A Method for Generating Large-Scale Spaceborne Long-Wave Infrared Point-Target Images Using Landsat 8 TIRS Data

TY - JOUR

T1 - A Method for Generating Large-Scale Spaceborne Long-Wave Infrared Point-Target Images Using Landsat 8 TIRS Data

AU - Shan, Ruiyan

AU - Dong, Liquan

AU - Li, Kang

AU - Dong, Lianqing

AU - Zheng, Guoxian

AU - Zhang, Muyao

AU - Wang, Yu

AU - Ren, Weihe

N1 - Publisher Copyright: © 2026 IEEE.

PY - 2026

Y1 - 2026

N2 - Deep learning has demonstrated remarkable potential in spaceborne infrared small target detection; however, its application is severely bottlenecked by the scarcity of high-quality, annotated on-orbit data. To bridge this 'Sim-to-Real' domain gap, this article proposes a radiometrically consistent image generation framework capable of synthesizing large-scale, high-fidelity long-wave infrared (LWIR) datasets. Unlike traditional methods that rely on simplistic image injection or computationally expensive ray-tracing, our approach adopts a hybrid strategy: it leverages the global archive of calibrated Landsat-8 data to ensure authentic background diversity and couples it with physically modeled targets derived from computational fluid dynamics. A key innovation is the radiance-domain fusion mechanism, which integrates targets and backgrounds strictly in physical units, considering atmospheric transmission and sensor degradation effects (PSF, motion blur, noise equivalent delta temperature), thereby guaranteeing energy conservation for subpixel targets. Quantitative validation against real on-orbit imagery from the SDG satellite demonstrates that the proposed framework achieves superior fidelity, with intensity profiles and statistical metrics (HP-PSNR) closely matching real observations. This work provides a scalable solution to the data scarcity problem, offering a valuable foundation for training and benchmarking robust deep learning models in spaceborne surveillance tasks.

AB - Deep learning has demonstrated remarkable potential in spaceborne infrared small target detection; however, its application is severely bottlenecked by the scarcity of high-quality, annotated on-orbit data. To bridge this 'Sim-to-Real' domain gap, this article proposes a radiometrically consistent image generation framework capable of synthesizing large-scale, high-fidelity long-wave infrared (LWIR) datasets. Unlike traditional methods that rely on simplistic image injection or computationally expensive ray-tracing, our approach adopts a hybrid strategy: it leverages the global archive of calibrated Landsat-8 data to ensure authentic background diversity and couples it with physically modeled targets derived from computational fluid dynamics. A key innovation is the radiance-domain fusion mechanism, which integrates targets and backgrounds strictly in physical units, considering atmospheric transmission and sensor degradation effects (PSF, motion blur, noise equivalent delta temperature), thereby guaranteeing energy conservation for subpixel targets. Quantitative validation against real on-orbit imagery from the SDG satellite demonstrates that the proposed framework achieves superior fidelity, with intensity profiles and statistical metrics (HP-PSNR) closely matching real observations. This work provides a scalable solution to the data scarcity problem, offering a valuable foundation for training and benchmarking robust deep learning models in spaceborne surveillance tasks.

KW - Long-wave infrared (LWIR)

KW - point target simulation

KW - radiometric consistency

KW - sim-to-real validation

KW - spaceborne infrared imaging

UR - https://www.scopus.com/pages/publications/105031845407

U2 - 10.1109/JSTARS.2026.3670447

DO - 10.1109/JSTARS.2026.3670447

M3 - Article

AN - SCOPUS:105031845407

SN - 1939-1404

VL - 19

SP - 9112

EP - 9126

JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

ER -