728 research outputs found
DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images
The domain adaptation of satellite images has recently gained an increasing
attention to overcome the limited generalization abilities of machine learning
models when segmenting large-scale satellite images. Most of the existing
approaches seek for adapting the model from one domain to another. However,
such single-source and single-target setting prevents the methods from being
scalable solutions, since nowadays multiple source and target domains having
different data distributions are usually available. Besides, the continuous
proliferation of satellite images necessitates the classifiers to adapt to
continuously increasing data. We propose a novel approach, coined DAugNet, for
unsupervised, multi-source, multi-target, and life-long domain adaptation of
satellite images. It consists of a classifier and a data augmentor. The data
augmentor, which is a shallow network, is able to perform style transfer
between multiple satellite images in an unsupervised manner, even when new data
are added over the time. In each training iteration, it provides the classifier
with diversified data, which makes the classifier robust to large data
distribution difference between the domains. Our extensive experiments prove
that DAugNet significantly better generalizes to new geographic locations than
the existing approaches
Data-Efficient Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Generative Adversarial Networks
Despite the significant advances noted in semantic segmentation of aerial imagery, a considerable limitation is blocking its adoption in real cases. If we test a segmentation model on a new area that is not included in its initial training set, accuracy will decrease remarkably. This is caused by the domain shift between the new targeted domain and the source domain used to train the model. In this paper, we addressed this challenge and proposed a new algorithm that uses Generative Adversarial Networks (GAN) architecture to minimize the domain shift and increase the ability of the model to work on new targeted domains. The proposed GAN architecture contains two GAN networks. The first GAN network converts the chosen image from the target domain into a semantic label. The second GAN network converts this generated semantic label into an image that belongs to the source domain but conserves the semantic map of the target image. This resulting image will be used by the semantic segmentation model to generate a better semantic label of the first chosen image. Our algorithm is tested on the ISPRS semantic segmentation dataset and improved the global accuracy by a margin up to 24% when passing from Potsdam domain to Vaihingen domain. This margin can be increased by addition of other labeled data from the target domain. To minimize the cost of supervision in the translation process, we proposed a methodology to use these labeled data efficiently.info:eu-repo/semantics/publishedVersio
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images
Semantic segmentation models based on convolutional neural networks (CNNs)
have gained much attention in relation to remote sensing and have achieved
remarkable performance for the extraction of buildings from high-resolution
aerial images. However, the issue of limited generalization for unseen images
remains. When there is a domain gap between the training and test datasets,
CNN-based segmentation models trained by a training dataset fail to segment
buildings for the test dataset. In this paper, we propose segmentation networks
based on a domain adaptive transfer attack (DATA) scheme for building
extraction from aerial images. The proposed system combines the domain transfer
and adversarial attack concepts. Based on the DATA scheme, the distribution of
the input images can be shifted to that of the target images while turning
images into adversarial examples against a target network. Defending
adversarial examples adapted to the target domain can overcome the performance
degradation due to the domain gap and increase the robustness of the
segmentation model. Cross-dataset experiments and the ablation study are
conducted for the three different datasets: the Inria aerial image labeling
dataset, the Massachusetts building dataset, and the WHU East Asia dataset.
Compared to the performance of the segmentation network without the DATA
scheme, the proposed method shows improvements in the overall IoU. Moreover, it
is verified that the proposed method outperforms even when compared to feature
adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure
SRDA-Net: Super-Resolution Domain Adaptation Networks for Semantic Segmentation
Recently, Unsupervised Domain Adaptation was proposed to address the domain
shift problem in semantic segmentation task, but it may perform poor when
source and target domains belong to different resolutions. In this work, we
design a novel end-to-end semantic segmentation network, Super-Resolution
Domain Adaptation Network (SRDA-Net), which could simultaneously complete
super-resolution and domain adaptation. Such characteristics exactly meet the
requirement of semantic segmentation for remote sensing images which usually
involve various resolutions. Generally, SRDA-Net includes three deep neural
networks: a Super-Resolution and Segmentation (SRS) model focuses on recovering
high-resolution image and predicting segmentation map; a pixel-level domain
classifier (PDC) tries to distinguish the images from which domains; and
output-space domain classifier (ODC) discriminates pixel label distributions
from which domains. PDC and ODC are considered as the discriminators, and SRS
is treated as the generator. By the adversarial learning, SRS tries to align
the source with target domains on pixel-level visual appearance and
output-space. Experiments are conducted on the two remote sensing datasets with
different resolutions. SRDA-Net performs favorably against the state-of-the-art
methods in terms of accuracy and visual quality. Code and models are available
at https://github.com/tangzhenjie/SRDA-Net
Adversarially Tuned Scene Generation
Generalization performance of trained computer vision systems that use
computer graphics (CG) generated data is not yet effective due to the concept
of 'domain-shift' between virtual and real data. Although simulated data
augmented with a few real world samples has been shown to mitigate domain shift
and improve transferability of trained models, guiding or bootstrapping the
virtual data generation with the distributions learnt from target real world
domain is desired, especially in the fields where annotating even few real
images is laborious (such as semantic labeling, and intrinsic images etc.). In
order to address this problem in an unsupervised manner, our work combines
recent advances in CG (which aims to generate stochastic scene layouts coupled
with large collections of 3D object models) and generative adversarial training
(which aims train generative models by measuring discrepancy between generated
and real data in terms of their separability in the space of a deep
discriminatively-trained classifier). Our method uses iterative estimation of
the posterior density of prior distributions for a generative graphical model.
