Siamese-GAN: Learning Invariant Representations for Aerial Vehicle Image Categorization

Bashmal, Laila; Bazi, Yakoub; Alhichri, Haikel; Alrahhal, Mohamad Mahmoud; Ammour, Nassim; Alajlan, Naif

doi:10.3390/rs10020351

Cited by 56 publications

(24 citation statements)

References 48 publications

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“…[8] improved the cross-domain robustness of object detection by enforcing adversarial domain adaption on both image and instance levels. [5] introduced a Siamese-GAN to learn invariant feature representations for both labeled and unlabeled aerial images coming from two different domains. CyCADA [25] unified cycle-consistency with adversarial loss to learn domain-invariance.…”

Section: Handling Domain Variancesmentioning

confidence: 99%

Delving Into Robust Object Detection From Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach

Suresh

Narayanan

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming increasingly useful. Despite the great success of the generic object detection methods trained on ground-to-ground images, a huge performance drop is observed when they are directly applied to images captured by UAVs. The unsatisfactory performance is owing to many UAV-specific nuisances, such as varying flying altitudes, adverse weather conditions, dynamically changing viewing angles, etc. Those nuisances constitute a large number of fine-grained domains, across which the detection model has to stay robust. Fortunately, UAVs will record meta-data that depict those varying attributes, which are either freely available along with the UAV images, or can be easily obtained. We propose to utilize those free meta-data in conjunction with associated UAV images to learn domain-robust features via an adversarial training framework dubbed Nuisance Disentangled Feature Transform (NDFT), for the specific challenging problem of object detection in UAV images, achieving a substantial gain in robustness to those nuisances. We demonstrate the effectiveness of our proposed algorithm, by showing state-ofthe-art performance (single model) on two existing UAVbased object detection benchmarks. The code is available at https:// github.com/ TAMU-VITA/ UAV-NDFT.

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Section: Handling Domain Variancesmentioning

confidence: 99%

Delving Into Robust Object Detection From Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach

Suresh

Narayanan

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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“…Finally, we present in Table 3 a comparison between MB-Net and some recent domain adaptation methods based on a single source. In particular, we compare our results to the adversarial discriminative domain adaptation (ADDA) [49], which combines adversarial and discriminative learning, and the Siamese-GAN method, which reduces the discrepancy between the source and target domains using a Siamese encoder-decoder architecture [59]. These two architectures have been proposed recently for single domain adaptation and they have shown promising results compared to several state-of-the-art methods.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Othman et al [58] added additional regularization terms to the objective function of the neural network besides the standard cross-entropy loss, in order to compensate for the distribution mismatch to alleviate the low accuracies resulting from the approaches relying on pre-trained CNNs. In [59], the authors developed an approach based on adversarial networks for cross-domain classification in aerial vehicle images to overcome the data shift problem. Finally, the authors in [60] addressed this issue by projecting the source domain samples to the target domain via a regression network, while keeping the discrimination ability of the source samples.…”

Section: Introductionmentioning

confidence: 99%

Learning a Multi-Branch Neural Network from Multiple Sources for Knowledge Adaptation in Remote Sensing Imagery

et al. 2018

Self Cite

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In this paper we propose a multi-branch neural network, called MB-Net, for solving the problem of knowledge adaptation from multiple remote sensing scene datasets acquired with different sensors over diverse locations and manually labeled with different experts. Our aim is to learn invariant feature representations from multiple source domains with labeled images and one target domain with unlabeled images. To this end, we define for MB-Net an objective function that mitigates the multiple domain shifts at both feature representation and decision levels, while retaining the ability to discriminate between different land-cover classes. The complete architecture is trainable end-to-end via the backpropagation algorithm. In the experiments, we demonstrate the effectiveness of the proposed method on a new multiple domain dataset created from four heterogonous scene datasets well known to the remote sensing community, namely, the University of California (UC-Merced) dataset, the Aerial Image dataset (AID), the PatternNet dataset, and the Northwestern Polytechnical University (NWPU) dataset. In particular, this method boosts the average accuracy over all transfer scenarios up to 89.05% compared to standard architecture based only on cross-entropy loss, which yields an average accuracy of 78.53%.

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“…The dug features are able to enhance the classification accuracy. Bashmal et al [132] provided a GAN-based method, called Siamese-GAN, to handle the aerial vehicle images classification problems under cross-domain conditions. In [133], to generate high-quality remote sensing images for scene classification, Xu et al added In the area of remote sensing scene image classification, most of GAN-based methods usually use GANs for sample generation or feature learning in an adversarial manner.…”

Section: Gan-based Remote Sensing Image Scene Classificationmentioning

confidence: 99%

Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

Cheng

Xie

Han

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

625

267

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Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. Considering the rapid evolution of this field, this paper provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers. To be specific, we discuss the main challenges of remote sensing image scene classification and survey (1) Autoencoder-based remote sensing image scene classification methods, (2) Convolutional Neural Network-based remote sensing image scene classification methods, and (3) Generative Adversarial Network-based remote sensing image scene classification methods. In addition, we introduce the benchmarks used for remote sensing image scene classification and summarize the performance of more than two dozen of representative algorithms on three commonly-used benchmark data sets. Finally, we discuss the promising opportunities for further research.

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Siamese-GAN: Learning Invariant Representations for Aerial Vehicle Image Categorization

Cited by 56 publications

References 48 publications

Delving Into Robust Object Detection From Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach

Delving Into Robust Object Detection From Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach

Learning a Multi-Branch Neural Network from Multiple Sources for Knowledge Adaptation in Remote Sensing Imagery

Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

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