A Multiple Cascade-Classifier System for a Robust and Partially Unsupervised Updating of Land-Cover Maps

A system for a regular updating of land-cover maps is proposed that is based on the use of multitemporal remote-sensing images. Such a system is able to face the updating problem under the realistic but critical constraint that, for the image to be classified (i.e., the most recent of the considered multitemporal data set), no ground truth information is available. The system is composed of an ensemble of partially unsupervised classifiers integrated in a multiple classifier architecture. Each classifier of the ensemble exhibits the following novel peculiarities: i) it is developed in the framework of the cascade-classification approach to exploit the temporal correlation existing between images acquired at different times in the considered area; ii) it is based on a partially unsupervised methodology capable to accomplish the classification process under the aforementioned critical constraint. Both a parametric maximum-likelihood classification approach and a non-parametric radial basis function (RBF) neural-network classification approach are used as basic methods for the development of partially unsupervised cascade classifiers. In addition, in order to generate an effective ensemble of classification algorithms, hybrid maximum-likelihood and RBF neural network cascade classifiers are defined by exploiting the peculiarities of the cascade-classification methodology. The results yielded by the different classifiers are combined by using standard unsupervised combination strategies. This allows the definition of a robust and accurate partially unsupervised classification system capable of analyzing a wide typology of remote-sensing data (e.g., images acquired by passive sensors, SAR images, multisensor and multisource data). Experimental results obtained on a real multitemporal and multisource data set confirm the effectiveness of the proposed system

Combining Parametric and Non-parametric Algorithms for a Partially Unsupervised Classification of Multitemporal Remote-Sensing Images

In this paper, we propose a classification system based on a multiple-classifier architecture, which is aimed at updating land-cover maps by using multisensor and/or multisource remote-sensing images. The proposed system is composed of an ensemble of classifiers that, once trained in a supervised way on a specific image of a given area, can be retrained in an unsupervised way to classify a new image of the considered site. In this context, two techniques are presented for the unsupervised updating of the parameters of a maximum-likelihood (ML) classifier and a radial basis function (RBF) neural-network classifier, on the basis of the distribution of the new image to be classified. Experimental results carried out on a multitemporal and multisource remote-sensing data set confirm the effectiveness of the proposed system

A system for monitoring NO2 emissions from biomass burning by using GOME and ATSR-2 data

In this paper, we propose a system for monitoring abnormal NO2 emissions in troposphere by using remote-sensing sensors. In particular, the system aims at estimating the amount of NO2 resulting from biomass burning by exploiting the synergies between the GOME and the ATSR-2 sensors mounted on board of the ERS-2 satellite. Two different approaches to the estimation of NO2 are proposed: the former, which is the simplest one, assumes a linear relationship between the GOME and ATSR-2 measurements and the NO2 concentration. The latter exploits a nonlinear and nonparametric method based on a radial basis function (RBF) neural network. The architecture of such a network is defined in order to retrieve the values of NO2 concentration on the basis of the GOME and ATSR-2 measurements, as well as of other ancillary input parameters. Experimental results, obtained on a real data set, confirm the effectiveness of the proposed system, which represents a promising tool for operational applications

Landfill aeration for emission control before and during landfill mining

The landfill of Modena, in northern Italy, is now crossed by the new high velocity railway line connecting Milan and Bologna. Waste was completely removed from a part of the landfill and a trench for the train line was built. With the aim of facilitating excavation and further disposal of the material extracted, suitable measures were defined. In order to prevent undesired emissions into the excavation area, the aerobic in situ stabilisation by means of the Airflow technology took place before and during the Landfill Mining. Specific project features involved the pneumatic leachate extraction from the aeration wells (to keep the leachate table low inside the landfill and increase the volume of waste available for air migration) and the controlled moisture addition into a limited zone, for a preliminary evaluation of the effects on process enhancement. Waste and leachate were periodically sampled in the landfill during the aeration before the excavation, for quality assessment over time; the evolution of biogas composition in the landfill body and in the extraction system for different plant set-ups during the project was monitored, with specific focus on uncontrolled migration into the excavation area. Waste biological stability significantly increased during the aeration (waste respiration index dropped to 33% of the initial value after six months). Leachate head decreased from 4 to 1.5m; leachate recirculation tests proved the beneficial effects of moisture addition on temperature control, without hampering waste aerobization. Proper management of the aeration plant enabled the minimization of uncontrolled biogas emissions into the excavation area

