A SVM and k-NN Restricted Stacking to Improve Land Use and Land Cover Classification

Land use and land cover (LULC) maps are remote sensing products that are used to classify areas into different landscapes. The newest techniques have been applied to improve the final LULC classification and most of them are based on SVM classifiers. In this paper, a new method based on a multiple classifiers ensemble to improve LULC map accuracy is shown. The method builds a statistical raster from LIDAR and image fusion data following a pixel-oriented strategy. Then, the pixels from a training area are used to build a SVM and k-NN restricted stacking taking into account the special characteristics of spatial data. A comparison between a SVM and the restricted stacking is carried out. The results of the tests show that our approach improves the results in the context of the real data from a riparian area of Huelva (Spain)

Magnetic heat conductivity in CaCu2O3\rm\bf CaCu_2O_3: linear temperature dependence

We present experimental results for the thermal conductivity $\kappa$ of the pseudo 2-leg ladder material $\rm CaCu_2O_3$. The strong buckling of the ladder rungs renders this material a good approximation to a $S=1/2$ Heisenberg-chain. Despite a strong suppression of the thermal conductivity of this material in all crystal directions due to inherent disorder, we find a dominant magnetic contribution $\kappa_\mathrm{mag}$ along the chain direction. $\kappa_\mathrm{mag}$ is \textit{linear} in temperature, resembling the low-temperature limit of the thermal Drude weight $D_\mathrm{th}$ of the $S=1/2$ Heisenberg chain. The comparison of $\kappa_\mathrm{mag}$ and $D_\mathrm{th}$ yields a magnetic mean free path of $l_\mathrm{mag}\approx 22 \pm 5$ \AA, in good agreement with magnetic measurements.Comment: appears in PR

Role of disorder when upscaling magnetocaloric Ni-Co-Mn-Al Heusler alloys from thin films to ribbons

Research in functional magnetic materials often employs thin films as model systems for finding new chemical compositions with promising properties. However, the scale-up of thin films towards bulk-like structures is challenging, since the material synthesis conditions are entirely different for thin films and e.g. rapid quenching methods. As one of the consequences, the type and degree of order in thin films and melt-spun ribbons are usually different, leading to different magnetic properties. In this work, using the example of magnetocaloric Ni-Co-Mn-Al melt-spun ribbons and thin films, we show that the excellent functional properties of the films can be reproduced also in ribbons, if an appropriate heat treatment is applied, that installs the right degree of order in the ribbons. We show that some chemical disorder is needed to get a pronounced and sharp martensitic transition. Increasing the order with annealing improves the magnetic properties only up to a point where selected types of disorder survive, which in turn compromise the magnetic properties. These findings allow us to understand the impact of the type and degree of disorder on the functional properties, paving the way for a faster transfer of combinatorial thin film research towards bulk-like materials for magnetic Heusler alloys

Predicting the dominating factors during heat transfer in magnetocaloric composite wires

Magnetocaloric composite wires have been studied by pulsed-field measurements up to μ0ΔH = 10 T with a typical rise time of 13 ms in order to evaluate the evolution of the adiabatic temperature change of the core, ΔTad, and to determine the effective temperature change at the surrounding steel jacket, ΔTeff, during the field pulse. An inverse thermal hysteresis is observed for ΔTad due to the delayed thermal transfer. By numerical simulations of application-relevant sinusoidal magnetic field profiles, it can be stated that for field-frequencies of up to two field cycles per second heat can be efficiently transferred from the core to the outside of the jacket. In addition, intense numerical simulations of the temperature change of the core and jacket were performed by varying different parameters, such as frequency, heat capacity, thermal conductivity and interface resistance in order to shed light on their impact on ΔTeff at the outside of the jacket in comparison to ΔTad provided by the core

