An Overview on the Generation and Detection of Synthetic and Manipulated Satellite Images

Abady, Lydia; Cannas, Edoardo Daniele; Bestagini, Paolo; Tondi, Benedetta; Tubaro, Stefano; Barni, Mauro

doi:10.1561/116.00000142

Cited by 13 publications

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“…Technological advancements in the last few decades have led to increased satellite launches and the availability of satellite image datasets because of technological cost reductions (Abady et al, 2022). Consequently, satellite images have attracted tremendous interest due to their various applications, including land use and land cover (LULC) studies, forest and crop monitoring, urban planning (e.g., growth modeling, change detection, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, satellite images have attracted tremendous interest due to their various applications, including land use and land cover (LULC) studies, forest and crop monitoring, urban planning (e.g., growth modeling, change detection, etc. ), to mention a few (Abady et al, 2022;Purnamasayangsukasih, Norizah, Ismail, & Shamsudin, 2016). However, for the above-mentioned studies, semantic label maps are required for accurate and precise predictions (J.…”

Section: Introductionmentioning

confidence: 99%

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Generation Of Dense Urban Features Using Conditional GAN

Nihal,

Kumar

2023

Preprint

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This paper discusses the use of conditional Generative Adversarial Networks (GANs) to generate dense urban features in satellite images and evaluate their effectiveness in semantic segmentation tasks. High-resolution true-color satellite imagery of Mumbai, obtained from Pleiades-1A at a 0.5m resolution, is utilized for the study. The proposed Multiple Discriminator pix2pix (MD-pix2pix) model, which employs multiple discriminators and a modified training procedure, is introduced to generate realistic satellite images. The performance of the MD-pix2pix model is compared to the traditional pix2pix model using a mix dataset of real and generated satellite images. The synthesized satellite images are assessed for their effectiveness in semantic segmentation tasks using various CNN models, including VGG16-UNet, MobileNetV2-UNet, and DeepLabV3+. The study aims to overcome the limitations of existing datasets that do not include informal settlements, such as slum areas, which are common in many cities. The results indicate that the MD-pix2pix model produces more realistic satellite images with greater variability in vegetation type, slum arrangements, and built-ups than the traditional pix2pix model. The synthetically generated satellite images are also effective in semantic segmentation tasks, with better segmentation accuracy achieved using the MD-pix2pix generated images for computationally less expensive architectures such as MobileNetV2-UNet. This study highlights the potential of GANs to generate realistic satellite images for urban feature mapping and monitoring applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generation Of Dense Urban Features Using Conditional GAN

Nihal,

Kumar

2023

Preprint

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Forensic research of satellite images forgery: a comprehensive survey

Ding,

Nie,

Yao

et al. 2024

Artif Intell Rev

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HFRAS: design of a high-density feature representation model for effective augmentation of satellite images

Saini,

Garg,

Malik

et al. 2023

SIViP

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Efficiently extracting features from satellite images is crucial for classification and post-processing activities. Many feature representation models have been created for this purpose. However, most of them either increase computational complexity or decrease classification efficiency. The proposed model in this paper initially collects a set of available satellite images and represents them via a hybrid of long short-term memory (LSTM) and gated recurrent unit (GRU) features. These features are processed via an iterative genetic algorithm, identifying optimal augmentation methods for the extracted feature sets. To analyse the efficiency of this optimization process, we model an iterative fitness function that assists in incrementally improving the classification process. The fitness function uses an accuracy & precision-based feedback mechanism, which helps in tuning the hyperparameters of the proposed LSTM & GRU feature extraction process. The suggested model used 100 k images, 60% allocated for training and 20% each designated for validation and testing purposes. The proposed model can increase classification precision by 16.1% and accuracy by 17.1% compared to conventional augmentation strategies. The model also showcased incremental accuracy enhancements for an increasing number of training image sets.

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An Overview on the Generation and Detection of Synthetic and Manipulated Satellite Images

Cited by 13 publications

References 0 publications

Generation Of Dense Urban Features Using Conditional GAN

Generation Of Dense Urban Features Using Conditional GAN

Forensic research of satellite images forgery: a comprehensive survey

HFRAS: design of a high-density feature representation model for effective augmentation of satellite images

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