scholarly journals Using Vehicle Synthesis Generative Adversarial Networks to Improve Vehicle Detection in Remote Sensing Images

2019 ◽  
Vol 8 (9) ◽  
pp. 390 ◽  
Author(s):  
Kun Zheng ◽  
Mengfei Wei ◽  
Guangmin Sun ◽  
Bilal Anas ◽  
Yu Li
Keyword(s):  
Remote Sensing ◽  
Traffic Control ◽  
Data Augmentation ◽  
Vehicle Detection ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Remote Sensing Images ◽  
Adversarial Networks ◽  
Vehicle Data ◽  
Augmentation Strategy

Vehicle detection based on very high-resolution (VHR) remote sensing images is beneficial in many fields such as military surveillance, traffic control, and social/economic studies. However, intricate details about the vehicle and the surrounding background provided by VHR images require sophisticated analysis based on massive data samples, though the number of reliable labeled training data is limited. In practice, data augmentation is often leveraged to solve this conflict. The traditional data augmentation strategy uses a combination of rotation, scaling, and flipping transformations, etc., and has limited capabilities in capturing the essence of feature distribution and proving data diversity. In this study, we propose a learning method named Vehicle Synthesis Generative Adversarial Networks (VS-GANs) to generate annotated vehicles from remote sensing images. The proposed framework has one generator and two discriminators, which try to synthesize realistic vehicles and learn the background context simultaneously. The method can quickly generate high-quality annotated vehicle data samples and greatly helps in the training of vehicle detectors. Experimental results show that the proposed framework can synthesize vehicles and their background images with variations and different levels of details. Compared with traditional data augmentation methods, the proposed method significantly improves the generalization capability of vehicle detectors. Finally, the contribution of VS-GANs to vehicle detection in VHR remote sensing images was proved in experiments conducted on UCAS-AOD and NWPU VHR-10 datasets using up-to-date target detection frameworks.

scholarly journals Improved SinGAN Integrated with an Attentional Mechanism for Remote Sensing Image Classification

2021 ◽  
Vol 13 (9) ◽  
pp. 1713
Author(s):  
Songwei Gu ◽  
Rui Zhang ◽  
Hongxia Luo ◽  
Mengyao Li ◽  
Huamei Feng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Life ◽  
Attention Mechanism ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Natural Image ◽  
Remote Sensing Images ◽  
Training Time ◽  
Adversarial Networks ◽  
Remote Sensing Image Classification

Deep learning is an important research method in the remote sensing field. However, samples of remote sensing images are relatively few in real life, and those with markers are scarce. Many neural networks represented by Generative Adversarial Networks (GANs) can learn from real samples to generate pseudosamples, rather than traditional methods that often require more time and man-power to obtain samples. However, the generated pseudosamples often have poor realism and cannot be reliably used as the basis for various analyses and applications in the field of remote sensing. To address the abovementioned problems, a pseudolabeled sample generation method is proposed in this work and applied to scene classification of remote sensing images. The improved unconditional generative model that can be learned from a single natural image (Improved SinGAN) with an attention mechanism can effectively generate enough pseudolabeled samples from a single remote sensing scene image sample. Pseudosamples generated by the improved SinGAN model have stronger realism and relatively less training time, and the extracted features are easily recognized in the classification network. The improved SinGAN can better identify sub-jects from images with complex ground scenes compared with the original network. This mechanism solves the problem of geographic errors of generated pseudosamples. This study incorporated the generated pseudosamples into training data for the classification experiment. The result showed that the SinGAN model with the integration of the attention mechanism can better guarantee feature extraction of the training data. Thus, the quality of the generated samples is improved and the classification accuracy and stability of the classification network are also enhanced.

scholarly journals Novelty Detection via Contrastive Learning with Negative Data Augmentation

2021 ◽  
Author(s):  
Chengwei Chen ◽  
Yuan Xie ◽  
Shaohui Lin ◽  
Ruizhi Qiao ◽  
Jian Zhou ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Novelty Detection ◽  
Training Data ◽  
Detection Methods ◽  
Generative Adversarial Networks ◽  
Negative Data ◽  
Discriminative Ability ◽  
Adversarial Networks ◽  
Training Mode ◽  
Augmentation Strategy

