Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation

• URL: http://arxiv.org/abs/2605.02471v1
• Date: Mon, 04 May 2026 11:13:50 GMT
• Title: Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation
• Abstract summary: We propose a generic blind super-resolution framework to learn the mapping from low-resolution to high-resolution multispectral image domains.<n>Our approach operates solely on unpaired samples, mimicking real-world conditions.<n>This is the first study to perform real-world and generic super-resolution for multispectral data in the scope of standing dead tree segmentation.
• Score: 3.164363223464948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation [49.13393683126712]
  Urban forests play a key role in enhancing environmental quality and supporting biodiversity in cities.<n> accurately detecting trees is challenging due to complex landscapes and the variability in image resolution caused by different satellite sensors or UAV flight altitudes.<n>We propose a novel pipeline that integrates domain adaptation with GANs and Diffusion models to enhance the quality of low-resolution aerial images.
  arXiv  Detail & Related papers  (2025-05-21T03:57:10Z)
• Plant detection from ultra high resolution remote sensing images: A Semantic Segmentation approach based on fuzzy loss [2.6489824612123716]
  We tackle the challenge of identifying plant species from ultra high resolution (UHR) remote sensing images. Our approach involves introducing an RGB remote sensing dataset, characterized by millimeter-level spatial resolution. First experimental results obtained on both our UHR dataset and a public dataset are presented, showing the relevance of the proposed methodology.
  arXiv  Detail & Related papers  (2024-08-31T17:40:17Z)
• High-Resolution Vision Transformers for Pixel-Level Identification of Structural Components and Damage [1.8923948104852863]
  We develop a semantic segmentation network based on vision transformers and Laplacian pyramids scaling networks. The proposed framework has been evaluated through comprehensive experiments on a dataset of bridge inspection report images.
  arXiv  Detail & Related papers  (2023-08-06T03:34:25Z)
• Progressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery [0.0]
  State-of-the-art object detection methods largely fail to identify small and dense objects. We propose a small object detection pipeline that improves the feature extraction process. We show we can alleviate the degradation of object identification in previously unseen datasets.
  arXiv  Detail & Related papers  (2022-09-06T15:16:35Z)
• Deep Semantic Statistics Matching (D2SM) Denoising Network [70.01091467628068]
  We introduce the Deep Semantic Statistics Matching (D2SM) Denoising Network. It exploits semantic features of pretrained classification networks, then it implicitly matches the probabilistic distribution of clear images at the semantic feature space. By learning to preserve the semantic distribution of denoised images, we empirically find our method significantly improves the denoising capabilities of networks.
  arXiv  Detail & Related papers  (2022-07-19T14:35:42Z)
• Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution [64.15915577164894]
  A hierarchical image super-resolution network (HSRNet) is proposed to suppress the influence of aliasing. HSRNet achieves better quantitative and visual performance than other works, and remits the aliasing more effectively.
  arXiv  Detail & Related papers  (2022-06-07T14:55:32Z)
• High Quality Segmentation for Ultra High-resolution Images [72.97958314291648]
  We propose the Continuous Refinement Model for the ultra high-resolution segmentation refinement task. Our proposed method is fast and effective on image segmentation refinement.
  arXiv  Detail & Related papers  (2021-11-29T11:53:06Z)
• Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priors [0.5276232626689566]
  We introduce a framework for segmenting instances of a common object class by multiple active contour evolution over semantic segmentation maps of images. We instantiate the proposed framework in the context of segmenting individual fallen stems from high-resolution aerial multispectral imagery.
  arXiv  Detail & Related papers  (2021-05-05T11:54:05Z)
• Gated Fusion Network for Degraded Image Super Resolution [78.67168802945069]
  We propose a dual-branch convolutional neural network to extract base features and recovered features separately. By decomposing the feature extraction step into two task-independent streams, the dual-branch model can facilitate the training process.
  arXiv  Detail & Related papers  (2020-03-02T13:28:32Z)
• Image Fine-grained Inpainting [89.17316318927621]
  We present a one-stage model that utilizes dense combinations of dilated convolutions to obtain larger and more effective receptive fields. To better train this efficient generator, except for frequently-used VGG feature matching loss, we design a novel self-guided regression loss. We also employ a discriminator with local and global branches to ensure local-global contents consistency.
  arXiv  Detail & Related papers  (2020-02-07T03:45:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.