Large-area, 1964 land cover classifications of Corona spy satellite imagery for the Caucasus Mountains

Rizayeva, Afag; Niţă, M. D.; Radeloff, Volker C.

doi:10.1016/j.rse.2022.113343

Cited by 14 publications

(14 citation statements)

References 77 publications

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“…The spy-satellite image archive from the Cold War period represents a unique and, so far, largely untapped opportunity to assess ecological phenomena and processes across broad spatial and temporal scales (Nita et al 2018 , Rizayeva et al 2023 ). We see several opportunities for interdisciplinary and cross-disciplinary approaches to better integrate historical remote sensing with modern remote sensing (Hansen et al 2010 ) and with historical ecological research (Moritz et al 2008 , Tingley and Beissinger 2009 ).…”

Section: Opportunities For Ecology and Conservationmentioning

confidence: 99%

“…The limited uptake of the data for research may be related to the major challenge of operationalizing analyses for large areas (but see Song et al 2014 , Nita et al 2018 , Spada et al 2018 , Gurjar and Tare 2019 , Rendenieks et al 2020 , Rizayeva et al 2023 ). Recent advances in geoinformation and digitalization suggest that these barriers can be overcome in the coming years.…”

Section: Challenges For Remote Sensingmentioning

confidence: 99%

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The potential of historical spy-satellite imagery to support research in ecology and conservation

Munteanu,

Kraemer,

Hansen

et al. 2024

BioScience

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Remote sensing data are important for assessing ecological change, but their value is often restricted by their limited temporal coverage. Major historical events that affected the environment, such as those associated with colonial history, World War II, or the Green Revolution are not captured by modern remote sensing. In the present article, we highlight the potential of globally available black-and-white satellite photographs to expand ecological and conservation assessments back to the 1960s and to illuminate ecological concepts such as shifting baselines, time-lag responses, and legacy effects. This historical satellite photography can be used to monitor ecosystem extent and structure, species’ populations and habitats, and human pressures on the environment. Even though the data were declassified decades ago, their use in ecology and conservation remains limited. But recent advances in image processing and analysis can now unlock this research resource. We encourage the use of this opportunity to address important ecological and conservation questions.

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Section: Opportunities For Ecology and Conservationmentioning

confidence: 99%

Section: Challenges For Remote Sensingmentioning

confidence: 99%

The potential of historical spy-satellite imagery to support research in ecology and conservation

Munteanu,

Kraemer,

Hansen

et al. 2024

BioScience

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“…At the same time, the application of deepfake technology has raised a new concern known as “deepfake geography,” 11 which refers to the act of replacing an image in a scene with a fake satellite image while still maintaining the overall authentic appearance of the scene. Since satellite images play a crucial role in various applications, 12 – 15 there may be risks if the authenticity of satellite images cannot be reliably determined or used without proper verification. In particular, if adversaries create false satellite images to conceal or deceive about military infrastructure, it could lead to serious threats to national security and defense.…”

Section: Introductionmentioning

confidence: 99%

Hybrid network of convolutional neural network and transformer for deepfake geographic image detection

Liu,

Dong,

Xie

et al. 2024

J. Electron. Imag.

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In recent years, generative adversarial networks have been widely used to generate realistic fake images, and the current research mainly focuses on deepfake detection in face images. However, we are now facing the emergence of fake satellite images, which could potentially mislead and threaten national security. To address this issue, we propose a hybrid network of convolutional neural network and Transformer for deepfake geographic image detection. Specifically, our method combines the local modeling capability of convolutional neural networks with the global modeling capability of transformers. This allows us to effectively extract both local features and global representations from the satellite images. Additionally, we enhance the detection performance of the model by introducing channel attention mechanisms. Experimental results demonstrate that our proposed method outperforms existing approaches. Furthermore, under common image processing scenarios, such as Joint Photographic Experts Group compression and Gaussian noise corruption, our method also performs better than other comparison methods.

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“…Presently, remote-sensing image classification predominantly utilizes pixel-based [22][23][24] and object-oriented methods [25][26][27][28] in conjunction with optical imagery. Traditional pixelbased techniques often generate fragmented object information.…”

Section: Introductionmentioning

confidence: 99%

Eucalyptus Plantation Area Extraction Based on SLPSO-RFE Feature Selection and Multi-Temporal Sentinel-1/2 Data

Lin,

Ren,

et al. 2023

Forests

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An accurate and efficient estimation of eucalyptus plantation areas is of paramount significance for forestry resource management and ecological environment monitoring. Currently, combining multidimensional optical and SAR images with machine learning has become an important method for eucalyptus plantation classification, but there are still some challenges in feature selection. This study proposes a feature selection method that combines multi-temporal Sentinel-1 and Sentinel-2 data with SLPSO (social learning particle swarm optimization) and RFE (Recursive Feature Elimination), which reduces the impact of information redundancy and improves classification accuracy. Specifically, this paper first fuses multi-temporal Sentinel-1 and Sentinel-2 data, and then carries out feature selection by combining SLPSO and RFE to mitigate the effects of information redundancy. Next, based on features such as the spectrum, red-edge indices, texture characteristics, vegetation indices, and backscatter coefficients, the study employs the Simple Non-Iterative Clustering (SNIC) object-oriented method and three different types of machine-learning models: Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM) for the extraction of eucalyptus plantation areas. Each model uses a supervised-learning method, with labeled training data guiding the classification of eucalyptus plantation regions. Lastly, to validate the efficacy of selecting multi-temporal data and the performance of the SLPSO–RFE model in classification, a comparative analysis is undertaken against the classification results derived from single-temporal data and the ReliefF–RFE feature selection scheme. The findings reveal that employing SLPSO–RFE for feature selection significantly elevates the classification precision of eucalyptus plantations across all three classifiers. The overall accuracy rates were noted at 95.48% for SVM, 96% for CART, and 97.97% for RF. When contrasted with classification outcomes from multi-temporal data and ReliefF–RFE, the overall accuracy for the trio of models saw an increase of 10%, 8%, and 8.54%, respectively. The accuracy enhancement was even more pronounced when juxtaposed with results from single-temporal data and ReliefF-RFE, at increments of 15.25%, 13.58%, and 14.54% respectively. The insights from this research carry profound theoretical implications and practical applications, particularly in identifying and extracting eucalyptus plantations leveraging multi-temporal data and feature selection.

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Large-area, 1964 land cover classifications of Corona spy satellite imagery for the Caucasus Mountains

Cited by 14 publications

References 77 publications

The potential of historical spy-satellite imagery to support research in ecology and conservation

The potential of historical spy-satellite imagery to support research in ecology and conservation

Hybrid network of convolutional neural network and transformer for deepfake geographic image detection

Eucalyptus Plantation Area Extraction Based on SLPSO-RFE Feature Selection and Multi-Temporal Sentinel-1/2 Data

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