Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence

Sandino, Juan; Pegg, Geoff S.; Gonzalez, Felipé; Smith, Grant R.

doi:10.3390/s18040944

Cited by 97 publications

(65 citation statements)

References 58 publications

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“…Among these algorithms, the gradient tree boosting (GTB) [59][60][61] and the extreme gradient boosting (XGB) algorithms [62][63][64] have proven to be among the most effective from the ensemble family and are known for outstanding performances and state-of-the-art results in many research areas. In particular, the XGB was reported to be faster, more robust to noise, class imbalance (a problem in our case study) and exhibiting promising performances on classification tasks of RS data, outperforming various benchmark classifiers [65][66][67].…”

Section: Ensemble Learning Classification and Validationmentioning

confidence: 83%

Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion

2020

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Recent developments in the fields of geographical object-based image analysis (GEOBIA) and ensemble learning (EL) have led the way to the development of automated processing frameworks suitable to tackle large-scale problems. Mapping riverscape units has been recognized in fluvial remote sensing as an important concern for understanding the macrodynamics of a river system and, if applied at large scales, it can be a powerful tool for monitoring purposes. In this study, the potentiality of GEOBIA and EL algorithms were tested for the mapping of key riverscape units along the main European river network. The Copernicus VHR Image Mosaic and the EU Digital Elevation Model (EU-DEM)—both made available through the Copernicus Land Monitoring Service—were integrated within a hierarchical object-based architecture. In a first step, the most well-known EL techniques (bagging, boosting and voting) were tested for the automatic classification of water, sediment bars, riparian vegetation and other floodplain units. Random forest was found to be the best-to-use classifier, and therefore was used in a second phase to classify the entire object-based river network. Finally, an independent validation was performed taking into consideration the polygon area within the accuracy assessment, hence improving the efficiency of the classification accuracy of the GEOBIA-derived map, both globally and by geographical zone. As a result, we automatically processed almost 2 million square kilometers at a spatial resolution of 2.5 meters, producing a riverscape-units map with a global overall accuracy of 0.915, and with per-class F1 accuracies in the range 0.79–0.97. The obtained results may allow for future studies aimed at quantitative, objective and continuous monitoring of river evolutions and fluvial geomorphological processes at the scale of Europe.

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Section: Ensemble Learning Classification and Validationmentioning

confidence: 83%

Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion

2020

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“…Background. Deep learning has been proven to be a powerful tool for pattern classification problems and sensor studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. A deep learning model usually has more than three layers, and by using multiple layers, the model extracts hierarchical features from the original data.…”

Section: Introductionmentioning

confidence: 99%

Iterative Deep Neighborhood: A Deep Learning Model Which Involves Both Input Data Points and Their Neighbors

Liu

Yan

et al. 2020

Computational Intelligence and Neuroscience

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Deep learning models, such as deep convolutional neural network and deep long-short term memory model, have achieved great successes in many pattern classification applications over shadow machine learning models with hand-crafted features. The main reason is the ability of deep learning models to automatically extract hierarchical features from massive data by multiple layers of neurons. However, in many other situations, existing deep learning models still cannot gain satisfying results due to the limitation of the inputs of models. The existing deep learning models only take the data instances of an input point but completely ignore the other data points in the dataset, which potentially provides critical insight for the classification of the given input. To overcome this gap, in this paper, we show that the neighboring data points besides the input data point itself can boost the deep learning model’s performance significantly and design a novel deep learning model which takes both the data instances of an input point and its neighbors’ classification responses as inputs. In addition, we develop an iterative algorithm which updates the neighbors of data points according to the deep representations output by the deep learning model and the parameters of the deep learning model alternately. The proposed algorithm, named “Iterative Deep Neighborhood (IDN),” shows its advantages over the state-of-the-art deep learning models over tasks of image classification, text sentiment analysis, property price trend prediction, etc.

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“…They reported many-to-one segmentation errors that needed to be corrected manually. Additionally, UAV's remote sensing capabilities have been used to detect and segment trees deteriorated by myrtle rust in natural and plantation forests [36]. This work illustrates an experimentation case on paperbark tea trees in Australia, which integrates an on-board hyperspectral camera and several machine learning algorithms for the classification.…”

Section: Introductionmentioning

confidence: 99%

On-the-Fly Olive Tree Counting Using a UAS and Cloud Services

et al. 2019

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Unmanned aerial systems (UAS) are becoming a common tool for aerial sensing applications. Nevertheless, sensed data need further processing before becoming useful information. This processing requires large computing power and time before delivery. In this paper, we present a parallel architecture that includes an unmanned aerial vehicle (UAV), a small embedded computer on board, a communication link to the Internet, and a cloud service with the aim to provide useful real-time information directly to the end-users. The potential of parallelism as a solution in remote sensing has not been addressed for a distributed architecture that includes the UAV processors. The architecture is demonstrated for a specific problem: the counting of olive trees in a crop field where the trees are regularly spaced from each other. During the flight, the embedded computer is able to process individual images on board the UAV and provide the total count. The tree counting algorithm obtains an F 1 score of 99 . 09 % for a sequence of ten images with 332 olive trees. The detected trees are geolocated and can be visualized on the Internet seconds after the take-off of the flight, with no further processing required. This is a use case to demonstrate near real-time results obtained from UAS usage. Other more complex UAS applications, such as tree inventories, search and rescue, fire detection, or stock breeding, can potentially benefit from this architecture and obtain faster outcomes, accessible while the UAV is still on flight.

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Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence

Cited by 97 publications

References 58 publications

Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion

Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion

Iterative Deep Neighborhood: A Deep Learning Model Which Involves Both Input Data Points and Their Neighbors

On-the-Fly Olive Tree Counting Using a UAS and Cloud Services

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