Search results for "artificial"
showing 10 items of 7394 documents
Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis
2015
In this paper we present an approach to perform relative spectral alignment between optical cross-sensor acquisitions. The proposed method aims at projecting the images from two different and possibly disjoint input spaces into a common latent space, in which standard change detection algorithms can be applied. The system relies on the regularized kernel canonical correlation analysis transformation (kCCA), which can accommodate nonlinear dependencies between pixels by means of kernel functions. To learn the projections, the method employs a subset of samples belonging to the unchanged areas or to uninteresting radiometric differences. Since the availability of ground truth information to p…
Crop specific algorithms trained over ground measurements provide the best performance for GAI and fAPAR estimates from Landsat-8 observations
2021
Abstract Estimation of Green Area Index (GAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) from decametric satellites was investigated in this study using a large database of ground measurements over croplands. It covers six main crop types including rice, corn, wheat and barley, sunflower, soybean and other types of crops. Ground measurements were completed using either digital hemispherical cameras, LAI-2000 or AccuPAR devices over sites representative of a decametric pixel. Sites were spread over the globe and the data collected at several growth stages concurrently to the acquisition of Landsat-8 images. Several machine learning techniques were investigated to re…
Hyperspectral dimensionality reduction for biophysical variable statistical retrieval
2017
Abstract Current and upcoming airborne and spaceborne imaging spectrometers lead to vast hyperspectral data streams. This scenario calls for automated and optimized spectral dimensionality reduction techniques to enable fast and efficient hyperspectral data processing, such as inferring vegetation properties. In preparation of next generation biophysical variable retrieval methods applicable to hyperspectral data, we present the evaluation of 11 dimensionality reduction (DR) methods in combination with advanced machine learning regression algorithms (MLRAs) for statistical variable retrieval. Two unique hyperspectral datasets were analyzed on the predictive power of DR + MLRA methods to ret…
Empirical and physical estimation of Canopy Water Content from CHRIS/PROBA data
2013
20 páginas, 4 tablas, 7 figuras.
Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran
2021
The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses. In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors (altitude, slope degree, profile curvature, distance to river, aspect, plan curvature, distance to road, distance to fault, rainfall, geology and land-sue) to construct a geospatial database and divided the data into the training and the testing dataset. We then d…
Modelling Complex Volume Shape Using Ellipsoid: Application to Pore Space Representation
2017
Natural shapes have complex volume forms that are usually difficult to model using simple analytical equations. The complexity of the representation is due to the heterogeneity of the physical environment and the variety of phenomena involved. In this study we consider the representation of the porous media. Thanks to the technological advances in Computed Topography scanners, the acquisition of images of complex shapes becomes possible. However, and unfortunately, the image data is not directly usable for simulation purposes. In this paper, we investigate the modeling of such shapes using a piece wise approximation of image data by ellipsoids. We propose to use a split-merge strategy and a…
Predicting year of plantation with hyperspectral and lidar data
2017
This paper introduces a methodology for predicting the year of plantation (YOP) from remote sensing data. The application has important implications in forestry management and inventorying. We exploit hyperspectral and LiDAR data in combination with state-of-the-art machine learning classifiers. In particular, we present a complete processing chain to extract spectral, textural and morphological features from both sensory data. Features are then combined and fed a Gaussian Process Classifier (GPC) trained to predict YOP in a forest area in North Carolina (US). The GPC algorithm provides accurate YOP estimates, reports spatially explicit maps and associated confidence maps, and provides sens…
The paramount power of selection: From Darwin to Kauffman
1995
For approximately two decades now, the Darwinian interpretation of evolution has now been challenged in many ways. Modern criticisms make it difficult, even for the staunchest Darwinians, not to take a distance from Darwin’s bold phrases on the “power” of natural selection. Let me remind you of some famous declarations of Darwin on the subject: “It may be said that natural selection is daily and hourly scrutinising, throughout the world, every variation, even the slightest; rejecting that which is bad, preserving and adding up all that is good; silently and insensibly working, whenever and wherever opportunity offers, at the improvement of each organic being in relation to its organic and i…
Stage boundaries, global stratigraphy, and the time scale: towards a simplification
2004
International audience; This paper examines four facets of stratigraphic terminology and usage considered faulty and proposes corrective measures. The four perfectible areas are: (1) The system of dual nomenclature requiring discrete terminologies for the superpositional and temporal aspects of rock units. (2) The premise that a GSSP establishes the base of a stage as being coincident with the top of the preceding stage rather than simply defining it as the boundary between stages. (3) The rejection of supplementary (auxiliary) sections that would broaden the knowledge of a GSSP and enlarge the area in which it is easily usable. (4) The current dual system of nomenclature for Precambrian an…
Modelling forest decline using SMOS soil moisture and vegetation optical depth
2018
Global change is increasing the risk of forest decline worldwide, impacting carbon and water cycles. Hence, there is an urgent need for predicting forest decline occurrence. To that purpose, this study links forest decline events in Catalonia, detected by the DEBOSCAT forest monitoring program, with information from the Soil Moisture and Ocean Salinity (SMOS) satellite. Firstly, this study reviews the role of the SMOS soil moisture in a previous forest decline episode occurred in 2012, where the authors concluded that dry soils increased the probability of observing decline in broadleaved forests. Secondly, the present study detects that forest decline in 2012 and 2016 was linked to very dr…