Fusion of hyperspectral and ground penetrating radar to estimate soil moisture

In this contribution, we investigate the potential of hyperspectral data combined with either simulated ground penetrating radar (GPR) or simulated (sensor-like) soil-moisture data to estimate soil moisture. We propose two simulation approaches to extend a given multi-sensor dataset which contains sparse GPR data. In the first approach, simulated GPR data is generated either by an interpolation along the time axis or by a machine learning model. The second approach includes the simulation of soil-moisture along the GPR profile. The soil-moisture estimation is improved significantly by the fusion of hyperspectral and GPR data. In contrast, the combination of simulated, sensor-like soil-moisture values and hyperspectral data achieves the worst regression performance. In conclusion, the estimation of soil moisture with hyperspectral and GPR data engages further investigations.Comment: This work has been accepted to the IEEE WHISPERS 2018 conference. (C) 2018 IEE

Development and Applications of Machine Learning Methods for Hyperspectral Data

Die hyperspektrale Fernerkundung der Erde stützt sich auf Daten passiver optischer Sensoren, die auf Plattformen wie Satelliten und unbemannten Luftfahrzeugen montiert sind. Hyperspektrale Daten umfassen Informationen zur Identifizierung von Materialien und zur Überwachung von Umweltvariablen wie Bodentextur, Bodenfeuchte, Chlorophyll a und Landbedeckung. Methoden zur Datenanalyse sind erforderlich, um Informationen aus hyperspektralen Daten zu erhalten. Ein leistungsstarkes Werkzeug bei der Analyse von Hyperspektraldaten ist das Maschinelle Lernen, eine Untergruppe von Künstlicher Intelligenz. Maschinelle Lernverfahren können nichtlineare Korrelationen lösen und sind bei steigenden Datenmengen skalierbar. Jeder Datensatz und jedes maschinelle Lernverfahren bringt neue Herausforderungen mit sich, die innovative Lösungen erfordern. Das Ziel dieser Arbeit ist die Entwicklung und Anwendung von maschinellen Lernverfahren auf hyperspektrale Fernerkundungsdaten. Im Rahmen dieser Arbeit werden Studien vorgestellt, die sich mit drei wesentlichen Herausforderungen befassen: (I) Datensätze, welche nur wenige Datenpunkte mit dazugehörigen Ausgabedaten enthalten, (II) das begrenzte Potential von nicht-tiefen maschinellen Lernverfahren auf hyperspektralen Daten und (III) Unterschiede zwischen den Verteilungen der Trainings- und Testdatensätzen. Die Studien zur Herausforderung (I) führen zur Entwicklung und Veröffentlichung eines Frameworks von Selbstorganisierten Karten (SOMs) für unüberwachtes, überwachtes und teilüberwachtes Lernen. Die SOM wird auf einen hyperspektralen Datensatz in der (teil-)überwachten Regression der Bodenfeuchte angewendet und übertrifft ein Standardverfahren des maschinellen Lernens. Das SOM-Framework zeigt eine angemessene Leistung in der (teil-)überwachten Klassifikation der Landbedeckung. Es bietet zusätzliche Visualisierungsmöglichkeiten, um das Verständnis des zugrunde liegenden Datensatzes zu verbessern. In den Studien, die sich mit Herausforderung (II) befassen, werden drei innovative eindimensionale Convolutional Neural Network (CNN) Architekturen entwickelt. Die CNNs werden für eine Bodentexturklassifikation auf einen frei verfügbaren hyperspektralen Datensatz angewendet. Ihre Leistung wird mit zwei bestehenden CNN-Ansätzen und einem Random Forest verglichen. Die beiden wichtigsten Erkenntnisse lassen sich wie folgt zusammenfassen: Erstens zeigen die CNN-Ansätze eine deutlich bessere Leistung als der angewandte nicht-tiefe Random Forest-Ansatz. Zweitens verbessert das Hinzufügen von Informationen über hyperspektrale Bandnummern zur Eingabeschicht eines CNNs die Leistung im Bezug auf die einzelnen Klassen. Die Studien über die Herausforderung (III) basieren auf einem Datensatz, der auf fünf verschiedenen Messgebieten in Peru im Jahr 2019 erfasst wurde. Die Unterschiede zwischen den Messgebieten werden mit qualitativen Methoden und mit unüberwachten maschinellen Lernverfahren, wie zum Beispiel Principal Component Analysis und Autoencoder, analysiert. Basierend auf den Ergebnissen wird eine überwachte Regression der Bodenfeuchte bei verschiedenen Kombinationen von Messgebieten durchgeführt. Zusätzlich wird der Datensatz mit Monte-Carlo-Methoden ergänzt, um die Auswirkungen der Verschiebung der Verteilungen des Datensatzes auf die Regression zu untersuchen. Der angewandte SOM-Regressor ist relativ robust gegenüber dem Rauschen des Bodenfeuchtesensors und zeigt eine gute Leistung bei kleinen Datensätzen, während der angewandte Random Forest auf dem gesamten Datensatz am besten funktioniert. Die Verschiebung der Verteilungen macht diese Regressionsaufgabe schwierig; einige Kombinationen von Messgebieten bilden einen deutlich sinnvolleren Trainingsdatensatz als andere. Insgesamt zeigen die vorgestellten Studien, die sich mit den drei größten Herausforderungen befassen, vielversprechende Ergebnisse. Die Arbeit gibt schließlich Hinweise darauf, wie die entwickelten maschinellen Lernverfahren in der zukünftigen Forschung weiter verbessert werden können

