87 research outputs found
Earth Resources Laboratory research and technology
The accomplishments of the Earth Resources Laboratory's research and technology program are reported. Sensors and data systems, the AGRISTARS project, applied research and data analysis, joint research projects, test and evaluation studies, and space station support activities are addressed
Integrated Applications of Geo-Information in Environmental Monitoring
This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society
Processeurs SAR Basés sur des Détecteurs de Sous-Espaces
Classical SAR Imagery algorithms or SAR processors are all based on the isotropic point model. When detecting Man Made Targets (MMT), this assumption shows its limitation because of the directive behavior of these kind of targets: this model does not take into account their physical properties. The basic idea of this thesis is that we shall be able to develop more efficient SAR processors for the detection of MMT, provided that we adopt a more suited description for these targets. A natural way to model these MMT is to consider them as sets of canonical elements with unknown orientation. If the signal backscattered by the canonical element, whatever its orientation, belongs to a low dimensional subspace, it is then possible to develop a SAR processor based on subspace detectors matched to the canonical element. This processor is called Subspace Signal Detector SAR (SSDSAR) algorithm. When the MMT is embedded in a medium, where specific scatterers create interferences, one can model the electromagnetic response of these scatterers to clear them from the SAR image. Once again, if the set of signals backscattered by the element used to model the scatterer, whatever its orientation, belongs to a low dimensional subspace, a SAR processor based on subspace detectors can be developed. This processor is called Signal or Interference Subspace Detector SAR (SISDSAR) algorithm. Different ways to implement SSDSAR and SISDSAR are presented. Theoretical performances of the two new algorithms are studied in details. Finally, we apply these two processors to simulated and real data.Les algorithmes classiques d'imagerie SAR sont tous basés sur le modèle du point isotrope. Lors de la détection de cibles manufacturées (Man-Made Targets ou MMT), cette hypothèse montre ses limites en raison de la diffusion directive de ce genre de cibles: ce modèle ne tient pas compte de leurs propriétés physiques. L'idée de base de cette thèse est que nous serons en mesure de développer des processeurs SAR plus efficaces pour la détection du MMT, à condition que nous adoptions une description plus adaptée à ces objectifs. Une façon naturelle de modéliser ces MMT est de les considérer comme des ensembles d'éléments canoniques avec orientation inconnue. Si le signal rétrodiffusé par l'élément canonique, quelle que soit son orientation, appartient à un sous-espace de faible dimension, il est alors possible d'élaborer un processeur SAR sur la base de détecteurs de sous-espaces adaptés à l'élément canonique. Cet algorithme est appelé processeur SAR basé sur un Détecteur de Sous Espaces Signaux (SSDSAR). Lorsque le MMT est intégré dans un milieu où les diffuseurs spécifiques créent des interférences, on peut modéliser la réponse électromagnétique de ces diffuseurs pour les faire disparaître de l'image SAR. Encore une fois, si l'ensemble des signaux rétrodiffusés par l'élément utilisé pour modéliser le diffuseur, quelle que soit son orientation, appartient à un sous-espace de dimension faible, un processeur SAR basé sur des détecteurs subspatiales peut être développé. Ce processeur est appelé processeur SAR basé sur des Détecteurs de Sous Espaces Signaux ou Interférences (SISDSAR). Différentes façons de mettre en œuvre SSDSAR et SISDSAR sont présentées. Les performances théoriques des deux nouveaux algorithmes sont étudiées en détail. Enfin, nous appliquons ces deux processeurs à des données réelles et simulées
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
Automatic Rural Road Centerline Extraction from Aerial Images for a Forest Fire Support System
In the last decades, Portugal has been severely affected by forest fires which have caused
massive damage both environmentally and socially. Having a well-structured and precise
mapping of rural roads is critical to help firefighters to mitigate these events. The
traditional process of extracting rural roads centerlines from aerial images is extremely
time-consuming and tedious, because the mapping operator has to manually label the road
area and extract the road centerline.
A frequent challenge in the process of extracting rural roads centerlines is the high
amount of environmental complexity and road occlusions caused by vehicles, shadows, wild
vegetation, and trees, bringing heterogeneous segments that can be further improved. This
dissertation proposes an approach to automatically detect rural road segments as well as
extracting the road centerlines from aerial images.
The proposed method focuses on two main steps: on the first step, an architecture based
on a deep learning model (DeepLabV3+) is used, to extract the road features maps and
detect the rural roads. On the second step, the first stage of the process is an optimization
for improving road connections, as well as cleaning white small objects from the predicted
image by the neural network. Finally, a morphological approach is proposed to extract
the rural road centerlines from the previously detected roads by using thinning algorithms
like the Zhang-Suen and Guo-Hall methods.
With the automation of these two stages, it is now possible to detect and extract road
centerlines from complex rural environments automatically and faster than the traditional
ways, and possibly integrating that data in a Geographical Information System (GIS),
allowing the creation of real-time mapping applications.Nas últimas décadas, Portugal tem sido severamente afetado por fogos florestais, que têm
causado grandes estragos ambientais e sociais. Possuir um sistema de mapeamento de
estradas rurais bem estruturado e preciso é essencial para ajudar os bombeiros a mitigar
este tipo de eventos. Os processos tradicionais de extração de eixos de via em estradas
rurais a partir de imagens aéreas são extremamente demorados e fastidiosos. Um desafio
frequente na extração de eixos de via de estradas rurais é a alta complexidade dos ambientes
rurais e de estes serem obstruídos por veículos, sombras, vegetação selvagem e árvores,
trazendo segmentos heterogéneos que podem ser melhorados.
Esta dissertação propõe uma abordagem para detetar automaticamente estradas rurais,
bem como extrair os eixos de via de imagens aéreas.
O método proposto concentra-se em duas etapas principais: na primeira etapa é utilizada
uma arquitetura baseada em modelos de aprendizagem profunda (DeepLabV3+),
para detetar as estradas rurais. Na segunda etapa, primeiramente é proposta uma otimização
de intercessões melhorando as conexões relativas aos eixos de via, bem como a
remoção de pequenos artefactos que estejam a introduzir ruído nas imagens previstas pela
rede neuronal. E, por último, é utilizada uma abordagem morfológica para extrair os eixos
de via das estradas previamente detetadas recorrendo a algoritmos de esqueletização tais
como os algoritmos Zhang-Suen e Guo-Hall.
Automatizando estas etapas, é então possível extrair eixos de via de ambientes rurais
de grande complexidade de forma automática e com uma maior rapidez em relação aos
métodos tradicionais, permitindo, eventualmente, integrar os dados num Sistema de Informação
Geográfica (SIG), possibilitando a criação de aplicativos de mapeamento em tempo
real
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