Towards High-Resolution Alignment and Super-Resolution of Multi-Sensor Satellite Imagery
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908476528132096 |
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| author | Shin, Philip Wootaek Gaur, Vishal Ramachandran, Rahul Maskey, Manil Sampson, Jack Narayanan, Vijaykrishnan Roy, Sujit |
| author_facet | Shin, Philip Wootaek Gaur, Vishal Ramachandran, Rahul Maskey, Manil Sampson, Jack Narayanan, Vijaykrishnan Roy, Sujit |
| contents | High-resolution satellite imagery is essential for geospatial analysis, yet differences in spatial resolution across satellite sensors present challenges for data fusion and downstream applications. Super-resolution techniques can help bridge this gap, but existing methods rely on artificially downscaled images rather than real sensor data and are not well suited for heterogeneous satellite sensors with differing spectral, temporal characteristics. In this work, we develop a preliminary framework to align and upscale Harmonized Landsat Sentinel 30m(HLS 30) imagery using Harmonized Landsat Sentinel 10m(HLS10) as a reference from the HLS dataset. Our approach aims to bridge the resolution gap between these sensors and improve the quality of super-resolved Landsat imagery. Quantitative and qualitative evaluations demonstrate the effectiveness of our method, showing its potential for enhancing satellite-based sensing applications. This study provides insights into the feasibility of heterogeneous satellite image super-resolution and highlights key considerations for future advancements in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23150 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards High-Resolution Alignment and Super-Resolution of Multi-Sensor Satellite Imagery Shin, Philip Wootaek Gaur, Vishal Ramachandran, Rahul Maskey, Manil Sampson, Jack Narayanan, Vijaykrishnan Roy, Sujit Image and Video Processing Computer Vision and Pattern Recognition High-resolution satellite imagery is essential for geospatial analysis, yet differences in spatial resolution across satellite sensors present challenges for data fusion and downstream applications. Super-resolution techniques can help bridge this gap, but existing methods rely on artificially downscaled images rather than real sensor data and are not well suited for heterogeneous satellite sensors with differing spectral, temporal characteristics. In this work, we develop a preliminary framework to align and upscale Harmonized Landsat Sentinel 30m(HLS 30) imagery using Harmonized Landsat Sentinel 10m(HLS10) as a reference from the HLS dataset. Our approach aims to bridge the resolution gap between these sensors and improve the quality of super-resolved Landsat imagery. Quantitative and qualitative evaluations demonstrate the effectiveness of our method, showing its potential for enhancing satellite-based sensing applications. This study provides insights into the feasibility of heterogeneous satellite image super-resolution and highlights key considerations for future advancements in the field. |
| title | Towards High-Resolution Alignment and Super-Resolution of Multi-Sensor Satellite Imagery |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.23150 |