Estimation of the Canopy Height Model From Multispectral Satellite Imagery With Convolutional Neural Networks

Illarionova, Svetlana; Shadrin, Dmitrii; Ignatiev, Vladimir; Shayakhmetov, Sergey; Trekin, Alexey; Oseledets, Ivan

doi:10.1109/access.2022.3161568

Cited by 25 publications

(16 citation statements)

References 67 publications

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“…We want to note that segmentation and image processing [52][53][54], as well as signal processing [55,56], are important research areas that must receive enough attention in the current studies. These papers demonstrate the importance of further innovation in these areas in order to help in the development of artificial intelligence.…”

Section: Discussionmentioning

confidence: 99%

Enabling Multi-Part Plant Segmentation with Instance-Level Augmentation Using Weak Annotations

et al. 2023

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Plant segmentation is a challenging computer vision task due to plant images complexity. For many practical problems, we have to solve even more difficult tasks. We need to distinguish plant parts rather than the whole plant. The major complication of multi-part segmentation is the absence of well-annotated datasets. It is very time-consuming and expensive to annotate datasets manually on the object parts level. In this article, we propose to use weakly supervised learning for pseudo-annotation. The goal is to train a plant part segmentation model using only bounding boxes instead of fine-grained masks. We review the existing weakly supervised learning approaches and propose an efficient pipeline for agricultural domains. It is designed to resolve tight object overlappings. Our pipeline beats the baseline solution by 23% for the plant part case and by 40% for the whole plant case. Furthermore, we apply instance-level augmentation to boost model performance. The idea of this approach is to obtain a weak segmentation mask and use it for cropping objects from original images and pasting them to new backgrounds during model training. This method provides us a 55% increase in mAP compared with the baseline on object part and a 72% increase on the whole plant segmentation tasks.

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Section: Discussionmentioning

confidence: 99%

Enabling Multi-Part Plant Segmentation with Instance-Level Augmentation Using Weak Annotations

et al. 2023

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“…UNet model and its derivative/improved models One of the main and frequently used architectures of CNNs for semantic segmentation of satellite images, including the forest mask segmentation, is the U-Net architecture. UNet is a variant of convolutional network originally introduced for biomedical image segmentation [21] and is presently often used in various semantic segmentation and regression tasks [15], [19], [22]. The basic UNet (also known as Vanilla UNet) uses convolutional network to extract image features.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning Models in Forest Mapping Using Multitemporal SAR and Optical Satellite Data

et al. 2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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Deep learning (DL) models are gaining popularity in forest variable prediction using Earth Observation images. However, in practical forest inventories, reference datasets are often represented by plot-or stand-level measurements, while high-quality representative wall-to-wall reference data for end-toend training of DL models are rarely available. Transfer learning facilitates expansion of the use of deep learning models into areas with sub-optimal training data by allowing pretraining of the model in areas where high-quality teaching data are available. In this study, we perform a "model transfer" (or domain adaptation) of a pretrained DL model into a target area using plot-level measurements and compare performance versus other machine learning models. We use an earlier developed UNet based model (SeUNet) to demonstrate the approach on two distinct taiga sites with varying forest structure and composition. Multisource Earth Observation (EO) data are represented by a combination of Copernicus Sentinel-1 C-band SAR and Sentinel-2 multispectral images, JAXA ALOS-2 PALSAR-2 SAR mosaic and TanDEM-X bistatic interferometric radar data. The training study site is located in Finnish Lapland, while the target site is located in Southern Finland. By leveraging transfer learning, the prediction of SeUNet achieved root mean squared error (RMSE) of 2.70 m and R 2 of 0.882, considerably more accurate than traditional benchmark methods. We expect such forest-specific DL model transfer can be suitable also for other forest variables and other EO data sources that are sensitive to forest structure.

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“…Although such features can be extracted from forest inventory data, another approach is to train a model to predict it. For instance, in [158], the canopy height was estimated using a CNN model. Next, these predictions are used to supply multispectral data in a forest-type classification task.…”

Section: Computer Vision Algorithms For Classifying Forest-forming Sp...mentioning

confidence: 99%

A Survey of Computer Vision Techniques for Forest Characterization and Carbon Monitoring Tasks

et al. 2022

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Estimation of terrestrial carbon balance is one of the key tasks in the understanding and prognosis of climate change impacts and the development of tools and policies according to carbon mitigation and adaptation strategies. Forest ecosystems are one of the major pools of carbon stocks affected by controversial processes influencing carbon stability. Therefore, monitoring forest ecosystems is a key to proper inventory management of resources and planning their sustainable use. In this survey, we discuss which computer vision techniques are applicable to the most important aspects of forest management actions, considering the wide availability of remote sensing (RS) data of different resolutions based both on satellite and unmanned aerial vehicle (UAV) observations. Our analysis applies to the most occurring tasks such as estimation of forest areas, tree species classification, and estimation of forest resources. Through the survey, we also provide a necessary technical background with a description of suitable data sources, algorithms’ descriptions, and corresponding metrics for their evaluation. The implementation of the provided techniques into routine workflows is a significant step toward the development of systems of continuous actualization of forest data, including real-time monitoring. It is crucial for diverse purposes on both local and global scales. Among the most important are the implementation of improved forest management strategies and actions, carbon offset projects, and enhancement of the prediction accuracy of system changes under different land-use and climate scenarios.

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Estimation of the Canopy Height Model From Multispectral Satellite Imagery With Convolutional Neural Networks

Cited by 25 publications

References 67 publications

Enabling Multi-Part Plant Segmentation with Instance-Level Augmentation Using Weak Annotations

Enabling Multi-Part Plant Segmentation with Instance-Level Augmentation Using Weak Annotations

Deep Learning Models in Forest Mapping Using Multitemporal SAR and Optical Satellite Data

A Survey of Computer Vision Techniques for Forest Characterization and Carbon Monitoring Tasks

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