279 research outputs found
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
SDF2Net: Shallow to Deep Feature Fusion Network for PolSAR Image Classification
Polarimetric synthetic aperture radar (PolSAR) images encompass valuable
information that can facilitate extensive land cover interpretation and
generate diverse output products. Extracting meaningful features from PolSAR
data poses challenges distinct from those encountered in optical imagery. Deep
learning (DL) methods offer effective solutions for overcoming these challenges
in PolSAR feature extraction. Convolutional neural networks (CNNs) play a
crucial role in capturing PolSAR image characteristics by leveraging kernel
capabilities to consider local information and the complex-valued nature of
PolSAR data. In this study, a novel three-branch fusion of complex-valued CNN,
named the Shallow to Deep Feature Fusion Network (SDF2Net), is proposed for
PolSAR image classification. To validate the performance of the proposed
method, classification results are compared against multiple state-of-the-art
approaches using the airborne synthetic aperture radar (AIRSAR) datasets of
Flevoland and San Francisco, as well as the ESAR Oberpfaffenhofen dataset. The
results indicate that the proposed approach demonstrates improvements in
overallaccuracy, with a 1.3% and 0.8% enhancement for the AIRSAR datasets and a
0.5% improvement for the ESAR dataset. Analyses conducted on the Flevoland data
underscore the effectiveness of the SDF2Net model, revealing a promising
overall accuracy of 96.01% even with only a 1% sampling ratio
Speckle noise reduction in PolSAR images with binary partition tree
In some remote sensing applications such as PolSAR (Polarimetric Synthetic Aperture Radar), the use of Binary Partition Trees (BPTs) for Speckle Noise filtering schemes is currently gaining interest. In this thesis, a new approach using this representation is investigated: branch filtering. This approach consists in searching for each leaf its ancestors and selecting the one that best represents it, that is, the one that yields the lower error. A potentiality assessment is done to evaluate the margin of improvement that new techniques based on this approach may provide and describe the basic specifications of the algorithms based on it. After that, different new techniques are developed, analysed and compared against the State-of-the-Art. We point out the main strengths and weaknesses of each technique. Our main goal is to understand the behaviour of the filtered data along the BPT branch and interpret how this information can be used in the future for speckle noise reduction in PolSAR images. Finally some conclusions are drawn and some possible future lines of work are exposed and commented.En algunas aplicaciones de teledetección como Polarimetric SAR, el uso de Árboles de Decisión Binarios está ganando interés. En esta tésis se incorpora un nuevo método que usa esta representación: filtraje por ramas. Este método consiste en buscar para cada hoja sus antepasados y seleccionar el mejor nodo como el que de el menor error. Se lleva a cabo un análisis de potencialidad para evaluar el margen de mejora que nuevas técnicas basadas en este método podrían proporcionar y se describen los principios basicos de los algoritmos que se basan en él. Tras esto, se desenvolupan distintas técnicas y se comparan con las del estado del arte. De cada técnica, destacamos sus principales fortalezas y debilidades. Nuestro objetivo principal es entender el comportamiento de los datos filtrados a lo largo de la rama del BPT e interpretar como podemos usar esta información en un futuro para la reducción de ruido especular (speckle) en imágenes PolSAR. Por último, se exponen algunas conclusiones y se presentan y comentan algunas posibles líneas de trabajo futuras.En algunes aplicacions de teledetecció com Polarimetric SAR, l'ús d'Arbres de Particio Binària està guanyant interès. En aquesta tesi, s'investiga un nou mètode que utilitza aquesta representació: filtratge per branques. Aquest mètode consisteix en buscar per cada fulla els seus avantpassats i seleccionar el millor node, és a dir, el que doni un error menor. Es duu a terme un analisi de potencialitat per evaluar el marge de millora que noves tècniques basades en aquest mètode podrien aportar i es descriuen els principis bàsics dels algorismes que s'hi basen. Després, es desenvolupen diverses tècniques i es comparen amb les de l'estat de l'art. Destaquem les principals fortalesses i feblesses de cada tècnica. El nostre principal objectiu és entendre el comportament de les dades filtrades al llarg de la branca del BPT i interpretar com podem utilitzar aquesta informació en un futur per la reducció del soroll especular (speckle) en imatges PolSAR. Per últim s'exposen algunes conclusions i es proposen i comenten possibles noves línies de treball
On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based Penalty
Synthetic aperture radar (SAR) imagery can provide useful information in a
multitude of applications, including climate change, environmental monitoring,
meteorology, high dimensional mapping, ship monitoring, or planetary
exploration. In this paper, we investigate solutions to a number of inverse
problems encountered in SAR imaging. We propose a convex proximal splitting
method for the optimization of a cost function that includes a non-convex
Cauchy-based penalty. The convergence of the overall cost function optimization
is ensured through careful selection of model parameters within a
forward-backward (FB) algorithm. The performance of the proposed penalty
function is evaluated by solving three standard SAR imaging inverse problems,
including super-resolution, image formation, and despeckling, as well as ship
wake detection for maritime applications. The proposed method is compared to
several methods employing classical penalty functions such as total variation
() and norms, and to the generalized minimax-concave (GMC) penalty.
We show that the proposed Cauchy-based penalty function leads to better image
reconstruction results when compared to the reference penalty functions for all
SAR imaging inverse problems in this paper.Comment: 18 pages, 7 figure
Advanced signal processing solutions for ATR and spectrum sharing in distributed radar systems
Previously held under moratorium from 11 September 2017 until 16 February 2022This Thesis presents advanced signal processing solutions for Automatic
Target Recognition (ATR) operations and for spectrum sharing in distributed radar systems.
Two Synthetic Aperture Radar (SAR) ATR algorithms are described for
full- and single-polarimetric images, and tested on the GOTCHA and the
MSTAR datasets. The first one exploits the Krogager polarimetric decomposition in order to enhance peculiar scattering mechanisms from manmade targets, used in combination with the pseudo-Zernike image moments. The second algorithm employs the Krawtchouk image moments,
that, being discrete defined, provide better representations of targets’ details. The proposed image moments based framework can be extended to
the availability of several images from multiple sensors through the implementation of a simple fusion rule.
A model-based micro-Doppler algorithm is developed for the identification of helicopters. The approach relies on the proposed sparse representation of the signal scattered from the helicopter’s rotor and received by
the radar. Such a sparse representation is obtained through the application of a greedy sparse recovery framework, with the goal of estimating
the number, the length and the rotation speed of the blades, parameters
that are peculiar for each helicopter’s model. The algorithm is extended to
deal with the identification of multiple helicopters flying in formation that
cannot be resolved in another domain. Moreover, a fusion rule is presented
to integrate the results of the identification performed from several sensors
in a distributed radar system. Tests performed both on simulated signals
and on real signals acquired from a scale model of a helicopter, confirm
the validity of the algorithm.
Finally, a waveform design framework for joint radar-communication systems is presented. The waveform is composed by quasi-orthogonal chirp
sub-carriers generated through the Fractional Fourier Transform (FrFT),
with the aim of preserving the radar performance of a typical Linear Frequency Modulated (LFM) pulse while embedding data to be sent to a
cooperative system. Techniques aimed at optimise the design parameters and mitigate the Inter-Carrier Interference (ICI) caused by the quasiorthogonality of the chirp sub-carriers are also described. The FrFT based
waveform is extensively tested and compared with Orthogonal Frequency
Division Multiplexing (OFDM) and LFM waveforms, in order to assess
both its radar and communication performance.This Thesis presents advanced signal processing solutions for Automatic
Target Recognition (ATR) operations and for spectrum sharing in distributed radar systems.
Two Synthetic Aperture Radar (SAR) ATR algorithms are described for
full- and single-polarimetric images, and tested on the GOTCHA and the
MSTAR datasets. The first one exploits the Krogager polarimetric decomposition in order to enhance peculiar scattering mechanisms from manmade targets, used in combination with the pseudo-Zernike image moments. The second algorithm employs the Krawtchouk image moments,
that, being discrete defined, provide better representations of targets’ details. The proposed image moments based framework can be extended to
the availability of several images from multiple sensors through the implementation of a simple fusion rule.
A model-based micro-Doppler algorithm is developed for the identification of helicopters. The approach relies on the proposed sparse representation of the signal scattered from the helicopter’s rotor and received by
the radar. Such a sparse representation is obtained through the application of a greedy sparse recovery framework, with the goal of estimating
the number, the length and the rotation speed of the blades, parameters
that are peculiar for each helicopter’s model. The algorithm is extended to
deal with the identification of multiple helicopters flying in formation that
cannot be resolved in another domain. Moreover, a fusion rule is presented
to integrate the results of the identification performed from several sensors
in a distributed radar system. Tests performed both on simulated signals
and on real signals acquired from a scale model of a helicopter, confirm
the validity of the algorithm.
Finally, a waveform design framework for joint radar-communication systems is presented. The waveform is composed by quasi-orthogonal chirp
sub-carriers generated through the Fractional Fourier Transform (FrFT),
with the aim of preserving the radar performance of a typical Linear Frequency Modulated (LFM) pulse while embedding data to be sent to a
cooperative system. Techniques aimed at optimise the design parameters and mitigate the Inter-Carrier Interference (ICI) caused by the quasiorthogonality of the chirp sub-carriers are also described. The FrFT based
waveform is extensively tested and compared with Orthogonal Frequency
Division Multiplexing (OFDM) and LFM waveforms, in order to assess
both its radar and communication performance
Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review
This paper is focused on considering the effects of
speckle noise on the eigen decomposition of the co-
herency matrix. Based on a perturbation analysis of the
matrix, it is possible to obtain an analytical expression for
the mean value of the eigenvalues and the eigenvectors,
as well as for the Entropy, the Anisotroopy and the dif-
ferent a angles. The analytical expressions are compared
against simulated polarimetric SAR data, demonstrating
the correctness of the different expressions.Peer ReviewedPostprint (published version
Microwave satellite remote sensing for a sustainable sea
The oceans cover roughly 2/3 of the Earth’s surface and are a fundamental ecosystem regulating climate, weather and representing a huge reservoir of biodiversity and natural resources. The preservation of the oceans is therefore not only relevant on an environmental perspective but also on an economical one. A sustainable approach is requested that cannot be simply achieved by improving technologies but calls for a shared new vision of common goods.Within such a complex and holistic problem, the role of satellite microwave remote sensing to observe marine ecosystem and to assist a sustainable development of human activities must be considered. In such a view the paper is meant. Accordingly, the key microwave sensor technologies are reviewed paying particular emphasis on those applications that can provide effective support to pursue some of the UN Sustainable Development Goals. Three meaningful sectors are showcased:oil and gas, where microwave sensors can provide continuous fine-resolution monitoring of critical infrastructures; renewable energy, where microwave satellite remote sensing allows supporting the management of offshore wind farms during both feasibility and operational stages; plastic pollution, where microwave technologies that exploit signals of opportunity offer large-scale monitoring capability to provide marine litter maps of the oceans
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