Novelty detection is the process of determining whether a query example differs from the learned training distribution. Previous generative adversarial networks based methods and self-supervised approaches suffer from instability training, mode dropping, and low discriminative ability. We overcome such problems by introducing a novel decoder-encoder framework. Firstly, a generative network (decoder) learns the representation by mapping the initialized latent vector to an image. In particular, this vector is initialized by considering the entire distribution of training data to avoid the problem of mode-dropping. Secondly, a contrastive network (encoder) aims to ``learn to compare'' through mutual information estimation, which directly helps the generative network to obtain a more discriminative representation by using a negative data augmentation strategy. Extensive experiments show that our model has significant superiority over cutting-edge novelty detectors and achieves new state-of-the-art results on various novelty detection benchmarks, e.g. CIFAR10 and DCASE. Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.

scholarly journals Unsupervised Haze Removal for High-Resolution Optical Remote-Sensing Images Based on Improved Generative Adversarial Networks

2020 ◽  
Vol 12 (24) ◽  
pp. 4162
Author(s):  
Anna Hu ◽  
Zhong Xie ◽  
Yongyang Xu ◽  
Mingyu Xie ◽  
Liang Wu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Data Set ◽  
Adversarial Networks ◽  
Ground Object ◽  
Edge Sharpening

One major limitation of remote-sensing images is bad weather conditions, such as haze. Haze significantly reduces the accuracy of satellite image interpretation. To solve this problem, this paper proposes a novel unsupervised method to remove haze from high-resolution optical remote-sensing images. The proposed method, based on cycle generative adversarial networks, is called the edge-sharpening cycle-consistent adversarial network (ES-CCGAN). Most importantly, unlike existing methods, this approach does not require prior information; the training data are unsupervised, which mitigates the pressure of preparing the training data set. To enhance the ability to extract ground-object information, the generative network replaces a residual neural network (ResNet) with a dense convolutional network (DenseNet). The edge-sharpening loss function of the deep-learning model is designed to recover clear ground-object edges and obtain more detailed information from hazy images. In the high-frequency information extraction model, this study re-trained the Visual Geometry Group (VGG) network using remote-sensing images. Experimental results reveal that the proposed method can recover different kinds of scenes from hazy images successfully and obtain excellent color consistency. Moreover, the ability of the proposed method to obtain clear edges and rich texture feature information makes it superior to the existing methods.

scholarly journals Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1104
Author(s):  
Yuanfu Gong ◽  
Puyun Liao ◽  
Xiaodong Zhang ◽  
Lifei Zhang ◽  
Guanzhou Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Super Resolution ◽  
The Other ◽  
Generative Adversarial Networks ◽  
Remote Sensing Images ◽  
Adversarial Networks ◽  
Large Size ◽  
Internal Inconsistency ◽  
Sentinel 2

Previously, generative adversarial networks (GAN) have been widely applied on super resolution reconstruction (SRR) methods, which turn low-resolution (LR) images into high-resolution (HR) ones. However, as these methods recover high frequency information with what they observed from the other images, they tend to produce artifacts when processing unfamiliar images. Optical satellite remote sensing images are of a far more complicated scene than natural images. Therefore, applying the previous networks on remote sensing images, especially mid-resolution ones, leads to unstable convergence and thus unpleasing artifacts. In this paper, we propose Enlighten-GAN for SRR tasks on large-size optical mid-resolution remote sensing images. Specifically, we design the enlighten blocks to induce network converging to a reliable point, and bring the Self-Supervised Hierarchical Perceptual Loss to attain performance improvement overpassing the other loss functions. Furthermore, limited by memory, large-scale images need to be cropped into patches to get through the network separately. To merge the reconstructed patches into a whole, we employ the internal inconsistency loss and cropping-and-clipping strategy, to avoid the seam line. Experiment results certify that Enlighten-GAN outperforms the state-of-the-art methods in terms of gradient similarity metric (GSM) on mid-resolution Sentinel-2 remote sensing images.

scholarly journals StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1497
Author(s):  
Harold Achicanoy ◽  
Deisy Chaves ◽  
Maria Trujillo
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Industrial Applications ◽  
Generative Models ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Augmentation Strategy ◽  
Synthetic Images ◽  
The Impact

