Two-Level Semantic-Driven Diffusion Based Hyperspectral Pansharpening
Over recent years, denoising diffusion probabilistic models (DDPMs) have received many attentions due to their powerful ability to infer data distribution. However, most of existing DDPM-based hyperspectral (HS) pansharpening methods over rely on local processing to perform recovery, which usually f...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10842049/ |
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| author | Lin He Wenrui Liang Antonio Plaza |
| author_facet | Lin He Wenrui Liang Antonio Plaza |
| author_sort | Lin He |
| collection | DOAJ |
| description | Over recent years, denoising diffusion probabilistic models (DDPMs) have received many attentions due to their powerful ability to infer data distribution. However, most of existing DDPM-based hyperspectral (HS) pansharpening methods over rely on local processing to perform recovery, which usually fails to reconcile global contextual semantics and local details in data. To address the issue, we propose a two-level semantic-driven diffusion method for HS pansharpening. In our method, we first extract semantics in two levels, where the low-level semantic not only leads the extraction of conditional details, but also supports the further semantic extraction while the high-level semantic is related to scene cognition. Then, the features from both the low-level and high-level semantics are conditionally injected to the denoising network to guide the high-resolution HS recovery. Experiments on multiple datasets verify the effectiveness of our method. |
| format | Article |
| id | doaj-art-bf4a2d5a9f4443cb93bb958e4606c2e6 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-bf4a2d5a9f4443cb93bb958e4606c2e62025-02-04T00:00:23ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184213422610.1109/JSTARS.2025.352999310842049Two-Level Semantic-Driven Diffusion Based Hyperspectral PansharpeningLin He0https://orcid.org/0000-0003-3801-7257Wenrui Liang1Antonio Plaza2https://orcid.org/0000-0002-9613-1659School of Automation Science and Engineering, South China University of Technology, Guangzhou, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou, ChinaHyperspectral Computing Laboratory, University of Extremadura, Caceres, SpainOver recent years, denoising diffusion probabilistic models (DDPMs) have received many attentions due to their powerful ability to infer data distribution. However, most of existing DDPM-based hyperspectral (HS) pansharpening methods over rely on local processing to perform recovery, which usually fails to reconcile global contextual semantics and local details in data. To address the issue, we propose a two-level semantic-driven diffusion method for HS pansharpening. In our method, we first extract semantics in two levels, where the low-level semantic not only leads the extraction of conditional details, but also supports the further semantic extraction while the high-level semantic is related to scene cognition. Then, the features from both the low-level and high-level semantics are conditionally injected to the denoising network to guide the high-resolution HS recovery. Experiments on multiple datasets verify the effectiveness of our method.https://ieeexplore.ieee.org/document/10842049/Diffusion modelhyperspectral (HS) imagespansharpeningtwo-level semantics |
| spellingShingle | Lin He Wenrui Liang Antonio Plaza Two-Level Semantic-Driven Diffusion Based Hyperspectral Pansharpening IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Diffusion model hyperspectral (HS) images pansharpening two-level semantics |
| title | Two-Level Semantic-Driven Diffusion Based Hyperspectral Pansharpening |
| title_full | Two-Level Semantic-Driven Diffusion Based Hyperspectral Pansharpening |
| title_fullStr | Two-Level Semantic-Driven Diffusion Based Hyperspectral Pansharpening |
| title_full_unstemmed | Two-Level Semantic-Driven Diffusion Based Hyperspectral Pansharpening |
| title_short | Two-Level Semantic-Driven Diffusion Based Hyperspectral Pansharpening |
| title_sort | two level semantic driven diffusion based hyperspectral pansharpening |
| topic | Diffusion model hyperspectral (HS) images pansharpening two-level semantics |
| url | https://ieeexplore.ieee.org/document/10842049/ |
| work_keys_str_mv | AT linhe twolevelsemanticdrivendiffusionbasedhyperspectralpansharpening AT wenruiliang twolevelsemanticdrivendiffusionbasedhyperspectralpansharpening AT antonioplaza twolevelsemanticdrivendiffusionbasedhyperspectralpansharpening |