Artificial Neural Network Approach for Land Cover Classification of Fused Hyperspectral and Lidar Data

Part 7: Intelligent Signal and Image ProcessingInternational audienceHyperspectral remote sensing images are consisted of several hundreds of contiguous spectral bands that can provide very rich information and has the potential to differentiate land cover classes with similar spectral characteristics. LIDAR data gives detailed height information and thus can be used complementary with Hyperspectral data. In this work, a hyperspectral image is combined with LIDAR data and used for land cover classification. A Principal Component Analysis (PCA) is applied on the Hyperspectral image to perform feature extraction and dimension reduction. The first 4 PCA components along with the LIDAR image were used as inputs to a supervised feedforward neural network. The neural network was trained in a small part of the dataset (less than 0.4%) and a validation set, using the Bayesian regularization backpropagation algorithm. The experimental results demonstrate efficiency of the method for hyperspectral and LIDAR land cover classification

A Social Environmental Sensor Network Integrated within a Web GIS Platform

We live in an era where typical measures towards the mitigation of environmental degradation follow the identification and recording of natural parameters closely associated with it. In addition, current scientific knowledge on the one hand may be applied to minimize the environmental impact of anthropogenic activities, whereas informatics on the other, playing a key role in this ecosystem, do offer new ways of implementing complex scientific processes regarding the collection, aggregation and analysis of data concerning environmental parameters. Furthermore, another related aspect to consider is the fact that almost all relevant data recordings are influenced by their given spatial characteristics. Taking all aforementioned inputs into account, managing such a great amount of complex and remote data requires specific digital structures; these structures are typically deployed over the Web on an attempt to capitalize existing open software platforms and modern developments of hardware technology. In this paper we present an effort to provide a technical solution based on sensing devices that are based on the well-known Arduino platform and operate continuously for gathering and transmitting of environmental state information. Controls, user interface and extensions of the proposed project rely on the Android mobile device platform (both from the software and hardware side). Finally, a crucial novel aspect of our work is the fact that all herein gathered data carry spatial information, which is rather fundamental for the successful correlation between pollutants and their place of origin. The latter is implemented by an interactive Web GIS platform operating oversight in situ and on a timeline basis

Delineation of Lineaments from Satellite Data Based on Efficient Neural Network and Pattern Recognition Techniques

Abstract: An automated lineament detection method based on a modified Hough transform is presented. The method first performs an efficient data clustering using Kohonen’s self-organizing maps then binarizes the classification result and finally applies the modified Hough transform in order to identify lineaments. The capabilities of the method are described using Landsat TM satellite data from the Vermion area in Greece. The results of the automated analysis show major geological faults in the selected area.

Application of Artificial Neural Networks on improving predictions of nuclear radii

A poster presented at HNPS2018, 8-9.06.2018<br