Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

The Smartcanula: a new tool for remote access perfusion in limited access cardiac surgery.

Devices for venous cannulation have seen significant progress over time: the original, rigid steel cannulas have evolved toward flexible plastic cannulas with wire support that prevents kinking, very thin walled wire wound cannulas allowing for percutaneous application, and all sorts of combinations. In contrast to all these rectilinear venous cannula designs, which present the same cross-sectional area over their entire intravascular path, the smartcanula concept of "collapsed insertion and expansion in situ" is the logical next step for venous access. Automatically adjusting cross-sectional area up to a pre-determined diameter or the vessel lumen provides optimal flow and ease of use for both, insertion and removal. Smartcanula performance was assessed in a small series of patients (76 +/- 17 kg) undergoing redo procedures. The calculated target pump flow (2.4 L/min/m2) was 4.42 +/- 61 L/ min. Mean pump flow achieved during cardiopulmonary bypass was 4.84 +/- 87 L/min or 110% of the target. Reduced atrial chatter, kink resistance in situ, and improved blood drainage despite smaller access orifice size, are the most striking advantages of this new device. The benefits of smart cannulation are obvious in remote cannulation for limited access cardiac surgery, but there are many other cannula applications where space is an issue, and that is where smart cannulation is most effective.

Web-based Implementation of Winter Maintenance Decision Support System Using GIS and Remote Sensing, May 2005

Winter maintenance, particularly snow removal and the stress of snow removal materials on public structures, is an enormous budgetary burden on municipalities and nongovernmental maintenance organizations in cold climates. Lately, geospatial technologies such as remote sensing, geographic information systems (GIS), and decision support tools are roviding a valuable tool for planning snow removal operations. A few researchers recently used geospatial technologies to develop winter maintenance tools. However, most of these winter maintenance tools, while having the potential to address some of these information needs, are not typically placed in the hands of planners and other interested stakeholders. Most tools are not constructed with a nontechnical user in mind and lack an easyto-use, easily understood interface. A major goal of this project was to implement a web-based Winter Maintenance Decision Support System (WMDSS) that enhances the capacity of stakeholders (city/county planners, resource managers, transportation personnel, citizens, and policy makers) to evaluate different procedures for managing snow removal assets optimally. This was accomplished by integrating geospatial analytical techniques (GIS and remote sensing), the existing snow removal asset management system, and webbased spatial decision support systems. The web-based system was implemented using the ESRI ArcIMS ActiveX Connector and related web technologies, such as Active Server Pages, JavaScript, HTML, and XML. The expert knowledge on snow removal procedures is gathered and integrated into the system in the form of encoded business rules using Visual Rule Studio. The system developed not only manages the resources but also provides expert advice to assist complex decision making, such as routing, optimal resource allocation, and monitoring live weather information. This system was developed in collaboration with Black Hawk County, IA, the city of Columbia, MO, and the Iowa Department of transportation. This product was also demonstrated for these agencies to improve the usability and applicability of the system.

Implementing NX Remote Desktop technology in the LTSP system

This project focuses on studying and testing the benefits of the NX Remote Desktop technology in administrative use for Finnish Meteorological Institutes existing Linux Terminal Service Project environment. This was done due to the criticality of the system caused by growing number of users as the Linux Terminal Service Project system expands. Although many of the supporting tasks can be done via Secure Shell connection, testing graphical programs or desktop behaviour in such a way is impossible. At first basic technologies behind the NX Remote Desktop were studied, and after that started the testing of two possible programs, FreeNX and NoMachine NX server. Testing the functionality and bandwidth demands were first done in a closed local area network, and results were studied. The better candidate was then installed in a virtual server simulating actual Linux Terminal Service Project server at Finnish Meteorological Institute and connection from Internet was tested to see was there any problems with firewalls and security policies. The results are reported in this study. Studying and testing the two different candidates of NX Remote Desktop showed, that NoMachine NX Server provides better customer support and documentation. Security aspects of the Finnish Meteorological Institute had also to be considered, and since updates along with the new developing tools are announced in next version of the program, this version was the choice. Studies also show that even NoMachine promises a swift connection over an average of 20Kbit/s bandwidth, at least double of that is needed. This project gives an overview of available remote desktop products along their benefits. NX Remote Desktop technology is studied, and installation instructions are included. Testing is done in both, closed and the actual environment and problems and suggestions are studied and analyzed. The installation to the actual LTSP server is not yet made, but a virtual server is put up in the same place in the view of network topology. This ensures, that if the administrators are satisfied with the system, installation and setting up the system will go as described in this report.

