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arXiv · 2605.02471

Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation

Mete Ahishali·Anis Ur Rahman·Einari Heinaro·Aysen Degerli·Samuli Junttila
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Abstract

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. Our approach operates solely on unpaired samples, mimicking real-world conditions, i.e., low-resolution images are not synthetically obtained by downsampling the high-resolution images. Moreover, the proposed method serves as a general-purpose restorer addressing several image degradation types, including saturation, noise, and low contrast that typically occur in low-resolution images acquired by low-end sensors. To the best of our knowledge, this is the first study to perform real-world and generic super-resolution for multispectral data in the scope of standing dead tree segmentation. Experimental evaluations demonstrate segmentation performances of 54% and 64% in Dice scores. Notably, the first result is obtained without using any high-resolution annotations; the segmentation network is trained on super-resolved low-resolution images, while evaluation is performed on the high-resolution data. We publicly share the aerial multispectral dataset with manually annotated labels at https://www.kaggle.com/datasets/meteahishali/aerial-imagery-for-dead-tree-segmentation-poland.

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Mete Ahishali, Anis Ur Rahman, Einari Heinaro, Aysen Degerli, Samuli Junttila. 2026-05-04. Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation. https://arxiv.org/abs/2605.02471

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