TY - GEN
T1 - ESC-MISR
T2 - 31st International Conference on Multimedia Modeling, MMM 2025
AU - Zhang, Zhihui
AU - Hao, Xiaoshuai
AU - Li, Jianan
AU - Pang, Jinhui
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Multi-Image Super-Resolution (MISR) is a crucial yet challenging research task in the remote sensing community. In this paper, we address the challenging task of Multi-Image Super-Resolution in Remote Sensing (MISR-RS), aiming to generate a High-Resolution (HR) image from multiple Low-Resolution (LR) images obtained by satellites. Recently, the weak temporal correlations among LR images have attracted increasing attention in the MISR-RS task. However, existing MISR methods treat the LR images as sequences with strong temporal correlations, overlooking spatial correlations and imposing temporal dependencies. To address this problem, we propose a novel end-to-end framework named Enhancing Spatial Correlations in MISR (ESC-MISR), which fully exploits the spatial-temporal relations of multiple images for HR image reconstruction. Specifically, we first introduce a novel fusion module named Multi-Image Spatial Transformer (MIST), which emphasizes parts with clearer global spatial features and enhances the spatial correlations between LR images. Besides, we perform a random shuffle strategy for the sequential inputs of LR images to attenuate temporal dependencies and capture weak temporal correlations in the training stage. Compared with the state-of-the-art methods, our ESC-MISR achieves 0.70 dB and 0.76 dB cPSNR improvements on the two bands of the PROBA-V dataset respectively, demonstrating the superiority of our method.
AB - Multi-Image Super-Resolution (MISR) is a crucial yet challenging research task in the remote sensing community. In this paper, we address the challenging task of Multi-Image Super-Resolution in Remote Sensing (MISR-RS), aiming to generate a High-Resolution (HR) image from multiple Low-Resolution (LR) images obtained by satellites. Recently, the weak temporal correlations among LR images have attracted increasing attention in the MISR-RS task. However, existing MISR methods treat the LR images as sequences with strong temporal correlations, overlooking spatial correlations and imposing temporal dependencies. To address this problem, we propose a novel end-to-end framework named Enhancing Spatial Correlations in MISR (ESC-MISR), which fully exploits the spatial-temporal relations of multiple images for HR image reconstruction. Specifically, we first introduce a novel fusion module named Multi-Image Spatial Transformer (MIST), which emphasizes parts with clearer global spatial features and enhances the spatial correlations between LR images. Besides, we perform a random shuffle strategy for the sequential inputs of LR images to attenuate temporal dependencies and capture weak temporal correlations in the training stage. Compared with the state-of-the-art methods, our ESC-MISR achieves 0.70 dB and 0.76 dB cPSNR improvements on the two bands of the PROBA-V dataset respectively, demonstrating the superiority of our method.
KW - high-resolution remote sensing images
KW - Multi-image super-resolution
KW - spatial transformer
KW - spatial-temporal correlations
UR - https://www.scopus.com/pages/publications/85216112098
U2 - 10.1007/978-981-96-2054-8_28
DO - 10.1007/978-981-96-2054-8_28
M3 - Conference contribution
AN - SCOPUS:85216112098
SN - 9789819620531
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 373
EP - 387
BT - MultiMedia Modeling - 31st International Conference on Multimedia Modeling, MMM 2025, Proceedings
A2 - Ide, Ichiro
A2 - Kompatsiaris, Ioannis
A2 - Xu, Changsheng
A2 - Yanai, Keiji
A2 - Chu, Wei-Ta
A2 - Nitta, Naoko
A2 - Riegler, Michael
A2 - Yamasaki, Toshihiko
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 8 January 2025 through 10 January 2025
ER -