An Adaptive Semi-Parametric and Context-Based Approach to Unsupervised Change Detection in Multitemporal Remote-Sensing Images

In this paper, a novel automatic approach to the unsupervised identification of changes in multitemporal remote-sensing images is proposed. This approach, unlike classical ones, is based on the formulation of the unsupervised change-detection problem in terms of the Bayesian decision theory. In this context, an adaptive semi-parametric technique for the unsupervised estimation of the statistical terms associated with the gray levels of changed and unchanged pixels in a difference image is presented. Such a technique exploits the effectivenesses of two theoretically well-founded estimation procedures: the reduced Parzen estimate (RPE) procedure and the expectation-maximization (EM) algorithm. Then, thanks to the resulting estimates and to a Markov Random Field (MRF) approach used to model the spatial-contextual information contained in the multitemporal images considered, a change detection map is generated. The adaptive semi-parametric nature of the proposed technique allows its application to different kinds of remote-sensing images. Experimental results, obtained on two sets of multitemporal remote-sensing images acquired by two different sensors, confirm the validity of the proposed approach

A partially unsupervised cascade classifier for the analysis of multitemporal remote-sensing images

A partially unsupervised approach to the classification of multitemporal remote-sensing images is presented. Such an approach allows the automatic classification of a remote-sensing image for which training data are not available, drawing on the information derived from an image acquired in the same area at a previous time. In particular, the proposed technique is based on a cascade classifier approach and on a specific formulation of the expectation-maximization (EM) algorithm used for the unsupervised estimation of the statistical parameters of the image to be classified. The results of experiments carried out on a multitemporal data set confirm the validity of the proposed approach

OPTIMAL PRICING AND GRANT POLICIES FOR MUSEUMS

Considering two potential sources of income (public grants and ticket revenues),we have defined a theoretical model where the public agency is the principal and the manager of the museum is the agent. This model allows us to design the optimal contract between both sides and thus to establish the optimal values of grants, ticket prices, budget and effort applied by the manager. Furthermore, we have found a theoretical reason to explain the inelastic pricing strategy that has been found in some of the empirical research on cultural and sports economics. The main conclusion is that the optimal contract allows a Pareto optimum solution in prices that does not change if we introduce moral hazard into this relationship. This solution allows us to conclude that the public agency should regulate ticket prices in accordance with the social valuation. However, public grants and museum budgets would be affected by the existence of this problem, moving the equilibrium away from the Pareto optimum situation. In this case, even with a risk averse manager and a risk neutral public agency, grants and budgets will depend on results because higher budgets related to good results provide the main incentives to increase the manager’s level of effort. Although the focus of this paper is on museum administration, the model that we have developed can be easily generalized and applied to other institutions, such as schools, sport facilities or NGOs, which are able to raise funds directly from private (e. g. ticket revenues or membership fees) or public sources (e.g. public grants).cultural economics, grants, public prices, museums, principal- agent model

TIGER Capacity Building Facility - Phase 1, lessons learnt

The TIGER Capacity Building Facility focused on closing the technological gap between the users and the earth observation community within the TIGER programme. Thirteen projects participated in different aspects of the capacity building facility: 1. - Basic education, provided via distance learning. 2. - Tailored short courses, selected according to the research interest and technical background of the participants. 3. - Research topic oriented supervision, provided by specialists of the research fields of the participants. 4. - Advanced short courses focusing on selected earth observation techniques. Distance education turned to be efficient and cost effective in the programme - but only for those, who followed the courses completely. There was a relatively large percentage that could not complete the studies. The second and the third type of education were carried out in ITC, in the Netherlands. The participants evaluated the courses and the supervision very effective and adequate. Nevertheless, the follow-up was not always possible. Two advanced short courses were held in Africa (Cape Town and Nairobi). One of them addressed the 'scientific elite' of the EO community, whilst the second focused on the users of this technology

