A simple geometrical model for analysing the spectral response of a citrus canopy using satellite images †

Abstract

A simple geometrical model has been proposed for a citrus canopy. We assume the citrus orchard to be a lattice structure, with the trees positioned at its points and where the composite-scene reflectance is the sum of the reflectance of its individual components as weighted by their respective surfaces within a unit area. The model has been used to analyse the citrus spectral response obtained from Landsat-5 TM images for winter and summer, where the status of the orchard is different. The correlations between spectral and geometrical data show the influence of per cent crop cover, shadows and background in the composite scene reflectance. We conclude that the summer images could be more useful than the winter ones for parcel classification according to per cent crop cover.     

Improving spectral results in a GTS context

This paper shows the advantages of post-processing spectral classifications in a Geographical Information System (GIS) context in order to improve results. A maximum-likelihood algorithm was used to classify(both supervised and non-supervised) a Landsat TM sub-image in Central Mexico. Purely spectral processing yielded poor accuracy results, showing the spectral limitation to distinguish classes; as a consequence, merging classes was necessary in order to increase accuracy (from less than 55 to 82 per cent). GIS rules were finally applied based on ancillary data (terrain mapping units and elevation data) improving the final accuracy to 88.2 and 83.0 per cent (supervised and non-supervised classifications).     

Land-use classification in central Spain using SIR-A and MSS imagery

Abstract

Methods for making more efficient use of synthetic aperture radar (SAR) imagery are considered. Local standard deviation and autocorrelation texture measures are used to provide information on the spatial variability in the scattering cross-section. Use of these statistics in a window of 180×180m improved classification success rates from 39 to 66 per cent with digitized shuttle imaging radar (S1R-A) data. Multispectral scanner (MSS) achieves 70 per cent success with the same window size and, by combining this with SAR, a 78 per cent success rate is reached.     

Extreme Learning Machines for Feature Selection and Classification of Cocaine Dependent Patients on Structural MRI Data

In this paper, we present a Computer Aided Diagnosis and image biomarker identification system for cocaine dependence, which selects relevant regions from a set of brain structural magnetic resonance images (sMRI). After sMRI volume preprocessing for spatial normalization, we compute Pearson’s correlation between pixel values across volumes and the indicative variable, obtaining a volume of correlation values (VCV). We calculate the gradient of the VCV which is used to perform a watershed segmentation of the brain volume into regions. A region selection stage finds the most relevant watershed regions. We propose two different approaches to characterize region relevance: (a) a wrapper procedure using extreme learning machines (ELM), and (b) apply correlation distribution percentiles to select most discriminant regions. Next, we consider three different procedures to extract the image features corresponding to selected regions: (1) collecting the sMRI intensity values of all the voxels that compose each region, compute (2) the mean or (3) the median of the sMRI intensity value of the voxels contained in each selected region. Extracted feature vectors are used to build a classifier aiming to discriminate between cocaine dependent patients and healthy controls. We compare results of several classifiers: ELM, OP-ELM, SVM and 1NN. Also, we visualize the brain locations of selected regions, checking if these locations are in accordance with previous findings in the medical literature.     

Induction heating of mastic containing conductive fibers and fillers

The objective of this research is to examine the induction heating of mastic through the addition of electrically conductive fillers and fibers (graphite and steel wool), and to prove that this material can be healed with induction energy. The effect of fibers content, sand–bitumen ratio and the combination of fillers and fibers on the induction heating of mastic was investigated. It was found that there is an optimum content of fibers for each sand–bitumen ratio, above which mastic cannot be heated any more. This optimum seems to coincide with the optimum electrical conductivity of the mixture shown in [1]. It was found that the maximum temperature reached within a certain time period was a function of the sand–bitumen ratio (s–b) and of the volume content of fibers. The mastic could be heated with the addition of a very low volume of conductive fibers. The fastest heating power was obtained with the mix with the maximum electrical conductivity. Gel-Permeation Chromatography (GPC) was also used to show that there is not ageing of bitumen during the heating process.     

To the understanding of the formation of the III–V based droplet epitxial nanorings

The paper deals with the kinetics of the droplet epitaxial GaAs quantum ring formation grown on AlGaAs (0 0 1) surface. The observation is, that the aspect ratio of these nano structures is depends not only on the technological parameters but on the size of the initial droplet. Under appropriate growth conditions, the depressions, in the middle of the rings are deeper than the surface level of the substrate. A large number of tests show, that the depressions in the middle of the small rings are often deeper than that of the larger ones. The number is larger, than just statistical fluctuation. An explanation for this phenomenon and its kinetics are given in the paper, based on the size dependence of the material properties, like for instance solubility. The plausible explanation assumes is that the probability of the crystal seed formation in the larger droplets is higher.     

Evidence of Oxygen Ferromagnetism in ZnO Based Materials

Discoveries of room‐temperature ferromagnetism (RTFM) in semiconductors hold great promise in future spintronics technologies. Unfortunately, this ferromagnetism remains poorly understood and the debate concerning the nature, carrier‐mediated versus defect‐mediated, of this ferromagnetism in semiconducting oxides is still open. Here, by using X‐ray absorption (XAS) and X‐ray magnetic circular dichroism (XMCD), it is demonstrated that the oxygen ions have a ferromagnetic response in different ZnO‐based compounds showing RTFM behavior: ZnO nanoparticles capped with organic molecules and ZnO/ZnS heterostructures. These results demonstrate the intrinsic occurrence of RTFM in these systems, and point out that it is not related to the metallic cation but it relays on the conduction band of the semiconductor.