861 research outputs found
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
Multiresolution Approximation of a Bayesian Inverse Problem using Second-Generation Wavelets
Bayesian approaches are one of the primary methodologies to tackle an inverse
problem in high dimensions. Such an inverse problem arises in hydrology to
infer the permeability field given flow data in a porous media. It is common
practice to decompose the unknown field into some basis and infer the
decomposition parameters instead of directly inferring the unknown. Given the
multiscale nature of permeability fields, wavelets are a natural choice for
parameterizing them. This study uses a Bayesian approach to incorporate the
statistical sparsity that characterizes discrete wavelet coefficients. First,
we impose a prior distribution incorporating the hierarchical structure of the
wavelet coefficient and smoothness of reconstruction via scale-dependent
hyperparameters. Then, Sequential Monte Carlo (SMC) method adaptively explores
the posterior density on different scales, followed by model selection based on
Bayes Factors. Finally, the permeability field is reconstructed from the
coefficients using a multiresolution approach based on second-generation
wavelets. Here, observations from the pressure sensor grid network are computed
via Multilevel Adaptive Wavelet Collocation Method (AWCM). Results highlight
the importance of prior modeling on parameter estimation in the inverse
problem
Exploring space situational awareness using neuromorphic event-based cameras
The orbits around earth are a limited natural resource and one that hosts a vast range of vital space-based systems that support international systems use by both commercial industries, civil organisations, and national defence. The availability of this space resource is rapidly depleting due to the ever-growing presence of space debris and rampant overcrowding, especially in the limited and highly desirable slots in geosynchronous orbit. The field of Space Situational Awareness encompasses tasks aimed at mitigating these hazards to on-orbit systems through the monitoring of satellite traffic. Essential to this task is the collection of accurate and timely observation data. This thesis explores the use of a novel sensor paradigm to optically collect and process sensor data to enhance and improve space situational awareness tasks. Solving this issue is critical to ensure that we can continue to utilise the space environment in a sustainable way. However, these tasks pose significant engineering challenges that involve the detection and characterisation of faint, highly distant, and high-speed targets. Recent advances in neuromorphic engineering have led to the availability of high-quality neuromorphic event-based cameras that provide a promising alternative to the conventional cameras used in space imaging. These cameras offer the potential to improve the capabilities of existing space tracking systems and have been shown to detect and track satellites or ‘Resident Space Objects’ at low data rates, high temporal resolutions, and in conditions typically unsuitable for conventional optical cameras. This thesis presents a thorough exploration of neuromorphic event-based cameras for space situational awareness tasks and establishes a rigorous foundation for event-based space imaging. The work conducted in this project demonstrates how to enable event-based space imaging systems that serve the goals of space situational awareness by providing accurate and timely information on the space domain. By developing and implementing event-based processing techniques, the asynchronous operation, high temporal resolution, and dynamic range of these novel sensors are leveraged to provide low latency target acquisition and rapid reaction to challenging satellite tracking scenarios. The algorithms and experiments developed in this thesis successfully study the properties and trade-offs of event-based space imaging and provide comparisons with traditional observing methods and conventional frame-based sensors. The outcomes of this thesis demonstrate the viability of event-based cameras for use in tracking and space imaging tasks and therefore contribute to the growing efforts of the international space situational awareness community and the development of the event-based technology in astronomy and space science applications
BDS GNSS for Earth Observation
For millennia, human communities have wondered about the possibility of observing
phenomena in their surroundings, and in particular those affecting the Earth on which they live.
More generally, it can be conceptually defined as Earth observation (EO) and is the collection of
information about the biological, chemical and physical systems of planet Earth. It can be undertaken
through sensors in direct contact with the ground or airborne platforms (such as weather balloons and
stations) or remote-sensing technologies. However, the definition of EO has only become significant
in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit.
Referring strictly to civil applications, satellites of this type were initially designed to provide
satellite images; later, their purpose expanded to include the study of information on land
characteristics, growing vegetation, crops, and environmental pollution. The data collected are used
for several purposes, including the identification of natural resources and the production of accurate
cartography. Satellite observations can cover the land, the atmosphere, and the oceans.
Remote-sensing satellites may be equipped with passive instrumentation such as infrared or
cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are
non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the
Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and
Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly
called ’temporal resolution’), i.e., in a certain number of orbits around the Earth.
