Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation

TL;DR

This paper introduces a novel blind super-resolution framework using ADA-Nets for multispectral aerial images, enabling effective dead tree segmentation without relying on paired high-resolution data.

Contribution

It presents the first real-world, generic super-resolution method for multispectral data aimed at standing dead tree segmentation, operating solely on unpaired samples.

Findings

Achieved 54% Dice score without high-resolution annotations.

Demonstrated effective restoration of degraded low-resolution multispectral images.

Provided a publicly available dataset for dead tree segmentation.

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.