Neutron diffraction study of the inverse spinels Co2TiO4 and Co2SnO4

We report a detailed single crystal and powder neutron diffraction study of Co2TiO4 and Co2SnO4 between the temperature 1.6 and 80K to probe the spin structure in the ground state. For both compounds the strongest magnetic intensity was observed for the 111 M reflection due to ferrimagnetic ordering, which sets in below TN 48.6 and 41 K for Co2TiO4 and Co2SnO4, respectively. An additional low intensity magnetic reflection 200 M was noticed in Co2TiO4 due to the presence of an additional weak antiferromagnetic component. Interestingly, from both the powder and single crystal neutron data of Co2TiO4, we noticed a significant broadening of the magnetic 111 M reflection, which possibly results from the disordered character of the Ti and Co atoms on the B site. Practically, the same peak broadening was found for the neutron powder data of Co2SnO4. On the other hand, from our single crystal neutron diffraction data of Co2TiO4, we found a spontaneous increase of particular nuclear Bragg reflections below the magnetic ordering temperature. Our data analysis showed that this unusual effect can be ascribed to the presence of anisotropic extinction, which is associated to a change of the mosaicity of the crystal. In this case, it can be expected that competing Jahn Teller effects acting along different crystallographic axes can induce anisotropic local strain. In fact, for both ions Ti3 and Co3 , the 2tg levels split into a lower dxy level yielding a higher twofold degenerate dxz dyz level. As a consequence, one can expect a tetragonal distortion in Co2TiO4 with c a lt; 1, which we could not significantly detect in the present wor

Can I trust my one-class classification?

Contrary to binary and multi-class classifiers, the purpose of a one-class classifier for remote sensing applications is to map only one specific land use/land cover class of interest. Training these classifiers exclusively requires reference data for the class of interest, while training data for other classes is not required. Thus, the acquisition of reference data can be significantly reduced. However, one-class classification is fraught with uncertainty and full automatization is difficult, due to the limited reference information that is available for classifier training. Thus, a user-oriented one-class classification strategy is proposed, which is based among others on the visualization and interpretation of the one-class classifier outcomes during the data processing. Careful interpretation of the diagnostic plots fosters the understanding of the classification outcome, e.g., the class separability and suitability of a particular threshold. In the absence of complete and representative validation data, which is the fact in the context of a real one-class classification application, such information is valuable for evaluation and improving the classification. The potential of the proposed strategy is demonstrated by classifying different crop types with hyperspectral data from Hyperion

Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions

Land use and land cover maps can support our understanding of coupled human-environment systems and provide important information for environmental modeling and water resource management. Satellite data are a valuable source for land use and land cover mapping. However, cloud-free or weather independent data are necessary to map cloud-prone regions. This particularly applies to monsoon regions such as the Chennai basin, located in the north of Tamil Nadu and the south of Andhra Pradesh, India, which is influenced by the South Asian Monsoon and has abundant cloud cover, throughout the monsoon season. The Basin is characterized by small-scale agriculture with multiple cropping seasons and the rapidly developing metropolitan area of Chennai. This study aims to generate a land use and land cover map of the Chennai Basin for the cropping season of Rabi 2015/16 and to assess the influence of combining the new ESA Copernicus satellites Sentinel-1 and -2 on classification accuracies. A Random Forest based wrapper approach was applied to select the most relevant radar (Sentinel-1) images for the combination with the optical (Sentinel-2) data. Area proportion weighted accuracy with 95% confidence interval were estimated for the Random Forest models, which differentiated 13 land cover classes. The highest overall accuracy of 91.53% ± 0.89 pp was achieved with a combination of 1 Sentinel-2 and 8 Sentinel-1 scenes. This is an improvement of 5.68 pp over a classification with Sentinel-2 data only. An addition of further Sentinel-1 scenes showed no improvement in overall accuracy. The strongest improvement in class-specific accuracy was achieved for paddy fields. This study shows for the first time how land use and land cover classifications in cloud-prone monsoon regions with small-scale agriculture and multiple cropping patterns can be improved by combining Sentinel-1 and Sentinel-2 data