The first remote-sensing satellites were the American NASA/USGS Landsat Program;
subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing
satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian
RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were
dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed
system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the
Chinese BuFeng-1 and Fengyun-3 series.
Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers
worldwide for a multitude of Earth monitoring and exploration applications. On the other hand,
over the past 40 years, GNSSs have become an essential part of many human activities. As is widely
noted, there are currently four fully operational GNSSs; two of these were developed for military
purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for
civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European
Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning
System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation
Satellite System (IRNSS/NavIC), will become available in the next few years, which will have
enormous potential for scientific applications and geomatics professionals.
In addition to their traditional role of providing global positioning, navigation, and timing (PNT)
information, GNSS navigation signals are now being used in new and innovative ways. Across the
globe, new fields of scientific study are opening up to examine how signals can provide information
about the characteristics of the atmosphere and even the surfaces from which they are reflected before
being collected by a receiver.
EO researchers monitor global environmental systems using in situ and remote monitoring tools.
Their findings provide tools to support decision makers in various areas of interest, from security
to the natural environment. GNSS signals are considered an important new source of information
because they are a free, real-time, and globally available resource for the EO community
Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead
Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks compared with any of the source images. It has been widely applied to image interpretation and pre-processing of various applications. A large number of methods have been proposed to achieve better fusion results by considering the spatial and spectral relationships among panchromatic and multispectral images. In recent years, the fast development of artificial intelligence (AI) and deep learning (DL) has significantly enhanced the development of pan-sharpening techniques. However, this field lacks a comprehensive overview of recent advances boosted by the rise of AI and DL. This paper provides a comprehensive review of a variety of pan-sharpening methods that adopt four different paradigms, i.e., component substitution, multiresolution analysis, degradation model, and deep neural networks. As an important aspect of pan-sharpening, the evaluation of the fused image is also outlined to present various assessment methods in terms of reduced-resolution and full-resolution quality measurement. Then, we conclude this paper by discussing the existing limitations, difficulties, and challenges of pan-sharpening techniques, datasets, and quality assessment. In addition, the survey summarizes the development trends in these areas, which provide useful methodological practices for researchers and professionals. Finally, the developments in pan-sharpening are summarized in the conclusion part. The aim of the survey is to serve as a referential starting point for newcomers and a common point of agreement around the research directions to be followed in this exciting area
Uncertainty Quantification for Scale-Space Blob Detection
We consider the problem of blob detection for uncertain images, such as
images that have to be inferred from noisy measurements. Extending recent work
motivated by astronomical applications, we propose an approach that represents
the uncertainty in the position and size of a blob by a region in a
three-dimensional scale space. Motivated by classic tube methods such as the
taut-string algorithm, these regions are obtained from level sets of the
minimizer of a total variation functional within a high-dimensional tube. The
resulting non-smooth optimization problem is challenging to solve, and we
compare various numerical approaches for its solution and relate them to the
literature on constrained total variation denoising. Finally, the proposed
methodology is illustrated on numerical experiments for deconvolution and
models related to astrophysics, where it is demonstrated that it allows to
represent the uncertainty in the detected blobs in a precise and physically
interpretable way
A review of spatial enhancement of hyperspectral remote sensing imaging techniques
Remote sensing technology has undeniable importance in various industrial applications, such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding, and optical gas imaging, to name a few. Remote sensing technology has been continuously evolving, offering a range of image modalities that can facilitate the aforementioned applications. One such modality is Hyperspectral Imaging (HSI). Unlike Multispectral Images (MSI) and natural images, HSI consist of hundreds of bands. Despite their high spectral resolution, HSI suffer from low spatial resolution in comparison to their MSI counterpart, which hinders the utilization of their full potential. Therefore, spatial enhancement, or Super Resolution (SR), of HSI is a classical problem that has been gaining rapid attention over the past two decades. The literature is rich with various SR algorithms that enhance the spatial resolution of HSI while preserving their spectral fidelity. This paper reviews and discusses the most important algorithms relevant to this area of research between 2002-2022, along with the most frequently used datasets, HSI sensors, and quality metrics. Meta-analysis are drawn based on the aforementioned information, which is used as a foundation that summarizes the state of the field in a way that bridges the past and the present, identifies the current gap in it, and recommends possible future directions
An uncertainty prediction approach for active learning - application to earth observation
Mapping land cover and land usage dynamics are crucial in remote sensing since farmers
are encouraged to either intensify or extend crop use due to the ongoing rise in the world’s
population. A major issue in this area is interpreting and classifying a scene captured in
high-resolution satellite imagery. Several methods have been put forth, including neural
networks which generate data-dependent models (i.e. model is biased toward data) and
static rule-based approaches with thresholds which are limited in terms of diversity(i.e.
