework. To this end, a conditional generative adversarial network (cGAN) is trained to generate accurate LIDAR DSM-like height images from noisy stereo DSMs.
  @cite_32: The integration of information acquired with different modalities, spatial resolution and spectral bands has shown to improve predictive accuracies. Data fusion is therefore one of the key challenges in remote sensing. Most prior work focusing on multi-modal fusion, assumes that modalities are always available during inference. This assumption limits the applications of multi-modal models since in practice the data collection process is likely to generate data with missing, incomplete or corrupted modalities. In this paper, we show that Generative Adversarial Networks can be effectively used to overcome the problems that arise when modalities are missing or incomplete. Focusing on semantic segmentation of building footprints with missing modalities, our approach achieves an improvement of about 2 on the Intersection over Union (IoU) against the same network that relies only on the available modality.
  @cite_3: 
  @cite_19: 
  @cite_27: Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols. Manually annotating new data for each test domain is not a feasible solution. In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more robust to differences in the input data, and which does not require any annotations on the test domain. Specifically, we derive domain-invariant features by learning to counter an adversarial network, which attempts to classify the domain of the input data by observing the activations of the segmentation network. Furthermore, we propose a multi-connected domain discriminator for improved adversarial training. Our system is evaluated using two MR databases of subjects with traumatic brain injuries, acquired using different scanners and imaging protocols. Using our unsupervised approach, we obtain segmentation accuracies which are close to the upper bound of supervised domain adaptation.
  @cite_5: Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic segmentation models. We train a convolutional semantic segmentation network along with an adversarial network that discriminates segmentation maps coming either from the ground truth or from the segmentation network. The motivation for our approach is that it can detect and correct higher-order inconsistencies between ground truth segmentation maps and the ones produced by the segmentation net. Our experiments show that our adversarial training approach leads to improved accuracy on the Stanford Background and PASCAL VOC 2012 datasets.
summary: Adversarial training has been demonstrated to improve the labeling accuracy of semantic segmentation models @cite_5 . One study compares the performance of U-Net and adversarial-trained U-Net for semantic segmentation of roads and finds that adversarial training reduces over fitting and improves validation accuracy. @cite_26 . Much work has been done applying adversarial semantic segmentation in medical imaging as well as style transfer @cite_27 @cite_19 @cite_3 . One study focusing on rooftop segmentation uses GANs to overcome missing data, while another uses conditional GANs to refine 3D building shapes @cite_32 @cite_14 . In our study, we build upon progress in semantic segmentation by applying and evaluating progressive training.