Image Simulation in Remote Sensing

Remote sensing is being actively researched in the fields of environment, military and urban planning through technologies such as monitoring of natural climate phenomena on the earth, land cover classification, and object detection. Recently, satellites equipped with observation cameras of various resolutions were launched, and remote sensing images are acquired by various observation methods including cluster satellites. However, the atmospheric and environmental conditions present in the observed scene degrade the quality of images or interrupt the capture of the Earth's surface information. One method to overcome this is by generating synthetic images through image simulation. Synthetic images can be generated by using statistical or knowledge-based models or by using spectral and optic-based models to create a simulated image in place of the unobtained image at a required time. Various proposed methodologies will provide economical utility in the generation of image learning materials and time series data through image simulation. The 6 published articles cover various topics and applications central to Remote sensing image simulation. Although submission to this Special Issue is now closed, the need for further in-depth research and development related to image simulation of High-spatial and spectral resolution, sensor fusion and colorization remains.I would like to take this opportunity to express my most profound appreciation to the MDPI Book staff, the editorial team of Applied Sciences journal, especially Ms. Nimo Lang, the assistant editor of this Special Issue, talented authors, and professional reviewers

Exoplanet Imaging Data Challenge, phase II: Characterization of exoplanet signals in high-contrast images

Today, there exists a wide variety of algorithms dedicated to high-contrast imaging, especially for the detection and characterisation of exoplanet signals. These algorithms are tailored to address the very high contrast between the exoplanet signal(s), which can be more than two orders of magnitude fainter than the bright starlight residuals in coronagraphic images. The starlight residuals are inhomogeneously distributed and follow various timescales that depend on the observing conditions and on the target star brightness. Disentangling the exoplanet signals within the starlight residuals is therefore challenging, and new post-processing algorithms are striving to achieve more accurate astrophysical results. The Exoplanet Imaging Data Challenge is a community-wide effort to develop, compare and evaluate algorithms using a set of benchmark high-contrast imaging datasets. After a first phase ran in 2020 and focused on the detection capabilities of existing algorithms, the focus of this ongoing second phase is to compare the characterisation capabilities of state-of-the-art techniques. The characterisation of planetary companions is two-fold: the astrometry (estimated position with respect to the host star) and spectrophotometry (estimated contrast with respect to the host star, as a function of wavelength). The goal of this second phase is to offer a platform for the community to benchmark techniques in a fair, homogeneous and robust way, and to foster collaborations.Comment: Submitted to SPIE Astronomical Telescopes + Instrumentation 2022, Adaptive Optics Systems VIII, Paper 12185-

Preparing an unsupervised massive analysis of SPHERE high contrast data with the PACO algorithm

We aim at searching for exoplanets on the whole ESO/VLT-SPHERE archive with improved and unsupervised data analysis algorithm that could allow to detect massive giant planets at 5 au. To prepare, test and optimize our approach, we gathered a sample of twenty four solar-type stars observed with SPHERE using angular and spectral differential imaging modes. We use PACO, a new generation algorithm recently developed, that has been shown to outperform classical methods. We also improve the SPHERE pre-reduction pipeline, and optimize the outputs of PACO to enhance the detection performance. We develop custom built spectral prior libraries to optimize the detection capability of the ASDI mode for both IRDIS and IFS. Compared to previous works conducted with more classical algorithms than PACO, the contrast limits we derived are more reliable and significantly better, especially at short angular separations where a gain by a factor ten is obtained between 0.2 and 0.5 arcsec. Under good observing conditions, planets down to 5 MJup, orbiting at 5 au could be detected around stars within 60 parsec. We identified two exoplanet candidates that require follow-up to test for common proper motion. In this work, we demonstrated on a small sample the benefits of PACO in terms of achievable contrast and of control of the confidence levels. Besides, we have developed custom tools to take full benefits of this algorithm and to quantity the total error budget on the estimated astrometry and photometry. This work paves the way towards an end-to-end, homogeneous, and unsupervised massive re-reduction of archival direct imaging surveys in the quest of new exoJupiters.Comment: Accepted for publication in A&

Semi-Supervised Learning for Mars Imagery Classification and Segmentation

With the progress of Mars exploration, numerous Mars image data are collected and need to be analyzed. However, due to the imbalance and distortion of Martian data, the performance of existing computer vision models is unsatisfactory. In this paper, we introduce a semi-supervised framework for machine vision on Mars and try to resolve two specific tasks: classification and segmentation. Contrastive learning is a powerful representation learning technique. However, there is too much information overlap between Martian data samples, leading to a contradiction between contrastive learning and Martian data. Our key idea is to reconcile this contradiction with the help of annotations and further take advantage of unlabeled data to improve performance. For classification, we propose to ignore inner-class pairs on labeled data as well as neglect negative pairs on unlabeled data, forming supervised inter-class contrastive learning and unsupervised similarity learning. For segmentation, we extend supervised inter-class contrastive learning into an element-wise mode and use online pseudo labels for supervision on unlabeled areas. Experimental results show that our learning strategies can improve the classification and segmentation models by a large margin and outperform state-of-the-art approaches.Comment: Accepted by ACM Trans. on Multimedia Computing Communications and Applications (TOMM

Exoplanet imaging data challenge, phase II: characterization of exoplanet signals in high-contract images

peer reviewedToday, there exists a wide variety of algorithms dedicated to high-contrast imaging, especially for the detection and characterisation of exoplanet signals. These algorithms are tailored to address the very high contrast between the exoplanet signal(s), which can be more than two orders of magnitude fainter than the bright starlight residuals in coronagraphic images. The starlight residuals are inhomogeneously distributed and follow various timescales that depend on the observing conditions and on the target star brightness. Disentangling the exoplanet signals within the starlight residuals is therefore challenging, and new post-processing algorithms are striving to achieve more accurate astrophysical results. The Exoplanet Imaging Data Challenge is a community-wide effort to develop, compare and evaluate algorithms using a set of benchmark high-contrast imaging datasets. After a first phase ran in 2020 and focused on the detection capabilities of existing algorithms, the focus of this ongoing second phase is to compare the characterisation capabilities of state-of-the-art techniques. The characterisation of planetary companions is two-fold: the astrometry (estimated position with respect to the host star) and spectrophotometry (estimated contrast with respect to the host star, as a function of wavelength). The goal of this second phase is to offer a platform for the community to benchmark techniques in a fair, homogeneous and robust way, and to foster collaborations

Advanced Data Processing Techniques for Exoplanet Detection in High Contrast Images

High contrast imaging (HCI) is one of the most challenging techniques for exoplanet detection, but also one of the most promising. The main difficulties encountered with HCI arise from the small angular separation between the host star and the potential exoplanets, the flux ratio between them, and the image degradation caused by the Earth's atmosphere. Adaptive optics and coronagraphic techniques are now widely used to improve the quality and the dynamic range of the images with dedicated instruments. However, despite the use of these cutting-edge technologies, the resulting images are still affected by residual aberrations. Under good observing conditions, the performance of HCI instruments is limited by aberrations arising in the optical train of the telescope and instrument, generating quasi-statics speckles in the field of view. Different post-processing techniques along with observing strategies have been proposed in the last decade to deal with these quasi-static speckles, whose shape and intensity are similar to potential companions.This PhD thesis builds upon these recent advances, focusing mainly on the development of a new data processing technique to unveil fainter planetary signals from angular differential imaging (ADI) sequences, and to retrieve their observed properties. Most post-processing techniques are based on the ADI observing strategy and perform a subtraction of a reference point spread function (PSF), which models the speckle field. Such techniques generally make use of signal-to-noise maps to infer the existence of planetary signals via thresholding. An alternative method to generate the final detection map based on a regime-switching model (RSM) is developed in the first part of this thesis. This approach considers a planetary regime and a speckle regime to describe, via a Markov chain, the evolution of the pixels intensity within cubes of residuals generated by one or multiple PSF-subtraction techniques. The short memory process used in the RSM algorithm allows quasi-static speckles to be treated more effectively. Using multiple PSF-subtraction techniques helps reducing further the residual speckle noise level, better discriminating planetary signals from residual speckles. The RSM map algorithm showed an overall better performance in the receiver operating characteristic space when compared with standard signal-to-noise ratio maps for several state-of-the-art ADI-based post-processing algorithms. Building on the good results obtained with the RSM algorithm, several improvements of the vanilla RSM map algorithm are then implemented. We started by considering two forward-model versions of the RSM map algorithm based on the LOCI and KLIP PSF-subtraction techniques, allowing to account for the planetary signal self-subtraction observed at short separations. We then addressed the question of optimally selecting the PSF subtraction techniques to optimise the overall performance of the RSM map. A new forward-backward approach is also implemented to take into account both past and future observations to compute the RSM map probabilities, leading to improved precision in terms of astrometry and lowering the background speckle noise. Performance analysis demonstrate the benefits of these improvements. Following these developments, the RSM map algorithm can use up to seven PSF-subtraction techniques. The selection of the optimal parameters for these PSF-subtraction techniques as well as for the RSM map is therefore not straightforward, time consuming, and can be biased by assumptions made as to the underlying data set. We propose in the fourth chapter of this thesis a novel optimisation procedure that can be applied to each of the PSF-subtraction techniques alone, or to the entire RSM framework. This optimisation procedure, called auto-RSM, consists of three main steps: (i) definition of the optimal set of parameters for the PSF-subtraction techniques, (ii) optimisation of the RSM algorithm, and (iii) selection of the optimal set of PSF-subtraction techniques and ADI sequences used to generate the final RSM probability map. The optimisation procedure is applied to the data sets of the exoplanet imaging data challenge (EIDC). The results demonstrate the interest of the proposed optimisation procedure, with better performance metrics compared to the earlier version of RSM, as well as to other HCI data-processing techniques. The auto-RSM framework is finally applied to the SHARDDS survey to bring an additional piece to the exoplanet puzzle, by contributing to the characterisation of planetary population via the estimation of occurrence rate maps. This survey gathers 55 main-sequence stars within 100\,pc, known to host a high-infrared-excess debris disk, allowing us to potentially better understand the complex interactions between substellar companions and disks. A clustering approach is used to divide the set of targets into multiple subsets, in order to reduce the computation time by estimating a single optimal parametrisation for each considered subset. A new planetary characterisation algorithm, based on the RSM framework, is developed and tested successfully. We uncover the companion around HD206893, but do not detect any new companion around other stars. Planet detection and planet occurrence frequencies are nevertheless derived from the generated contrast curves and show a high sensitivity between 10 and 100 au for substellar companions with masses over 10 Jupiter masses. Throughout the different chapters of this thesis, we have built a complex but highly efficient post-processing framework for ADI sequences, adding in each chapter many new features and simplifying its use. All these developments have been compiled into a python package, called PyRSM, which offers a parameter-free detection map computation algorithm with a very low level of residual speckles. This package has largely increased in maturity thanks to the SHARDDS survey and has become a robust HCI post-processing pipeline, achieving good performance in terms of contrasts. PyRSM will hopefully be used for many more surveys and provide unprecedented detection limits, allowing the detection of many exoplanets with the next generation of telescopes and instruments

Tracing exoplanets through time with TESS

In the thirty years since the discovery of the first exoplanet, over 5000 verified exoplanets have been discovered, unveiling a rich array of different exoplanetary architectures. However, there are still many unanswered questions regarding the formation and evolution pathways which have led to the observed population. To understand these processes it is imperative to trace exoplanets across time, both over galactic and human timescales. This thesis presents work in both of these areas, using data from the Transiting Exoplanet Survey Satellite (TESS). The bulk of this thesis focuses on the challenge of discovering new young exoplanets (age <1 Gyr) and understanding the variability of their potential host stars. This begins with building an extended population of young stars around which to search for exoplanets, illustrating the kinematic power of the recently launched Gaia satellite and resulting in a target list of over three million young stars. A dedicated new young star detrending pipeline is then presented, which is in turn used to search for new young exoplanets in stellar associations within TESS sectors 1-5. Although no new exoplanets were found, the pipeline’s effectiveness is demonstrated by recovering the previously known young exoplanets DS Tuc Ab and AU Mic b, alongside all other 2 min Targets of Interest (TOIs) from the 30 min cadence data alone. The completed young exoplanet search highlighted the challenging diversity of young stellar variability. To understand this variability, Kohonen Self-Organising Maps are used for the first time on a dedicated sample of young stars observed in the first year of TESS’s primary mission, in order to sort light-curves by topology and look for distinct variability classes. This analysis forms the first step in the YOUNGSTER programme, aiming to use knowledge of young star variability to inform more targeted detrending in future young exoplanet searches. Finally, this thesis presents work on tracing exoplanets through time on human timescales, updating and improving the ephemerides for all previously known Kepler planets (22) and candidates (4) which were observed with sufficient signal to noise in TESS. It also explores any transit-timing variations seen for these objects, including intriguing results for HAT-P-7b, Kepler-411d, K00075.01 and K00076.01