MixChannel: Advanced Augmentation for Multispectral Satellite Images

Illarionova, Svetlana; Nesteruk, Sergey; Shadrin, Dmitrii; Ignatiev, Vladimir; Pukalchik, Mariia; Oseledets, Ivan

doi:10.3390/rs13112181

Cited by 19 publications

(10 citation statements)

References 32 publications

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“…In this study, we used all available images both for training and testing (splitting them into training and testing regions) as it is a common choice in the remote sensing domain [82]. However, in the future work, image-based cross-validation techniques can be used and robustness for new environmental conditions can be considered [83].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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The canopy height model (CHM) is a representation of the height of the top of vegetation from the surrounding ground level. It is crucial for the extraction of various forest characteristics, for instance, timber stock estimations and forest growth measurements. There are different ways of obtaining the vegetation height, such as through ground-based observations or the interpretation of remote sensing images. The severe downside of field measurement is its cost and acquisition difficulty. Therefore, utilizing remote sensing data is, in many cases, preferable. The enormous advances in computer vision during the previous decades have provided various methods of satellite imagery analysis. In this work, we developed the canopy height evaluation workflow using only RGB and NIR (near-infrared) bands of a very high spatial resolution (investigated on WorldView-2 satellite bands). Leveraging typical data from airplane-based LiDAR (Light Detection and Ranging), we trained a deep neural network to predict the vegetation height. The provided approach is less expensive than the commonly used drone measurements, and the predictions have a higher spatial resolution (less than 5 m) than the vast majority of studies using satellite data (usually more than 30 m). The experiments, which were conducted in Russian boreal forests, demonstrated a strong correlation between the prediction and LiDAR-derived measurements. Moreover, we tested the generated CHM as a supplementary feature in the species classification task. Among different input data combinations and training approaches, we achieved the mean absolute error equal to 2.4 m using U-Net with Inception-ResNet-v2 encoder, high-resolution RGB image, near-infrared band, and ArcticDEM. The obtained results show promising opportunities for advanced forestry analysis and management. We also developed the easyto-use open-access solution for solving these tasks based on the approaches discussed in the study cloud-free composite orthophotomap provided by Mapbox via tile-based map service.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…Moreover, the usage of autoencoder to increase the dataset can be studied in future. Some augmentation methods are already evaluated in the remote sensing area using hyperspectral data 29 – 31 .…”

Section: Discussionmentioning

confidence: 99%

Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging

Maktabi

Wichmann

Köhler

et al. 2022

Sci Rep

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Esophageal cancer is the sixth leading cause of cancer-related death worldwide. Histopathological confirmation is a key step in tumor diagnosis. Therefore, simplification in decision-making by discrimination between malignant and non-malignant cells of histological specimens can be provided by combination of new imaging technology and artificial intelligence (AI). In this work, hyperspectral imaging (HSI) data from 95 patients were used to classify three different histopathological features (squamous epithelium cells, esophageal adenocarcinoma (EAC) cells, and tumor stroma cells), based on a multi-layer perceptron with two hidden layers. We achieved an accuracy of 78% for EAC and stroma cells, and 80% for squamous epithelium. HSI combined with machine learning algorithms is a promising and innovative technique, which allows image acquisition beyond Red–Green–Blue (RGB) images. Further method validation and standardization will be necessary, before automated tumor cell identification algorithms can be used in daily clinical practice.

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“…For example, in [227], an approach was put forward capable of processing individual objects in the obtained image (RS data) and creating a new image sample that includes a meaningful composition of the target objects. A different augmentation approach proposed in [228] uses the multispectral specifics of RS data. The main idea of the proposed approach is the generation of a new image sample by mixing spectral bands from the satellite images collected for the same area but at different times.…”

Section: Data and Labeling Limitationsmentioning

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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MixChannel: Advanced Augmentation for Multispectral Satellite Images

Cited by 19 publications

References 32 publications

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

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

Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging

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

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