model lacks diversity in terms of rules). However, the problem of having a machine learning
model that, given a large amount of training data, can classify multiple classes over different
geographic Sentinel-2 imagery that out scales existing approaches remains open.
On the other hand, supervised machine learning has evolved into an essential part of many
areas due to the increasing number of labeled datasets. Examples include creating classifiers
for applications that recognize images and voices, anticipate traffic, propose products, act
as a virtual personal assistant and detect online fraud, among many more. Since these
classifiers are highly dependent from the training datasets, without human interaction or
accurate labels, the performance of these generated classifiers with unseen observations
is uncertain. Thus, researchers attempted to evaluate a number of independent models
using a statistical distance. However, the problem of, given a train-test split and classifiers
modeled over the train set, identifying a prediction error using the relation between train
and test sets remains open.
Moreover, while some training data is essential for supervised machine learning, what
happens if there is insufficient labeled data? After all, assigning labels to unlabeled datasets
is a time-consuming process that may need significant expert human involvement. When
there aren’t enough expert manual labels accessible for the vast amount of openly available
data, active learning becomes crucial. However, given a large amount of training and
unlabeled datasets, having an active learning model that can reduce the training cost of
the classifier and at the same time assist in labeling new data points remains an open
problem.
From the experimental approaches and findings, the main research contributions, which
concentrate on the issue of optical satellite image scene classification include: building
labeled Sentinel-2 datasets with surface reflectance values; proposal of machine learning
models for pixel-based image scene classification; proposal of a statistical distance based
Evidence Function Model (EFM) to detect ML models misclassification; and proposal of
a generalised sampling approach for active learning that, together with the EFM enables
a way of determining the most informative examples.
Firstly, using a manually annotated Sentinel-2 dataset, Machine Learning (ML) models
for scene classification were developed and their performance was compared to Sen2Cor the reference package from the European Space Agency – a micro-F1 value of 84%
was attained by the ML model, which is a significant improvement over the corresponding
Sen2Cor performance of 59%. Secondly, to quantify the misclassification of the ML models,
the Mahalanobis distance-based EFM was devised. This model achieved, for the labeled
Sentinel-2 dataset, a micro-F1 of 67.89% for misclassification detection. Lastly, EFM was
engineered as a sampling strategy for active learning leading to an approach that attains
the same level of accuracy with only 0.02% of the total training samples when compared
to a classifier trained with the full training set.
With the help of the above-mentioned research contributions, we were able to provide
an open-source Sentinel-2 image scene classification package which consists of ready-touse
Python scripts and a ML model that classifies Sentinel-2 L1C images generating a
20m-resolution RGB image with the six studied classes (Cloud, Cirrus, Shadow, Snow,
Water, and Other) giving academics a straightforward method for rapidly and effectively
classifying Sentinel-2 scene images. Additionally, an active learning approach that uses, as
sampling strategy, the observed prediction uncertainty given by EFM, will allow labeling
only the most informative points to be used as input to build classifiers; Sumário:
Uma Abordagem de Previsão de Incerteza para
Aprendizagem Ativa – Aplicação à Observação da Terra
O mapeamento da cobertura do solo e a dinâmica da utilização do solo são cruciais na
deteção remota uma vez que os agricultores são incentivados a intensificar ou estender as
culturas devido ao aumento contÃnuo da população mundial. Uma questão importante
nesta área é interpretar e classificar cenas capturadas em imagens de satélite de alta resolução.
Várias aproximações têm sido propostas incluindo a utilização de redes neuronais
que produzem modelos dependentes dos dados (ou seja, o modelo é tendencioso em relação
aos dados) e aproximações baseadas em regras que apresentam restrições de diversidade
(ou seja, o modelo carece de diversidade em termos de regras). No entanto, a criação de
um modelo de aprendizagem automática que, dada uma uma grande quantidade de dados
de treino, é capaz de classificar, com desempenho superior, as imagens do Sentinel-2 em
diferentes áreas geográficas permanece um problema em aberto.
