文献类型：期刊文献

英文题名：Temporal-Variation-Resistant Bidirectional Convolution-Transformer GAN for Remote Sensing Image Spatiotemporal Fusion

作者：Wu, Yuanyuan[1];Fu, Linjie[1];Zhong, Xinying[1];Qiu, Yuxuan[1];Lin, Cong[1]

机构：[1]Guangdong Ocean Univ, Guangdong Prov Engn Technol Res Ctr Smart Marine S, Sch Elect & Informat Engn, Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China

年份：2026

卷号：18

期号：15

外文期刊名：REMOTE SENSING

收录：SCI-EXPANDED(收录号：WOS:001847202600001)、、EI(收录号：20263321290989)、Scopus(收录号：2-s2.0-105047086740)、WOS

基金：This research was funded by the Youth S&T Talent Support Program of Guangdong Provincial Association for Science and Technology under Grant SKXRC2026527, Zhanjiang City Science and Technology Plan Project under Grant 2025B01103, Program for Scientific Research Start-Up Funds of Guangdong Ocean University under Grants 060302112405 and 060302112501, Guangdong Province Undergraduate Teaching Quality and Teaching Reform Project (Guangdong Higher Education Letter [2026] No. 4), Natural Science Foundation of Guangdong Province under Grant 2025A1515011356, National Natural Science Foundation of China under Grants 62562030, and the Undergraduate Innovation Team Project of Guangdong Ocean University under Grants CXTD2024011, JDTD2024003, CXXL2026193, and CXXL2026189.

语种：英文

外文关键词：time-varying disturbance resistance; bidirectional encoder; dual-guided cross convolution-attention fusion; decision attention fusion; multiresolution input discriminator

外文摘要：Highlights What are the main findings? What are the implications of the main findings?Highlights What are the main findings? What are the implications of the main findings?Abstract Single-source remote sensing image (RSI) cannot simultaneously meet high-spatial and high-temporal resolution requirements, failing to provide decision-makers with timely and accurate monitoring data. Spatiotemporal fusion (STF) of multi-source RSIs represents an efficient and convenient means of producing land-cover observations with high-temporal and high-spatial resolutions. However, current STF approaches still suffer from severe prediction distortion under abrupt changes, long-interval temporal variations, and land-cover type transitions, as well as poor robustness against disturbances in prior data. To address these challenges, a temporal-variation-resistant bidirectional convolution-Transformer generative adversarial network (TRB-GAN) for RSI STF, which comprises a temporal-variation-resistant bidirectional convolution-Transformer generator (TRBG) and a multiresolution input convolution-Transformer discriminator (MICTD), is devised to improve the robustness in predicting time-varying information and enhance STF capability. First, the TRBG designs a temporal-variation-resistant bidirectional encoder to capture prior information and arbitrary time-varying local-global features, enhancing prediction robustness and representation capability for time-varying information. Second, the TRBG designs a dual-guided triple-attention fusion decoder (DTAFD), incorporating dual-guided cross convolution-attention fusion and decision attention fusion. DTAFD dynamically calculates correlations among spectral, spatial, and time-varying information to aggregate heterogeneous features and adaptively performs stepwise weighting and integration, effectively mitigating the adverse impacts from heterogeneous imaging mechanisms and significant resolution gaps. Finally, MICTD and deep supervision enable adversarial learning of local-global structures and spectra across resolutions, providing feedback to the TRBG for producing finer images. Ablation and comparative experiments demonstrate the TRB-GAN achieves superior STF performance and stronger robustness to time-varying disturbances for the widely used CIA and LGC datasets.