AI RESEARCH
Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation
arXiv CS.CV
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ArXi:2605.02471v1 Announce Type: new Mapping standing dead trees is crucial for acquiring information on the effects of climate change on forests and forest biodiversity. However, leveraging high-quality aerial imagery for dead tree segmentation poses challenges due to limitations in sensor availability and the scarcity of annotated data. In this study, we propose a generic blind super-resolution framework that incorporates Attention-Guided Domain Adaptation Networks (ADA-Nets) to learn the mapping from low-resolution to high-resolution multispectral image domains.