Por outro lado, têm sido utilizadas técnicas de aprendizagem supervisionada na resolução
de problemas nas mais diversas áreas de devido à proliferação de conjuntos de dados etiquetados.
Exemplos disto incluem classificadores para aplicações que reconhecem imagem
e voz, antecipam tráfego, propõem produtos, atuam como assistentes pessoais virtuais e
detetam fraudes online, entre muitos outros. Uma vez que estes classificadores são fortemente
dependente do conjunto de dados de treino, sem interação humana ou etiquetas
precisas, o seu desempenho sobre novos dados é incerta. Neste sentido existem propostas
para avaliar modelos independentes usando uma distância estatÃstica. No entanto, o problema
de, dada uma divisão de treino-teste e um classificador, identificar o erro de previsão
usando a relação entre aqueles conjuntos, permanece aberto.
Mais ainda, embora alguns dados de treino sejam essenciais para a aprendizagem supervisionada,
o que acontece quando a quantidade de dados etiquetados é insuficiente? Afinal,
atribuir etiquetas é um processo demorado e que exige perÃcia, o que se traduz num envolvimento
humano significativo. Quando a quantidade de dados etiquetados manualmente por
peritos é insuficiente a aprendizagem ativa torna-se crucial. No entanto, dada uma grande
quantidade dados de treino não etiquetados, ter um modelo de aprendizagem ativa que
reduz o custo de treino do classificador e, ao mesmo tempo, auxilia a etiquetagem de novas
observações permanece um problema em aberto.
A partir das abordagens e estudos experimentais, as principais contribuições deste trabalho,
que se concentra na classificação de cenas de imagens de satélite óptico incluem:
criação de conjuntos de dados Sentinel-2 etiquetados, com valores de refletância de superfÃcie;
proposta de modelos de aprendizagem automática baseados em pixels para classificação de cenas de imagens de satétite; proposta de um Modelo de Função de Evidência (EFM)
baseado numa distância estatÃstica para detetar erros de classificação de modelos de aprendizagem;
e proposta de uma abordagem de amostragem generalizada para aprendizagem
ativa que, em conjunto com o EFM, possibilita uma forma de determinar os exemplos mais
informativos.
Em primeiro lugar, usando um conjunto de dados Sentinel-2 etiquetado manualmente,
foram desenvolvidos modelos de Aprendizagem Automática (AA) para classificação de cenas
e seu desempenho foi comparado com o do Sen2Cor – o produto de referência da
Agência Espacial Europeia – tendo sido alcançado um valor de micro-F1 de 84% pelo classificador,
o que representa uma melhoria significativa em relação ao desempenho Sen2Cor
correspondente, de 59%. Em segundo lugar, para quantificar o erro de classificação dos
modelos de AA, foi concebido o Modelo de Função de Evidência baseado na distância de
Mahalanobis. Este modelo conseguiu, para o conjunto de dados etiquetado do Sentinel-2
um micro-F1 de 67,89% na deteção de classificação incorreta. Por fim, o EFM foi utilizado
como uma estratégia de amostragem para a aprendizagem ativa, uma abordagem
que permitiu atingir o mesmo nÃvel de desempenho com apenas 0,02% do total de exemplos
de treino quando comparado com um classificador treinado com o conjunto de treino
completo.
Com a ajuda das contribuições acima mencionadas, foi possÃvel desenvolver um pacote
de código aberto para classificação de cenas de imagens Sentinel-2 que, utilizando num
conjunto de scripts Python, um modelo de classificação, e uma imagem Sentinel-2 L1C,
gera a imagem RGB correspondente (com resolução de 20m) com as seis classes estudadas
(Cloud, Cirrus, Shadow, Snow, Water e Other), disponibilizando à academia um método
direto para a classificação de cenas de imagens do Sentinel-2 rápida e eficaz. Além disso, a
abordagem de aprendizagem ativa que usa, como estratégia de amostragem, a deteção de
classificacão incorreta dada pelo EFM, permite etiquetar apenas os pontos mais informativos
a serem usados como entrada na construção de classificadores
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