Towards user-adaptive remote sensing: Knowledge-driven automatic classification of Sentinel-2 time series

Arvor, Damien; Betbeder, Julie; Daher, Felipe R.G.; Blossier, Tim; Roux, Renan Le; Corgne, Samuel; Corpetti, Thomas; Silgueiro, Vinícius de Freitas; Silva, Carlos Antônio da

doi:10.1016/j.rse.2021.112615

Cited by 20 publications

(9 citation statements)

References 86 publications

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“…The overall accuracy of the GlobeLand30 landcover data for 2010 and 2020 is 83.50% and 85.72%, respectively, with kappa coefficients of 0.78 and 0.82 [33]. GlobeLand30 global land-cover data have a relatively high accuracy and can be used in relatively large-scale studies [34][35][36].…”

Section: Datamentioning

confidence: 97%

Analysis of Changes in Vegetation Carbon Storage and Net Primary Productivity as Influenced by Land-Cover Change in Inner Mongolia, China

et al. 2023

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Exploring the influence of vegetation carbon stocks over land-cover change and the relationship between land-cover change and net primary production (NPP) is of great practical importance for achieving China’s carbon neutrality target. Therefore, this paper analyses the spatio-temporal distribution of land-cover change and NPP change in Inner Mongolia from 2000 to 2020 and explores the vegetation carbon stock change caused by land-cover change, the relationship between land cover and NPP, and the relationship between land-cover change and NPP change. The results show that the main land-cover change in Inner Mongolia during 2000–2020 was the conversion from other land-cover types to grassland, with the conversion of bare land to grassland from 2000 to 2010 covering an area of 20,634.87 km2. During the period of 2000–2020, the high values of NPP were mainly located in northeastern Inner Mongolia, and the low values were mainly distributed in southwestern Inner Mongolia. The total carbon storage changes in vegetation affected by land-cover change during 2000–2010 and 2010–2020 were 10,736,986.11 t and 3,894,272.41 t. The mean values of NPP for different land covers were, in descending order, forest > cultivated land > wetland > grassland > artificial surfaces > shrubland > water bodies > bare land. Between 2000 and 2010, the change in land-cover type to water bodies was the main cause of the decrease in NPP, and the increase in NPP was mainly due to the conversion of other land-cover types to cultivated land, forest, and grassland. The implementation of appropriate conservation and management measures (e.g., the construction of forest and grass ecological protection), planting, and the protection of high-vegetation carbon storage plants and other means can improve the level of vegetation carbon sequestration and protect the ecological environment of Inner Mongolia.

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Section: Datamentioning

confidence: 97%

Analysis of Changes in Vegetation Carbon Storage and Net Primary Productivity as Influenced by Land-Cover Change in Inner Mongolia, China

et al. 2023

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“…R 2 (coefficient of determination), which reflects the accuracy of model fitting data and represents the proportion of variance explained by the model. The range is 0 to 1.…”

Section: Model Evaluationmentioning

confidence: 99%

“…It can be divided into satellite, airborne, UAV and water surface from the perspective of data acquisition platform. Common satellite data include Sentinel [2], Landsat8 [3], Hyperion [4], MODIS [5], IKONOS [6], MERIS [7], AHSI [8], PRISMA [9]; Airborne and UAV airborne data include HyMap [10], HIS [11], Spectral Evolution [12], VNIR [13], Hyperspectral Imager [14], Ocean Optics [15], Headwall [16], Gaia Sky-mini [17]; There are a large number of micro sensors represented by ASD [16] for water surface data. The data of different platforms have obvious advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Calculating Water Quality Parameters by Integrating Space-Ground Hyperspectral Data and Spectral-In situ Assay Data

Zhang¹,

Zhang²,

Sun³

et al. 2022

Preprint

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The effective integration of aerial remote sensing data and ground multi-source data has always been one of the difficulties of quantitative remote sensing. A new monitoring mode is designed which installs the hyperspectral imager on the UAV and places a buoy spectrometer on the river. Water samples are collected simultaneously to obtain in situ assay data of total phosphorus, total nitrogen, COD, turbidity and chlorophyll during data collection. The cross correlogram spectral matching (CCSM) algorithm is used to match the data of the buoy spectrometer with the UAV spectral data to reduce the UAV data noise significantly. An absorption characteristics recognition algorithm (ACR) is designed to realize a new method for comparing UAV data with laboratory data. This method takes into account the spectral characteristics and the correlation characteristics of test data synchronously. It is concluded that the most accurate water quality parameters can be calculated by using the regression method under five scales after the regression tests of multiple linear regression method (MLR), support vector machine method (SVM) and neural network (NN) method. This new working mode of integrating spectral imager data with point spectrometer data will become a trend in water quality monitoring.

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“…However, the classification of urban surface elements remains a formidable task within the field of remote sensing due to the existence of both sensory and semantic gaps [3].…”

Section: Introductionmentioning

confidence: 99%

Classification of Urban Surface Elements by Combining Multisource Data and Ontology

Zhu,

Lu,

Fan

2023

Remote Sensing

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The rapid pace of urbanization and increasing demands for urban functionalities have led to diversification and complexity in the types of urban surface elements. The conventional approach of relying solely on remote sensing imagery for urban surface element extraction faces emerging challenges. Data-driven techniques, including deep learning and machine learning, necessitate a substantial number of annotated samples as prerequisites. In response, our study proposes a knowledge-driven approach that integrates multisource data with ontology to achieve precise urban surface element extraction. Within this framework, components from the EIONET Action Group on Land Monitoring in Europe matrix serve as ontology primitives, forming a shared vocabulary. The semantics of surface elements are deconstructed using these primitives, enabling the creation of specific descriptions for various types of urban surface elements by combining these primitives. Our approach integrates multitemporal high-resolution remote sensing data, network big data, and other heterogeneous data sources. It segments high-resolution images into individual patches, and for each unit, urban surface element classification is accomplished through semantic rule-based inference. We conducted experiments in two regions with varying levels of urban scene complexity, achieving overall accuracies of 93.03% and 97.35%, respectively. Through this knowledge-driven approach, our proposed method significantly enhances the classification performance of urban surface elements in complex scenes, even in the absence of sample data, thereby presenting a novel approach to urban surface element extraction.

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Towards user-adaptive remote sensing: Knowledge-driven automatic classification of Sentinel-2 time series

Cited by 20 publications

References 86 publications

Analysis of Changes in Vegetation Carbon Storage and Net Primary Productivity as Influenced by Land-Cover Change in Inner Mongolia, China

Analysis of Changes in Vegetation Carbon Storage and Net Primary Productivity as Influenced by Land-Cover Change in Inner Mongolia, China

A New Method for Calculating Water Quality Parameters by Integrating Space-Ground Hyperspectral Data and Spectral-In situ Assay Data

Classification of Urban Surface Elements by Combining Multisource Data and Ontology

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