A morphometric study of human submandibular gland in type 2 diabetic status

Diabetes Mellitus Type 2 represents one of the principal diseases that afflict the world population. It is well documented that diabetes affects both morphology and function of several organs. In diabetic rats significant structural changes have been demonstrated in salivary glands, such as accumulation of secretory material and lipid droplets within secretory cells, parenchymal degeneration and its replacement with fibrous connective tissue (1). With regard to human salivary glands, the data are scanty and conflicting. Our work, carried out by light and electron microscopy, is based on the evaluation of the morphological changes which occur in human submandibular glands of diabetic with respect to non diabetic patients. Surgical fragments of glandular tissue were fixed, dehydrated, and processed for light and electron microscopy. Randomly chosen images were analyzed with Image Pro Plus software to record the dimension of acini, serous cells, secretory granules and other variables. Data were analyzed by Student’s t-test and Mann Whitney test. In diabetic glands statistically significant morphological changes were observed, such as enlargement of serous acini and increase of secretory granules area. These results suggest that the secretory activity of human submandibular gland is severely affected by the diabetic status. Obviously these data need to be confirmed with further measurements in order to explain better how diabetes affects human salivary glands

Utility Scale Ground Mounted Photovoltaic Plants with Gable Structure and Inverter Oversizing for Land-Use Optimization

The paper proposes an effective layout for ground-mounted photovoltaic systems with a gable structure and inverter oversizing, which allows an optimized use of the land and, at the same time, guarantees a valuable return on investment. A case study is presented to show the technical, economic, and environmental advantages compared with conventional “fixed-tilt” and “sun-tracking” ground-mounted photovoltaic installations. The main advantage of this solution is that it maximizes the energy produced per unit of land area used; but, also considering the economic metrics, the net present value of the proposed PV arrangement solution results in a greater annual volume of energy produced and therefore of net revenues and cash flows, and greater than the compared conventional solution with modules exposed in an optimal fixed position or which make use of sun-tracking systems

A discussion on cell therapy in Manchester

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Ricognizione delle specie di Diptera Culicidae della Sardegna Nord-orientale

The results of a mosquito management programme in the North of Sardinia are here reported. Adult mosquito populations were monitored by portable light traps, baited with carbon dioxide. The traps gave an indication of mosquito abundance and species composition. Monitoring sites were located in the main resorts along the cost and checked on a weekly basis from June through October. The plan includes a clear communication system for advising the public of any potential public health threa

Models of the Unimodal and Multimodal Statistics of Adaptive Wavelet Packet Coefficients

In recent work, it was noted that although the subband histograms for standard wavelet coefficients take on a generalized Gaussian form, this is no longer true for wavelet packet bases adapted to a given texture. Instead, three types of subband statistics are observed: Gaussian, generalized Gaussian, and most interestingly, in some subbands, multimodal histograms with no mode at zero. As will be demonstrated in this report, these latter subbands are closely linked to the structure of the texture, and are thus likely to be important for many applications in which texture plays a role. Motivated by these observations, we extend the approach to texture modelling proposed by to include these subbands. We relax the Gaussian assumption to include generalized Gaussians and constrained Gaussian mixtures. We use a Bayesian methodology, finding MAP estimates for the adaptive basis, for subband model selection, and for subband model parameters. Results confirm the effectiveness of the proposed approach, and highlight the importance of multimodal subbands for texture discrimination and modelling

Applicazione del laser scanner terrestre per la valutazione della condizione delle chiome in Quercus suber L.

The objective of this paper is to show the first results on the evaluation of the possible use of this technique for the estimation of crown condition in cork oak stands (Quercus suber L.). The results show that the TLS (Terrestrial laser Scanner) technology has good potential applications for the deciduous woodlands. The next objective will be to evaluate the performance of this approach in the estimation of the damage caused by insects