This is done within a rejection sampling framework. Initially, we assume
uniform distributions as priors on the parameters of a scene described by a
generative graphical model. As iterations proceed the prior distributions get
updated to distributions that are closer to the (unknown) distributions of
target data. We demonstrate the utility of adversarially tuned scene generation
on two real-world benchmark datasets (CityScapes and CamVid) for traffic scene
semantic labeling with a deep convolutional net (DeepLab). We realized
performance improvements by 2.28 and 3.14 points (using the IoU metric) between
the DeepLab models trained on simulated sets prepared from the scene generation
models before and after tuning to CityScapes and CamVid respectively.Comment: 9 pages, accepted at CVPR 201
Unsupervised Domain Adaptation Using Generative Adversarial Networks for Semantic Segmentation of Aerial Images
Segmenting aerial images is of great potential in surveillance and scene understanding of urban areas. It provides a mean for automatic reporting of the different events that happen in inhabited areas. This remarkably promotes public safety and traffic management applications. After the wide adoption of convolutional neural networks methods, the accuracy of semantic segmentation algorithms could easily surpass 80% if a robust dataset is provided. Despite this success, the deployment of a pretrained segmentation model to survey a new city that is not included in the training set significantly decreases accuracy. This is due to the domain shift between the source dataset on which the model is trained and the new target domain of the new city images. In this paper, we address this issue and consider the challenge of domain adaptation in semantic segmentation of aerial images. We designed an algorithm that reduces the domain shift impact using generative adversarial networks (GANs). In the experiments, we tested the proposed methodology on the International Society for Photogrammetry and Remote Sensing (ISPRS) semantic segmentation dataset and found that our method improves overall accuracy from 35% to 52% when passing from the Potsdam domain (considered as source domain) to the Vaihingen domain (considered as target domain). In addition, the method allows efficiently recovering the inverted classes due to sensor variation. In particular, it improves the average segmentation accuracy of the inverted classes due to sensor variation from 14% to 61%.info:eu-repo/semantics/publishedVersio
Unsupervised Domain Adaptation for Semantic Segmentation using One-shot Image-to-Image Translation via Latent Representation Mixing
Domain adaptation is one of the prominent strategies for handling both domain
shift, that is widely encountered in large-scale land use/land cover map
calculation, and the scarcity of pixel-level ground truth that is crucial for
supervised semantic segmentation. Studies focusing on adversarial domain
adaptation via re-styling source domain samples, commonly through generative
adversarial networks, have reported varying levels of success, yet they suffer
from semantic inconsistencies, visual corruptions, and often require a large
number of target domain samples. In this letter, we propose a new unsupervised
domain adaptation method for the semantic segmentation of very high resolution
images, that i) leads to semantically consistent and noise-free images, ii)
operates with a single target domain sample (i.e. one-shot) and iii) at a
fraction of the number of parameters required from state-of-the-art methods.
More specifically an image-to-image translation paradigm is proposed, based on
an encoder-decoder principle where latent content representations are mixed
across domains, and a perceptual network module and loss function is further
introduced to enforce semantic consistency. Cross-city comparative experiments
have shown that the proposed method outperforms state-of-the-art domain
adaptation methods. Our source code will be available at
\url{https://github.com/Sarmadfismael/LRM_I2I}
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