Deep learning applications on computer vision involve the use of large-volume and representative data to obtain state-of-the-art results due to the massive number of parameters to optimise in deep models. However, data are limited with asymmetric distributions in industrial applications due to rare cases, legal restrictions, and high image-acquisition costs. Data augmentation based on deep learning generative adversarial networks, such as StyleGAN, has arisen as a way to create training data with symmetric distributions that may improve the generalisation capability of built models. StyleGAN generates highly realistic images in a variety of domains as a data augmentation strategy but requires a large amount of data to build image generators. Thus, transfer learning in conjunction with generative models are used to build models with small datasets. However, there are no reports on the impact of pre-trained generative models, using transfer learning. In this paper, we evaluate a StyleGAN generative model with transfer learning on different application domains—training with paintings, portraits, Pokémon, bedrooms, and cats—to generate target images with different levels of content variability: bean seeds (low variability), faces of subjects between 5 and 19 years old (medium variability), and charcoal (high variability). We used the first version of StyleGAN due to the large number of publicly available pre-trained models. The Fréchet Inception Distance was used for evaluating the quality of synthetic images. We found that StyleGAN with transfer learning produced good quality images, being an alternative for generating realistic synthetic images in the evaluated domains.

scholarly journals Generative inter-class transformations for imbalanced data weather classification

2021 ◽  
Vol 2021 (1) ◽  
pp. 16-20
Author(s):  
Apostolia Tsirikoglou ◽  
Marcus Gladh ◽  
Daniel Sahlin ◽  
Gabriel Eilertsen ◽  
Jonas Unger
Keyword(s):  
Data Augmentation ◽  
Imbalanced Data ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Medical Applications ◽  
Single Image ◽  
Adversarial Networks ◽  
Augmentation Strategies ◽  
Future Work ◽  
Small Dataset

This paper presents an evaluation of how data augmentation and inter-class transformations can be used to synthesize training data in low-data scenarios for single-image weather classification. In such scenarios, augmentations is a critical component, but there is a limit to how much improvements can be gained using classical augmentation strategies. Generative adversarial networks (GAN) have been demonstrated to generate impressive results, and have also been successful as a tool for data augmentation, but mostly for images of limited diversity, such as in medical applications. We investigate the possibilities in using generative augmentations for balancing a small weather classification dataset, where one class has a reduced number of images. We compare intra-class augmentations by means of classical transformations as well as noise-to-image GANs, to interclass augmentations where images from another class are transformed to the underrepresented class. The results show that it is possible to take advantage of GANs for inter-class augmentations to balance a small dataset for weather classification. This opens up for future work on GAN-based augmentations in scenarios where data is both diverse and scarce.

scholarly journals Generative Adversarial Networks for Pre-training of Medical Image Segmentation Networks

2020 ◽  
Author(s):  
Kun Chen ◽  
Manning Wang ◽  
Zhijian Song
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Medical Image ◽  
Deep Neural Networks ◽  
Data Augmentation ◽  
Medical Image Segmentation ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Synthetic Images

Abstract Background: Deep neural networks have been widely used in medical image segmentation and have achieved state-of-the-art performance in many tasks. However, different from the segmentation of natural images or video frames, the manual segmentation of anatomical structures in medical images needs high expertise so the scale of labeled training data is very small, which is a major obstacle for the improvement of deep neural networks performance in medical image segmentation. Methods: In this paper, we proposed a new end-to-end generation-segmentation framework by integrating Generative Adversarial Networks (GAN) and a segmentation network and train them simultaneously. The novelty is that during the training of the GAN, the intermediate synthetic images generated by the generator of the GAN are used to pre-train the segmentation network. As the advances of the training of the GAN, the synthetic images evolve gradually from being very coarse to containing more realistic textures, and these images help train the segmentation network gradually. After the training of GAN, the segmentation network is then fine-tuned by training with the real labeled images. Results: We evaluated the proposed framework on four different datasets, including 2D cardiac dataset and lung dataset, 3D prostate dataset and liver dataset. Compared with original U-net and CE-Net, our framework can achieve better segmentation performance. Our framework also can get better segmentation results than U-net on small datasets. In addition, our framework is more effective than the usual data augmentation methods. Conclusions: The proposed framework can be used as a pre-train method of segmentation network, which helps to get a better segmentation result. Our method can solve the shortcomings of current data augmentation methods to some extent.

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