23 research outputs found
Hyperspectral Imaging for Landmine Detection
This PhD thesis aims at investigating the possibility to detect landmines using hyperspectral imaging. Using this technology, we are able to acquire at each pixel of the image spectral data in hundreds of wavelengths. So, at each pixel we obtain a reflectance spectrum that is used as fingerprint to identify the materials in each pixel, and mainly in our project help us to detect the presence of landmines.
The proposed process works as follows: a preconfigured drone (hexarotor or octorotor) will carry the hyperspectral camera. This programmed drone is responsible of flying over the contaminated area in order to take images from a safe distance. Various image processing techniques will be used to treat the image in order to isolate the landmine from the surrounding. Once the presence of a mine or explosives is suspected, an alarm signal is sent to the base station giving information about the type of the mine, its location and the clear path that could be taken by the mine removal team in order to disarm the mine.
This technology has advantages over the actually used techniques:
⢠It is safer because it limits the need of humans in the searching process and gives the opportunity to the demining team to detect the mines while they are in a safe region.
⢠It is faster. A larger area could be cleared in a single day by comparison with demining techniques
⢠This technique can be used to detect at the same time objects other than mines such oil or minerals.
First, a presentation of the problem of landmines that is expanding worldwide referring to some statistics from the UN organizations is provided. In addition, a brief presentation of different types of landmines is shown. Unfortunately, new landmines are well camouflaged and are mainly made of plastic in order to make their detection using metal detectors harder. A summary of all landmine detection techniques is shown to give an idea about the advantages and disadvantages of each technique.
In this work, we give an overview of different projects that worked on the detection of landmines using hyperspectral imaging. We will show the main results achieved in this field and future work to be done in order to make this technology effective.
Moreover, we worked on different target detection algorithms in order to achieve high probability of detection with low false alarm rate. We tested different statistical and linear unmixing based methods. In addition, we introduced the use of radial basis function neural networks in order to detect landmines at subpixel level. A comparative study between different detection methods will be shown in the thesis.
A study of the effect of dimensionality reduction using principal component analysis prior to classification is also provided. The study shows the dependency between the two steps (feature extraction and target detection). The selection of target detection algorithm will define if feature extraction in previous phase is necessary.
A field experiment has been done in order to study how the spectral signature of landmine will change depending on the environment in which the mine is planted. For this, we acquired the spectral signature of 6 types of landmines in different conditions: in Lab where specific source of light is used; in field where mines are covered by grass; and when mines are buried in soil. The results of this experiment are very interesting. The signature of two types of landmines are used in the simulations. They are a database necessary for supervised detection of landmines. Also we extracted some spectral characteristics of landmines that would help us to distinguish mines from background
Matched filter stochastic background characterization for hyperspectral target detection
Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters, which may be derived in many different scientific fields, can be used to locate spectral targets by modeling scene background as either structured geometric) with a set of endmembers (basis vectors) or as unstructured stochastic) with a covariance matrix. In unstructured background research, various methods of calculating the background covariance matrix have been developed, each involving either the removal of target signatures from the background model or the segmenting of image data into spatial or spectral subsets. The objective of these methods is to derive a background which matches the source of mixture interference for the detection of sub pixel targets, or matches the source of false alarms in the scene for the detection of fully resolved targets. In addition, these techniques increase the multivariate normality of the data from which the background is characterized, thus increasing adherence to the normality assumption inherent in the matched filter and ultimately improving target detection results. Such techniques for improved background characterization are widely practiced but not well documented or compared. This thesis will establish a strong theoretical foundation, describing the necessary preprocessing of hyperspectral imagery, deriving the spectral matched filter, and capturing current methods of unstructured background characterization. The extensive experimentation will allow for a comparative evaluation of several current unstructured background characterization methods as well as some new methods which improve stochastic modeling of the background. The results will show that consistent improvements over the scene-wide statistics can be achieved through spatial or spectral subsetting, and analysis of the results provides insight into the tradespaces of matching the interference, background multivariate normality and target exclusion for these techniques
Anomalous change detection in multi-temporal hyperspectral images
In latest years, the possibility to exploit the high amount of spectral information
has made hyperspectral remote sensing a very promising approach to detect changes
occurred in multi-temporal images. Detection of changes in images of the same area
collected at different times is of crucial interest in military and civilian applications,
spanning from wide area surveillance and damage assessment to geology and land
cover. In military operations, the interest is in rapid location and tracking of objects of
interest, people, vehicles or equipment that pose a potential threat. In civilian contexts,
changes of interest may include different types of natural or manmade threats, such as
the path of an impending storm or the source of a hazardous material spill.
In this PhD thesis, the focus is on Anomalous Change Detection (ACD) in airborne
hyperspectral images. The goal is the detection of small changes occurred in two images
of the same scene, i.e. changes having size comparable with the sensor ground
resolution. The objects of interest typically occupy few pixels of the image and change detection must be accomplished in a pixel-wise
fashion. Moreover, since the images are in general not radiometrically comparable,
because illumination, atmospheric and environmental conditions change from one
acquisition to the other, pervasive and uninteresting changes must be accounted for in
developing ACD strategies.
ACD process can be distinguished into two main phases: a pre-processing step, which
includes radiometric correction, image co-registration and noise filtering, and a
detection step, where the pre-processed images are compared according to a defined
criterion in order to derive a statistical ACD map highlighting the anomalous changes
occurred in the scene. In the literature, ACD has been widely investigated providing
valuable methods in order to cope with these problems. In this work, a general overview
of ACD methods is given reviewing the most known pre-processing and detection
methods proposed in the literature. The analysis has been conducted unifying different
techniques in a common framework based on binary decision theory, where one has to
test the two competing hypotheses H0 (change absent) and H1 (change present) on the
basis of an observation vector derived from the radiance measured on each pixel of the
two images.
Particular emphasis has been posed on statistical approaches, where ACD is derived in
the framework of Neymann Pearson theory and the decision rule is carried out on the
basis of the statistical properties assumed for the two hypotheses distribution, the
observation vector space and the secondary data exploited for the estimation of the
unknown parameters. Typically, ACD techniques assume that the observation
represents the realization of jointly Gaussian spatially stationary random process.
Though such assumption is adopted because of its mathematical tractability, it may be
quite simplistic to model the multimodality usually met in real data. A more appropriate
model is that adopted to derive the well known RX anomaly detector which assumes the
local Gaussianity of the hyperspectral data. In this framework, a new statistical ACD
method has been proposed considering the local Gaussianity of the hyperspectral data.
The assumption of local stationarity for the observations in the two hypotheses is taken
into account by considering two different models, leading to two different detectors.
In addition, when data are collected by airborne platforms, perfect co-registration
between images is very difficult to achieve. As a consequence, a residual misregistration
(RMR) error should be taken into account in developing ACD techniques.
Different techniques have been proposed to cope with the performance degradation
problem due to the RMR, embedding the a priori knowledge on the statistical properties
of the RMR in the change detection scheme. In this context, a new method has been
proposed for the estimation of the first and second order statistics of the RMR. The
technique is based on a sequential strategy that exploits the Scale Invariant Feature
Transform (SIFT) algorithm cascaded with the Minimum Covariance Determinant
algorithm. The proposed method adapts the SIFT procedure to hyperspectral images and
improves the robustness of the outliers filtering by means of a highly robust estimator of
multivariate location.
Then, the attention has been focused on noise filtering techniques aimed at enforcing
the consistency of the ACD process. To this purpose, a new method has been proposed
to mitigate the negative effects due to random noise. In particular, this is achieved by
means of a band selection technique aimed at discarding spectral channels whose useful
signal content is low compared with the noise contribution. Band selection is performed
on a per-pixel basis by exploiting the estimates of the noise variance accounting also for
the presence of the signal dependent noise component.
Finally, the effectiveness of the proposed techniques has been extensively evaluated by
employing different real hyperspectral datasets containing anomalous changes collected
in different acquisition conditions and on different scenarios, highlighting advantages
and drawbacks of each method.
In summary, the main issues related to ACD in multi-temporal hyperspectral images
have been examined in this PhD thesis. With reference to the pre-processing step, two
original contributions have been offered: i) an unsupervised technique for the estimation
of the RMR noise affecting hyperspectral images, and ii) an adaptive approach for ACD
which mitigates the negative effects due to random noise. As to the detection step, a
survey of the existing techniques has been carried out, highlighting the major drawbacks
and disadvantages, and a novel contribution has been offered by presenting a new
statistical ACD method which considers the local Gaussianity of the hyperspectral data
Simulated Annealing
The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine
Global Monitoring for Security and Stability (GMOSS) - Integrated Scientific and Technological Research Supporting Security Aspects of the European Union
This report is a collection of scientific activities and achievements of members of the GMOSS Network of Excellence during the period March 2004 to November 2007. Exceeding the horizon of classical remote-sensing-focused projects, GMOSS is characterized by the integration of political and social aspects of security with the assessment of remote sensing capabilities and end-users support opportunities. The report layout reflects the work breakdown structure of GMOSS and is divided into four parts.
Part I Concepts and Integration addresses the political background of European Security Policy and possibilities for Earth Observation technologies for a contribution. Besides it illustrates integration activities just as the GMOSS Gender Action Plan or a description of the GMOSS testcases.
Part II of this book presents various Application activities conducted by the network partners. The contributions vary from pipeline sabotage analysis in Iraq to GIS studies about groundwater vulnerability in Gaza Strip, from Population Monitoring in Zimbabwe to Post-Conflict Urban Reconstruction Assessments and many more.
Part III focuses on the research and development of image processing methods and Tools. The themes range from SAR interferometry for the measurement of Surface Displacement to Robust Satellite Techniques for monitoring natural hazards like volcanoes and earthquakes. Further subjects are the 3D detection of buildings in VHR imagery or texture analysis techniques on time series of satellite images with variable illumination and many other more.
The report closes with Part IV. In the chapter ÂżThe Way ForwardÂż a review on four years of integrated work is done. Challenges and achievements during this period are depicted. It ends with an outlook about a possible way forward for integrated European security research.JRC.G.2-Support to external securit
Dietary Phenylalanine Requirement of Fingerling Oreochromis Niloticus (Linnaeus)
This study was conducted to determine the dietary
phenylalanine for fingerling Oreochromis niloticus by conducting
an 8 weeks experiment in a flow-through system (1-1.5L/min) at
28°C water temperature. Phenylalanine requirement was determined
by feeding six casein-gelatin based amino acid test diets (350 g kgâ
1 CP; 16.72 kJ gâ1 GE) with graded levels of phenylalanine (4, 6.5,
9, 11.5, 14 and 16.5 g kgâ1 dry diet) at a constant level (10 g kgâ1)
of dietary tyrosine to triplicate groups of fish (1.65Âą0.09 g) near to
satiation. Absolute weight gain (AWG g fish-1), feed conversion
ratio (FCR), protein deposition (PD%), phenylalanine retention
efficiency (PRE%) and RNA/DNA ratio was found to improve with
the increasing concentrations of phenylalanine and peaked at 11.5 g
kgâ1 of dry diet. Quadratic regression analysis of AWG, PD and
PRE against varying levels of dietary phenylalanine indicated the
requirement at 12.1, 11.6, and 12.7 g kgâ1 dry diet, respectively and
the inclusion of phenylalanine at 12.1 g kgâ1 of dry diet,
corresponding to 34.6 g kgâ1 dietary protein is optimum for this fish.
Based on above data, total aromatic amino acid requirement of
fingerling O. niloticus was found to be 20.6 g kgâ1 (12.1 g kgâ1
phenylalanine+8.5 g kgâ1 tyrosine) of dry diet, corresponding to
58.8 g kgâ1 of dietary protein
Online learning of physics during a pandemic: A report from an academic experience in Italy
The arrival of the Sars-Cov II has opened a new window on teaching physics in academia.
Frontal lectures have left space for online teaching, teachers have been faced with a new way
of spreading knowledge, adapting contents and modalities of their courses. Students have
faced up with a new way of learning physics, which relies on free access to materials and
their informatics knowledge. We decided to investigate how online didactics has influenced
studentsâ assessments, motivation, and satisfaction in learning physics during the pandemic
in 2020. The research has involved bachelor (n = 53) and master (n = 27) students of
the Physics Department at the University of Cagliari (N = 80, 47 male; 33 female). The
MANOVA supported significant mean differences about gender and university level with
higher values for girls and master students in almost all variables investigated. The path
analysis showed that student-student, student-teacher interaction, and the organization of
the courses significantly influenced satisfaction and motivation in learning physics. The
results of this study can be used to improve the standards of teaching in physics at the
University of Cagliar
Engineering and built environment project conference 2015: book of abstracts - Toowoomba, Australia, 21-25 September 2015
Book of Abstracts of the USQ Engineering and Built Environment Conference 2015, held Toowoomba, Australia, 21-25 September 2015. These proceedings include extended abstracts of the verbal presentations that are delivered at the project conference. The work reported at the conference is the research undertaken by students in meeting the requirements of courses ENG4111/ENG4112 Research Project for undergraduate or ENG8411/ENG8412 Research Project and Dissertation for postgraduate students