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Accurate spatial information of agricultural fields is important for providing actionable information to farmers, managers, and policymakers. On the other hand, the automated detection of field boundaries is a challenging task due to their small size, irregular shape and the use of mixed-cropping systems making field boundaries vaguely defined. In this paper, we propose a strategy for field boundary detection based on the fully convolutional network architecture called ResU-Net. The benefits of this model are two-fold: first, residual units ease training of deep networks. Second, rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters but better performance in comparison with the traditional U-Net model. An extensive experimental analysis is performed over the whole of Denmark using Sentinel-2 images and comparing several U-Net and ResU-Net field boundary detection algorithms. The presented results show that the ResU-Net model has a better performance with an average F1 score of 0.90 and average Jaccard coefficient of 0.80 in comparison to the U-Net model with an average F1 score of 0.88 and an average Jaccard coefficient of 0.77.
Alireza Taravat; Matthias Wagner; Rogerio Bonifacio; David Petit. Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection. Remote Sensing 2021, 13, 722 .
AMA StyleAlireza Taravat, Matthias Wagner, Rogerio Bonifacio, David Petit. Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection. Remote Sensing. 2021; 13 (4):722.
Chicago/Turabian StyleAlireza Taravat; Matthias Wagner; Rogerio Bonifacio; David Petit. 2021. "Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection." Remote Sensing 13, no. 4: 722.
Field mapping and information on agricultural landscapes is of increasing importance for many applications. Monitoring schemes and national cadasters provide a rich source of information but their maintenance and regular updating is costly and labor-intensive. Automatized mapping of fields based on remote sensing imagery may aid in this task and allow for a faster and more regular observation. Although remote sensing has seen extensive use in agricultural research topics, such as plant health monitoring, crop type classification, yield prediction, and irrigation, field delineation and extraction has seen comparatively little research interest. In this study, we present a field boundary detection technique based on deep learning and a variety of image features, and combine it with the graph-based growing contours (GGC) method to extract agricultural fields in a study area in northern Germany. The boundary detection step only requires red, green, and blue (RGB) data and is therefore largely independent of the sensor used. We compare different image features based on color and luminosity information and evaluate their usefulness for the task of field boundary detection. A model based on texture metrics, gradient information, Hessian matrix eigenvalues, and local statistics showed good results with accuracies up to 88.2%, an area under the ROC curve (AUC) of up to 0.94, and F1 score of up to 0.88. The exclusive use of these universal image features may also facilitate transferability to other regions. We further present modifications to the GGC method intended to aid in upscaling of the method through process acceleration with a minimal effect on results. We combined the boundary detection results with the GGC method for field polygon extraction. Results were promising, with the new GGC version performing similarly or better than the original version while experiencing an acceleration of 1.3× to 2.3× on different subsets and input complexities. Further research may explore other applications of the GGC method outside agricultural remote sensing and field extraction.
Matthias P. Wagner; Natascha Oppelt. Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction. Remote Sensing 2020, 12, 1990 .
AMA StyleMatthias P. Wagner, Natascha Oppelt. Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction. Remote Sensing. 2020; 12 (12):1990.
Chicago/Turabian StyleMatthias P. Wagner; Natascha Oppelt. 2020. "Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction." Remote Sensing 12, no. 12: 1990.
Roof and wall slates are fine-grained rocks with slaty cleavage, and it is often difficult to determine their mineral composition. A new norm mineral calculation called slatecalculation allows the determination of a virtual mineral composition based on full chemical analysis, including the amounts of carbon dioxide (CO2), carbon (C), and sulfur (S). Derived norm minerals include feldspars, carbonates, micas, hydro-micas, chlorites, ore-minerals, and quartz. The mineral components of the slate are assessed with superior accuracy compared to the petrographic analysis based on the European Standard EN 12326. The inevitable methodical inaccuracies in the calculations are limited and transparent. In the present paper, slates, shales, and phyllites from worldwide occurrences were examined. This also gives an overview of the rocks used for discontinuous roofing and external cladding.
Hans Wolfgang Wagner; Dieter Jung; Jean-Frank Wagner; Matthias Patrick Wagner. Slatecalculation—A Practical Tool for Deriving Norm Minerals in the Lowest-Grade Metamorphic Pelites and Roof Slates. Minerals 2020, 10, 395 .
AMA StyleHans Wolfgang Wagner, Dieter Jung, Jean-Frank Wagner, Matthias Patrick Wagner. Slatecalculation—A Practical Tool for Deriving Norm Minerals in the Lowest-Grade Metamorphic Pelites and Roof Slates. Minerals. 2020; 10 (5):395.
Chicago/Turabian StyleHans Wolfgang Wagner; Dieter Jung; Jean-Frank Wagner; Matthias Patrick Wagner. 2020. "Slatecalculation—A Practical Tool for Deriving Norm Minerals in the Lowest-Grade Metamorphic Pelites and Roof Slates." Minerals 10, no. 5: 395.
Knowledge of the location and extent of agricultural fields is required for many applications, including agricultural statistics, environmental monitoring, and administrative policies. Furthermore, many mapping applications, such as object-based classification, crop type distinction, or large-scale yield prediction benefit significantly from the accurate delineation of fields. Still, most existing field maps and observation systems rely on historic administrative maps or labor-intensive field campaigns. These are often expensive to maintain and quickly become outdated, especially in regions of frequently changing agricultural patterns. However, exploiting openly available remote sensing imagery (e.g., from the European Union’s Copernicus programme) may allow for frequent and efficient field mapping with minimal human interaction. We present a new approach to extracting agricultural fields at the sub-pixel level. It consists of boundary detection and a field polygon extraction step based on a newly developed, modified version of the growing snakes active contours model we refer to as graph-based growing contours. This technique is capable of extracting complex networks of boundaries present in agricultural landscapes, and is largely automatic with little supervision required. The whole detection and extraction process is designed to work independently of sensor type, resolution, or wavelength. As a test case, we applied the method to two regions of interest in a study area in the northern Germany using multi-temporal Sentinel-2 imagery. Extracted fields were compared visually and quantitatively to ground reference data. The technique proved reliable in producing polygons closely matching reference data, both in terms of boundary location and statistical proxies such as median field size and total acreage.
Matthias P. Wagner; Natascha Oppelt. Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours. Remote Sensing 2020, 12, 1205 .
AMA StyleMatthias P. Wagner, Natascha Oppelt. Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours. Remote Sensing. 2020; 12 (7):1205.
Chicago/Turabian StyleMatthias P. Wagner; Natascha Oppelt. 2020. "Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours." Remote Sensing 12, no. 7: 1205.
A growing world population, increasing prosperity in emerging countries, and shifts in energy and food demands necessitate a continuous increase in global agricultural production. Simultaneously, risks of extreme weather events and a slowing productivity growth in recent years has caused concerns about meeting the demands in the future. Crop monitoring and timely yield predictions are an important tool to mitigate risk and ensure food security. A common approach is to combine the temporal simulation of dynamic crop models with a geospatial component by assimilating remote sensing data. To ensure reliable assimilation, handling of uncertainties in both models and the assimilated input data is crucial. Here, we present a new approach for data assimilation using particle swarm optimization (PSO) in combination with statistical distance metrics that allow for flexible handling of model and input uncertainties. We explored the potential of the newly proposed method in a case study by assimilating canopy cover (CC) information, obtained from Sentinel-2 data, into the AquaCrop-OS model to improve winter wheat yield estimation on the pixel- and field-level and compared the performance with two other methods (simple updating and extended Kalman filter). Our results indicate that the performance of the new method is superior to simple updating and similar or better than the extended Kalman filter updating. Furthermore, it was particularly successful in reducing bias in yield estimation.
Matthias P. Wagner; Thomas Slawig; Alireza Taravat; Natascha Oppelt. Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization. ISPRS International Journal of Geo-Information 2020, 9, 105 .
AMA StyleMatthias P. Wagner, Thomas Slawig, Alireza Taravat, Natascha Oppelt. Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization. ISPRS International Journal of Geo-Information. 2020; 9 (2):105.
Chicago/Turabian StyleMatthias P. Wagner; Thomas Slawig; Alireza Taravat; Natascha Oppelt. 2020. "Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization." ISPRS International Journal of Geo-Information 9, no. 2: 105.
Grassland contributes to carbon storage and animal feed production. Its yield is largely determined by the cutting times of grassland. Previous studies have used remote sensing data for grassland biomass estimation, but only a few studies have focused on SAR remote sensing approaches for automatic grassland cutting status detection. Due to the occurrence of multiple cuttings in a year, it is crucial to effectively monitor grassland cutting events in order to achieve accurate biomass estimations of a whole season. In this study, we examined the capabilities of multilayer perceptron neural networks for automatic grassland cutting status detection using SAR imagery. The proposed model inputs are a time series dataset of VV and VH Sentinel-1 C-band SAR and second-order texture metrics (homogeneity, entropy, contrast and dissimilarity). The proposed approach has been successfully tested on a dataset collected from several fields in Germany in 2016, with an overall accuracy of 85.71% for the validation set.
Alireza Taravat; Matthias P. Wagner; Natascha Oppelt. Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks. Remote Sensing 2019, 11, 711 .
AMA StyleAlireza Taravat, Matthias P. Wagner, Natascha Oppelt. Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks. Remote Sensing. 2019; 11 (6):711.
Chicago/Turabian StyleAlireza Taravat; Matthias P. Wagner; Natascha Oppelt. 2019. "Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks." Remote Sensing 11, no. 6: 711.