Deep learning phase picking of large-N experiments

The popularisation of the use of large-N arrays of seismometers has resulted in a significant increase of the size of the datasets recorded during these experiments. Therefore, new challenges have arisen on how to process all these data efficiently, and in an automated fashion. This is particularly true in the case of induced seismicity monitoring, where often a large number of number of events are recorded at high frequency sampling rates. Several methods of automatic picking have been developed during recent years, from triggering algorithms (e.g. STA/LTA) to higher order statistics or waveform similarity. Latest development in computational power and the popularization of GPUs have made possible to apply machine learning methods to several problems, from arrival picking and phase detection to earthquake location. We have developed a deep neural network to detect the arrivals of seismic body waves, using an architecture based on convolutional layers. This type of models are widely used in computer vision applications, which is the most similar case to the phase picking by an operator. Trained with the data of the Southern California Seismic Network, this network is able to differentiate P and S waves from background noise with a precision higher than 98%. We have applied this neural network to other large-N experiments in other regions (Europe and Asia) and found that the network localizes the events with a precision comparable or superior to an human operator, even in the case of low signal-noise ratio and superposition of earthquakes.This research has been funded by MICINN Project CGL2017-88864-

How do your rivals' releasing dates affect your box office?

In this paper, we study to what extent a movie's box office receipts are affected by the temporal distribution of rival films. We propose a theoretical model that analyses the effects of past, present and future releases on a film's results. Using this model we can analyse how rivals' release dates impact on others' box office revenues. This theoretical model also allows us to carry out some comparative statics by changing some relevant parameters such as time depreciation, film quality or the timeline of exhibition. We have tested the empirical implications of this model using information on the films released in five countries: the USA, the United Kingdom, Germany, France and Spain. In order to maintain a degree of homogeneity, we have constructed an unbalanced panel consisting of films that were released in at least three of these countries. The geographical dimension of our data set allows us to use panel data techniques to control for unobserved heterogeneity among the films released. This allows us to control for one of the most relevant features of the movie market, namely the presence of highly differentiated products.temporal competition, movie exhibition, film industry, panel data, unobserved heterogeneity, differentiated product

Smooth non linear high gain observers for a class of dynamical systems

High-gain observers are powerful tools for estimating the state of nonlinear systems. However, their design poses several challenges due to the need of dealing with phenomena such as peaking and chattering. To address these issues, we propose a differentiator operator design based on a non linear second order high-gain observer, which is suited to a class of dynamical systems. Our method includes a procedure to determine high gains in order to avoid chattering in the case of noise-free models, and cut-off frequency based gain design in the case of noisy measurements. Complementary, we suggest performing observability analyses to ensure a priori the feasibility of the estimation. The main strengths of our approach are its simplicity and robustness. We demonstrate the performance of the proposed method by applying it to two processes (chemical and biological).Xunta de Galicia | Ref. ED431F 2021/003MCIN/AEI/10.13039/501100011033 | Ref. RYC-2019-027537-

The abundance of 28Si32S, 29Si32S, 28Si34S, and 30Si32S in the inner layers of the envelope of IRC+10216

We present high spectral resolution mid-IR observations of SiS towards the C-rich AGB star IRC+10216 carried out with the Texas Echelon-cross-Echelle Spectrograph mounted on the NASA Infrared Telescope Facility. We have identified 204 ro-vibrational lines of 28Si32S, 26 of 29Si32S, 20 of 28Si34S, and 15 of 30Si32S in the frequency range 720-790 cm-1. These lines belong to bands v=1-0, 2-1, 3-2, 4-3, and 5-4, and involve rotational levels with Jlow<90. About 30 per cent of these lines are unblended or weakly blended and can be partially or entirely fitted with a code developed to model the mid-IR emission of a spherically symmetric circumstellar envelope composed of expanding gas and dust. The observed lines trace the envelope at distances to the star <35R* (~0.7 arcsec). The fits are compatible with an expansion velocity of 1+2.5(r/R*-1) km/s between 1 and 5R*, 11 km/s between 5 and 20R*, and 14.5 km/s outwards. The derived abundance profile of 28Si32S with respect to H2 is 4.9e-6 between the stellar photosphere and 5R*, decreasing linearly to 1.6e-6 at 20R* and to 1.3e-6 at 50R*. 28Si32S seems to be rotationally under LTE in the region of the envelope probed with our observations and vibrationally out of LTE in most of it. There is a red-shifted emission excess in the 28Si32S lines of band v=1-0 that cannot be found in the lines of bands v=2-1, 3-2, 4-3, and 5-4. This excess could be explained by an enhancement of the vibrational temperature around 20R* behind the star. The derived isotopic ratios 28Si/29Si, and 32S/34S are 17 and 14, compatible with previous estimates.Comment: 11 pages, 5 figures, and 4 tables. Accepted for publication in MNRA