Artificial Neural Networks

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, …

Satellite Computer Vision mit Keras und Tensorflow - Best Practices und Beispiele aus der Forschung

Im Forschungsfeld des Maschinellen Lernens werden zunehmend leicht zugängliche Framework wie Keras, Tensorflow oder Pytorch verwendet. Hierdurch ist ein Austausch und die Wiederverwendung bestehender (trainierter) neuronaler Netze möglich. --- Wir am …

Examples for CNN training and classification on Sentinel-2 data

Overview about state-of-the-art land-use classification from satellite data with CNNs based on an open dataset.

Satellite data is for everyone: insights into modern remote sensing research with open data and Python

The largest earth observation programme Copernicus (http://copernicus.eu) makes it possible to perform terrestrial observations providing data for all kinds of purposes. One important objective is to monitor the land-use and land-cover changes with …

Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data

In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with IR data. Measurements from a multi-sensor field campaign represent the benchmark dataset which contains measured …

Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae, and Turbidity

Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters …