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Generative adversarial networks with texture recovery and physical constraints for remote sensing image dehazing. [PDF]

Sci Rep
Jia Y, Yu W, Zhao L.
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A new band selection framework for hyperspectral remote sensing image classification. [PDF]

Sci Rep
Phaneendra Kumar BLN, Vaddi R, Manoharan P, Agilandeeswari L, Sangeetha V.   +4 more
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Pure data correction enhancing remote sensing image classification with a lightweight ensemble model. [PDF]

Sci Rep
Song H, Xie H, Duan Y, Xie X, Gan F, Wang W, Liu J.   +6 more
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TGF-Net: Transformer and gist CNN fusion network for multi-modal remote sensing image classification. [PDF]

PLoS One
Wang H, Wang H, Wu L.
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Semantic information guided diffusion posterior sampling for remote sensing image fusion. [PDF]

Sci Rep
Zhang C, Chang Y, Wu Y, Shui Y, Wang Z, Zhu J.   +5 more
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DVF-NET: Bi-Temporal Remote Sensing Image Registration Network Based on Displacement Vector Field Fusion. [PDF]

Sensors (Basel)
Xue M, Zhang Y, Jia S, Cao C, Feng L, Liu W.   +5 more
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Remote sensing image Super-resolution reconstruction by fusing multi-scale receptive fields and hybrid transformer. [PDF]

Sci Rep
Liu D, Zhong L, Wu H, Li S, Li Y.
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Remote sensing image synthesis

2010 IEEE International Geoscience and Remote Sensing Symposium, 2010
For remote sensing data, the testing analysis tools is difficult since the ground-truth data are not available in many cases. To address this issue, a novel method for image synthesis is presented for use as a evaluation test-bed. Given the scale-dependent, non-stationary nature of remotely sensed data, a new modeling approach that combines a ...
Ying Liu, Alexander Wong, Paul Fieguth
openaire   +1 more source