Applying a Dynamical Systems Model and Network Theory to Major Depressive Disorder

Mental disorders like major depressive disorder can be seen as complex dynamical systems. In this study we investigate the dynamic behaviour of individuals to see whether or not we can expect a transition to another mood state. We introduce a mean field model to a binomial process, where we reduce a dynamic multidimensional system (stochastic cellular automaton) to a one-dimensional system to analyse the dynamics. Using maximum likelihood estimation, we can estimate the parameter of interest which, in combination with a bifurcation diagram, reflects the expectancy that someone has to transition to another mood state. After validating the proposed method with simulated data, we apply this method to two empirical examples, where we show its use in a clinical sample consisting of patients diagnosed with major depressive disorder, and a general population sample. Results showed that the majority of the clinical sample was categorized as having an expectancy for a transition, while the majority of the general population sample did not have this expectancy. We conclude that the mean field model has great potential in assessing the expectancy for a transition between mood states. With some extensions it could, in the future, aid clinical therapists in the treatment of depressed patients.Comment: arXiv admin note: text overlap with arXiv:1610.0504

Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data

Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on hyperspectral data. We develop and implement three 1-dimensional (1D) convolutional neural networks (CNN): the LucasCNN, the LucasResNet which contains an identity block as residual network, and the LucasCoordConv with an additional coordinates layer. Furthermore, we modify two existing 1D CNN approaches for the presented classification task. The code of all five CNN approaches is available on GitHub (Riese, 2019). We evaluate the performance of the CNN approaches and compare them to a random forest classifier. Thereby, we rely on the freely available LUCAS topsoil dataset. The CNN approach with the least depth turns out to be the best performing classifier. The LucasCoordConv achieves the best performance regarding the average accuracy. In future work, we can further enhance the introduced LucasCNN, LucasResNet and LucasCoordConv and include additional variables of the rich LUCAS dataset.Comment: Accepted to the ISPRS Geospatial Week 2019 in Enschede (NL

Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data

Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on hyperspectral data. We develop and implement three 1-dimensional (1D) convolutional neural networks (CNN): the LucasCNN, the LucasResNet which contains an identity block as residual network, and the LucasCoordConv with an additional coordinates layer. Furthermore, we modify two existing 1D CNN approaches for the presented classification task. The code of all five CNN approaches is available on GitHub (Riese, 2019). We evaluate the performance of the CNN approaches and compare them to a random forest classifier. Thereby, we rely on the freely available LUCAS topsoil dataset. The CNN approach with the least depth turns out to be the best performing classifier. The LucasCoordConv achieves the best performance regarding the average accuracy. In future work, we can further enhance the introduced LucasCNN, LucasResNet and LucasCoordConv and include additional variables of the rich LUCAS dataset

Deep Learning for Land Cover Change Detection

Land cover and its change are crucial for many environmental applications. This study focuses on the land cover classification and change detection with multitemporal and multispectral Sentinel-2 satellite data. To address the challenging land cover change detection task, we rely on two different deep learning architectures and selected pre-processing steps. For example, we define an excluded class and deal with temporal water shoreline changes in the pre-processing. We employ a fully convolutional neural network (FCN), and we combine the FCN with long short-term memory (LSTM) networks. The FCN can only handle monotemporal input data, while the FCN combined with LSTM can use sequential information (multitemporal). Besides, we provided fixed and variable sequences as training sequences for the combined FCN and LSTM approach. The former refers to using six defined satellite images, while the latter consists of image sequences from an extended training pool of ten images. Further, we propose measures for the robustness concerning the selection of Sentinel-2 image data as evaluation metrics. We can distinguish between actual land cover changes and misclassifications of the deep learning approaches with these metrics. According to the provided metrics, both multitemporal LSTM approaches outperform the monotemporal FCN approach, about 3 to 5 percentage points (p.p.). The LSTM approach trained on the variable sequences detects 3 p.p. more land cover changes than the LSTM approach trained on the fixed sequences. Besides, applying our selected pre-processing improves the water classification and avoids reducing the dataset effectively by 17.6%. The presented LSTM approaches can be modified to provide applicability for a variable number of image sequences since we published the code of the deep learning models. The Sentinel-2 data and the ground truth are also freely available

Supervised and Semi-Supervised Self-Organizing Maps for Regression and Classification Focusing on Hyperspectral Data

Machine learning approaches are valuable methods in hyperspectral remote sensing, especially for the classification of land cover or for the regression of physical parameters. While the recording of hyperspectral data has become affordable with innovative technologies, the acquisition of reference data (ground truth) has remained expensive and time-consuming. There is a need for methodological approaches that can handle datasets with significantly more hyperspectral input data than reference data. We introduce the Supervised Self-organizing Maps (SuSi) framework, which can perform unsupervised, supervised and semi-supervised classification as well as regression on high-dimensional data. The methodology of the SuSi framework is presented and compared to other frameworks. Its different parts are evaluated on two hyperspectral datasets. The results of the evaluations can be summarized in four major findings: (1) The supervised and semi-Supervised Self-organizing Maps (SOM) outperform random forest in the regression of soil moisture. (2) In the classification of land cover, the supervised and semi-supervised SOM reveal great potential. (3) The unsupervised SOM is a valuable tool to understand the data. (4) The SuSi framework is versatile, flexible, and easy to use. The SuSi framework is provided as an open-source Python package on GitHub

Long-term changes of hydrogen-containing species in the stratosphere

Understanding the 1% per year increase of stratospheric water vapour from 1954 to 2000 is a great challenge in atmospheric science. The increase is predominantly caused by long-term changes in transport of water vapour into the stratosphere and systematic increases of tropospheric methane levels. This paper gives a review on stratospheric water vapour changes for the 1980 and 2000 time period with emphasis on the contribution of methane oxidation. Predictions for 2050 indicate that likely increases of tropospheric methane levels will lead to an increase of upper stratospheric water vapour values of about 0.4 ppmv. A similar value is predicted as an upper limit of effects of a future hydrogen economy. (c) 2006 Elsevier Ltd. All rights reserved