A qui li dic? : la detecció de l’abús sexual infantil i el traspàs d’informació entre professionals

Aquest treball s'estructura en una primera part de fonamentació teòrica. El procediment que s'ha seguit ha estat la recerca bibliogràfica. Es basa en els aspectes tècnics a tenir en compte per la detecció de l' Abusos Sexuals Infantils (ASI), la ubicació dels Serveis Bàsics d'Atenció Social (SBAS) dins el sistema català de serveis socials, el marc jurídic dels drets i de la protecció a la infància i l'adolescència així com de qüestions relatives al tractament de dades, de la confidencialitat de les informacions de què disposem els/les professionals, l'obligació de secret, complementat amb la deontologia professional, que ens pot orientar a l'hora de prendre les decisions més adequades a la nostra praxi. La segona part del treball és empírica. El procediment ha estat portar a terme un grup de reflexió ètica amb professionals del Consorci de Benestar Social del Pla de l'Estany-Banyoles. Partint de casos pràctics de sospita d'ASI, les persones participants han concretat els principals problemes (alguns dels quals relacionats amb l'ètica aplicada) davant els quals s'han trobat a la fase de detecció/intervenció. Les aportacions extretes de les diferents sessions en les quals es van treballar part dels objectius d'aquesta recerca ens permeten concretar la realitat i una primera deliberació sobre quina podria ser la bona pràctica general. La tercera part utilitza la fonamentació teòrica i els material del treball de camp per presentar la reflexió final que pretén donar resposta als objectius plantejats en aquesta recerca

Structural characterization of rockfall sources in Yosemite Valley from remote sensing data and field surveys

Yosemite Valley poses significant rockfall hazard and related risk due to its glacially steepened walls and approximately 4 million visitors annually. To assess rockfall hazard, it is necessary to evaluate the geologic structure that contributes to the destabilization of rockfall sources and locate the most probable future source areas. Coupling new remote sensing techniques (Terrestrial Laser Scanning, Aerial Laser Scanning) and traditional field surveys, we investigated the regional geologic and structural setting, the orientation of the primary discontinuity sets for large areas of Yosemite Valley, and the specific discontinuity sets present at active rockfall sources. This information, combined with better understanding of the geologic processes that contribute to the progressive destabilization and triggering of granitic rock slabs, contributes to a more accurate rockfall susceptibility assessment for Yosemite Valley and elsewhere.

Remote control of pulmonary blood flow: a dream comes true.

The indication for pulmonary artery banding is currently limited by several factors. Previous attempts have failed to produce adjustable pulmonary artery banding with reliable external regulation. An implantable, telemetrically controlled, battery-free device (FloWatch) developed by EndoArt SA, a medical company established in Lausanne, Switzerland, for externally adjustable pulmonary artery banding was evaluated on minipigs and proved to be effective for up to 6 months. The first human implant was performed on a girl with complete atrioventricular septal defect with unbalanced ventricles, large patent ductus arteriosus and pulmonary hypertension. At one month of age she underwent closure of the patent ductus arteriosus and FloWatch implantation around the pulmonary artery through conventional left thoracotomy. The surgical procedure was rapid and uneventful. During the entire postoperative period bedside adjustments (narrowing or release of pulmonary artery banding with echocardiographic assessment) were repeatedly required to maintain an adequate pressure gradient. The early clinical results demonstrated the clinical benefits of unlimited external telemetric adjustments. The next step will be a multi-centre clinical trial to confirm the early results and adapt therapeutic strategies to this promising technology.

Parallelizing remote sensing image geometric correction

Remote sensing spatial, spectral, and temporal resolutions of images, acquired over a reasonably sized image extent, result in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is very attractive for monitoring, management, and scienti c activities. With Moore's Law alive and well, more and more parallelism is introduced into all computing platforms, at all levels of integration and programming to achieve higher performance and energy e ciency. Being the geometric calibration process one of the most time consuming processes when using remote sensing images, the aim of this work is to accelerate this process by taking advantage of new computing architectures and technologies, specially focusing in exploiting computation over shared memory multi-threading hardware. A parallel implementation of the most time consuming process in the remote sensing geometric correction has been implemented using OpenMP directives. This work compares the performance of the original serial binary versus the parallelized implementation, using several multi-threaded modern CPU architectures, discussing about the approach to nd the optimum hardware for a cost-e ective execution.

Active learning methods for remote sensing image classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

Guidelines for Cleaning up Former Methamphetamine Labs, June 2009

Methamphetamine (meth) drug labs are not a new hazard to Iowa. In 2004, federal, state and local authorities seized more than 1,400 Iowa labs. These labs are discovered in houses, apartments, motel rooms, motor vehicles, and even an occasional combine. A dramatic decrease in the number of meth labs occurred in 2005 when a law restricting the purchase of pseudoephedrine was implemented. Although the number of meth labs has decreased, they continue to exist. Since there is currently no official federal guidance or regulations on how to clean up a former meth lab, the Iowa Department of Public Health, Division of Environmental Health, has created these basic guidelines to assist public health officials, property owners and the general public in cleaning up former meth lab properties.

Semi-supervised remote sensing image classification with cluster kernels

A semisupervised support vector machine is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictions.