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Adapted from the source document.

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.

- The area of the GCC is bigger than West Europe. They are one of the major poles of international economy, especially in finance and banking domains. They are the most determining factor in the oil market, not only because of mass production, but also for the reason of their rich resources. - They are newly established countries. Their social-tribal structure and their cultural behavior are not fitting the modern governance. This contrast made and makes a lot of problems that will continue. Their location is one of the most strategic. - Their developments have more external origin than internal, especially from the Arab world: Arab nationalism and Nasserism, the 67 and 73 wars, the 1960's and 70's religious and political developments in Egypt, the Islamic revolution in Iran, the military intervention of US in Afghanistan, the Iran-Iraq war, Kuwait occupation, 11/9, 2003 war, and Arab Spring. And also rise of oil prices, flowage of foreign manpower, vast presence of US military, and rapid modernization. - All these factors strongly affected their cultural and religious attitude. The most Islamic sacred places lie in this region. This reality along with desert and tribal culture caused different reactions since the late 70's. Al-Uteibi and al-Qaeda are good examples, but a large number of their population became extremists and they are now enjoying a great political and financial influence. Certainly, modern communications played a big role. This reality made a new situation in the Arab and Muslim world and even on the international scene. I tried to explain briefly all these developments, including the socio-political stability. It can be done through a clear comprehension of Islamic culture and civilization, Arab peculiarities and their experiences in the modern time, and history of the Arabian Peninsula.

The dissertation focuses on EU and international environmental law since the 1990s, with particular attention to the role played by the EU in curbing emissions through market-based measures like the EU ETS and in mediating among reluctant actors on an international level. The key controversial point of the dissertation is the inclusion of international civil aviation in the EU ETS, which raised several concerns on its conformity with international treaty and customary law. In order to facilitate negotiations within ICAO, the EU decided to reduce the scope of its directive to only intra-EEA flights. An agreement on an offset carbon scheme, called CORSIA, was reached in 2016. Finally, a brief overview of the evolution from the Kyoto Protocol to the Paris Agreement is provided in order to understand the evolution of international climate change policy and the EU's role during negotiations, contributing to an innovative universal treaty tackling climate change.