Investigating 2d and 3d convolutions for multitemporal land cover classification using remote sensing images

With the availability of large amounts of satellite image time series (SITS), the identification of different materials of the Earth's surface is possible with a high temporal resolution. One of the basic tasks is the pixel-wise classification of land cover, i.e.The task of identifying the physical material of the Earth's surface in an image. Fully convolutional neural networks (FCN) are successfully used for this task. In this paper, we investigate different FCN variants, using different methods for the computation of spatial, spectral, and temporal features. We investigate the impact of 3D convolutions in the spatial-Temporal as well as in the spatial-spectral dimensions in comparison to 2D convolutions in the spatial dimensions only. Additionally, we introduce a new method to generate multitemporal input patches by using time intervals instead of fixed acquisition dates. We then choose the image that is closest in time to the middle of the corresponding time interval, which makes our approach more flexible with respect to the requirements for the acquisition of new data. Using these multi-Temporal input patches, generated from Sentinel-2 images, we improve the classification of land cover by 4% in the mean F1-score and 1.3% in the overall accuracy compared to a classification using mono-Temporal input patches. Furthermore, the usage of 3D convolutions instead of 2D convolutions improves the classification performance by a small amount of 0.4% in the mean F1-score and 1.2% in the overall accuracy

Automatically generated training data for land cover classification with cnns using sentinel-2 images

Pixel-wise classification of remote sensing imagery is highly interesting for tasks like land cover classification or change detection. The acquisition of large training data sets for these tasks is challenging, but necessary to obtain good results with deep learning algorithms such as convolutional neural networks (CNN). In this paper we present a method for the automatic generation of a large amount of training data by combining satellite imagery with reference data from an available geospatial database. Due to this combination of different data sources the resulting training data contain a certain amount of incorrect labels. We evaluate the influence of this so called label noise regarding the time difference between acquisition of the two data sources, the amount of training data and the class structure. We combine Sentinel-2 images with reference data from a geospatial database provided by the German Land Survey Office of Lower Saxony (LGLN). With different training sets we train a fully convolutional neural network (FCN) and classify four land cover classes (code Building, Agriculture, Forest, Water/code). Our results show that the errors in the training samples do not have a large influence on the resulting classifiers. This is probably due to the fact that the noise is randomly distributed and thus, neighbours of incorrect samples are predominantly correct. As expected, a larger amount of training data improves the results, especially for the less well represented classes. Other influences are different illuminations conditions and seasonal effects during data acquisition. To better adapt the classifier to these different conditions they should also be included in the training data. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Anisotropic multi-element polynomial chaos expansion for high-dimensional non-linear structural problems

International audienc

Fahrverhalten von Pkw bei Naesse. - Untersuchung des Fahrverhaltens von Pkw bei Kurvenfahrt. - Ermittlung zulaessiger Amplituden und Wellenlaengen unter den Gesichtspunkten Strassen- und Ladegutbeanspruchung, Fahrsicherheit und Schwingempfinden (Loesungsansaetze)

TIB Hannover: ZA 4681(466) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman