ORCID Profile
0000-0002-4960-7554
Current Organisations
Universite de Haute-Alsace
,
Monash University
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2016
Publisher: Springer Science and Business Media LLC
Date: 03-03-2018
DOI: 10.1007/S11548-018-1713-Y
Abstract: Surgery is one of the riskiest and most important medical acts that are performed today. The need to improve patient outcomes and surgeon training, and to reduce the costs of surgery, has motivated the equipment of operating rooms with sensors that record surgical interventions. The richness and complexity of the data that are collected call for new methods to support computer-assisted surgery. The aim of this paper is to support the monitoring of junior surgeons learning their surgical skill sets. Our method is fully automatic and takes as input a series of surgical interventions each represented by a low-level recording of all activities performed by the surgeon during the intervention (e.g., cut the skin with a scalpel). Our method produces a curve describing the process of standardization of the behavior of junior surgeons. Given the fact that junior surgeons receive constant feedback from senior surgeons during surgery, these curves can be directly interpreted as learning curves. Our method is assessed using the behavior of a junior surgeon in anterior cervical discectomy and fusion surgery over his first three years after residency. They revealed the ability of the method to accurately represent the surgical skill evolution. We also showed that the learning curves can be computed by phases allowing a finer evaluation of the skill progression. Preliminary results suggest that our approach constitutes a useful addition to surgical training monitoring.
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 12-2018
Publisher: Elsevier BV
Date: 09-2012
Publisher: IEEE
Date: 09-2016
Publisher: Public Library of Science (PLoS)
Date: 21-02-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2009
Publisher: IEEE
Date: 07-2020
Publisher: Springer Science and Business Media LLC
Date: 25-04-2017
DOI: 10.1007/S10549-017-4239-Z
Abstract: To improve microscopic evaluation of immune cells relevant in breast cancer oncoimmunology, we aim at distinguishing normal infiltration patterns from lymphocytic lobulitis by advanced image analysis. We consider potential immune cell variations due to the menstrual cycle and oral contraceptives in non-neoplastic mammary gland tissue. Lymphocyte and macrophage distributions were analyzed in the anatomical context of the resting mammary gland in immunohistochemically stained digital whole slide images obtained from 53 reduction mammoplasty specimens. Our image analysis workflow included automated regions of interest detection, immune cell recognition, and co-registration of regions of interest. In normal lobular epithelium, seven CD8[Formula: see text] lymphocytes per 100 epithelial cells were present on average and about 70% of this T-lymphocyte population was lined up along the basal cell layer in close proximity to the epithelium. The density of CD8[Formula: see text] T-cell was 1.6 fold higher in the luteal than in the follicular phase in spontaneous menstrual cycles and 1.4 fold increased under the influence of oral contraceptives, and not co-localized with epithelial proliferation. CD4[Formula: see text] T-cells were infrequent. Abundant CD163[Formula: see text] macrophages were widely spread, including the interstitial compartment, with minor variation during the menstrual cycle. Spatial patterns of different immune cell subtypes determine the range of normal, as opposed to inflammatory conditions of the breast tissue microenvironment. Advanced image analysis enables quantification of hormonal effects, refines lymphocytic lobulitis, and shows potential for comprehensive biopsy evaluation in oncoimmunology.
Publisher: Elsevier BV
Date: 04-2013
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 06-2011
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 13-09-2021
Publisher: Elsevier BV
Date: 12-2014
Publisher: Elsevier BV
Date: 09-2017
DOI: 10.1016/J.ARTMED.2017.03.007
Abstract: More than half a million surgeries are performed every day worldwide, which makes surgery one of the most important component of global health care. In this context, the objective of this paper is to introduce a new method for the prediction of the possible next task that a surgeon is going to perform during surgery. We formulate the problem as finding the optimal registration of a partial sequence to a complete reference sequence of surgical activities. We propose an efficient algorithm to find the optimal partial alignment and a prediction system using maximum a posteriori probability estimation and filtering. We also introduce a weighting scheme allowing to improve the predictions by taking into account the relative similarity between the current surgery and a set of pre-recorded surgeries. Our method is evaluated on two types of neurosurgical procedures: lumbar disc herniation removal and anterior cervical discectomy. Results show that our method outperformed the state of the art by predicting the next task that the surgeon will perform with 95% accuracy. This work shows that, even from the low-level description of surgeries and without other sources of information, it is often possible to predict the next surgical task when the conditions are consistent with the previously recorded surgeries. We also showed that our method is able to assess when there is actually a large ergence between the predictions and decide that it is not reasonable to make a prediction.
Publisher: SPIE
Date: 17-09-2009
DOI: 10.1117/12.830392
Publisher: Springer Science and Business Media LLC
Date: 22-09-2016
Publisher: IEEE
Date: 05-2012
Publisher: IEEE
Date: 07-2020
Publisher: Elsevier BV
Date: 09-2021
Publisher: IEEE
Date: 11-2017
Publisher: Springer Science and Business Media LLC
Date: 28-05-2019
Publisher: Springer International Publishing
Date: 2022
Publisher: IEEE
Date: 09-2015
Publisher: MDPI
Date: 21-06-2022
Publisher: Elsevier BV
Date: 09-2018
DOI: 10.1016/J.ARTMED.2018.08.002
Abstract: The analysis of surgical motion has received a growing interest with the development of devices allowing their automatic capture. In this context, the use of advanced surgical training systems makes an automated assessment of surgical trainee possible. Automatic and quantitative evaluation of surgical skills is a very important step in improving surgical patient care. In this paper, we present an approach for the discovery and ranking of discriminative and interpretable patterns of surgical practice from recordings of surgical motions. A pattern is defined as a series of actions or events in the kinematic data that together are distinctive of a specific gesture or skill level. Our approach is based on the decomposition of continuous kinematic data into a set of overlapping gestures represented by strings (bag of words) for which we compute comparative numerical statistic (tf-idf) enabling the discriminative gesture discovery via its relative occurrence frequency. We carried out experiments on three surgical motion datasets. The results show that the patterns identified by the proposed method can be used to accurately classify in idual gestures, skill levels and surgical interfaces. We also present how the patterns provide a detailed feedback on the trainee skill assessment. The proposed approach is an interesting addition to existing learning tools for surgery as it provides a way to obtain a feedback on which parts of an exercise have been used to classify the attempt as correct or incorrect.
Publisher: IEEE
Date: 11-2017
Publisher: Informa UK Limited
Date: 19-03-2022
Publisher: IEEE
Date: 12-2014
DOI: 10.1109/ICDM.2014.27
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Publisher: Springer Science and Business Media LLC
Date: 23-04-2015
DOI: 10.1007/S11548-015-1195-0
Abstract: Analyzing surgical activities has received a growing interest in recent years. Several methods have been proposed to identify surgical activities and surgical phases from data acquired in operating rooms. These context-aware systems have multiple applications including: supporting the surgical team during the intervention, improving the automatic monitoring, designing new teaching paradigms. In this paper, we use low-level recordings of the activities that are performed by a surgeon to automatically predict the current (high-level) phase of the surgery. We augment a decision tree algorithm with the ability to consider the local context of the surgical activities and a hierarchical clustering algorithm. Experiments were performed on 22 surgeries of lumbar disk herniation. We obtained an overall precision of 0.843 in detecting phases of 51,489 single activities. We also assess the robustness of the method with regard to noise. We show that using the local context allows us to improve the results compared with methods only considering single activity. Experiments show that the use of the local context makes our method very robust to noise and that clustering the input data first improves the predictions.
Publisher: Elsevier BV
Date: 2018
DOI: 10.2139/SSRN.3199114
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 11-05-2018
DOI: 10.1007/S11548-018-1775-X
Abstract: Surgical processes are generally only studied by identifying differences in populations such as participants or level of expertise. But the similarity between this population is also important in understanding the process. We therefore proposed to study these two aspects. In this article, we show how similarities in process workflow within a population can be identified as sequential surgical signatures. To this purpose, we have proposed a pattern mining approach to identify these signatures. We validated our method with a data set composed of seventeen micro-surgical suturing tasks performed by four participants with two levels of expertise. We identified sequential surgical signatures specific to each participant, shared between participants with and without the same level of expertise. These signatures are also able to perfectly define the level of expertise of the participant who performed a new micro-surgical suturing task. However, it is more complicated to determine who the participant is, and the method correctly determines this information in only 64% of cases. We show for the first time the concept of sequential surgical signature. This new concept has the potential to further help to understand surgical procedures and provide useful knowledge to define future CAS systems.
Publisher: Springer Science and Business Media LLC
Date: 12-09-2017
Publisher: Springer Science and Business Media LLC
Date: 15-09-2018
DOI: 10.1007/S11548-017-1666-6
Abstract: Teleoperated robotic systems are nowadays routinely used for specific interventions. Benefits of robotic training courses have already been acknowledged by the community since manipulation of such systems requires dedicated training. However, robotic surgical simulators remain expensive and require a dedicated human-machine interface. We present a low-cost contactless optical sensor, the Leap Motion, as a novel control device to manipulate the RAVEN-II robot. We compare peg manipulations during a training task with a contact-based device, the electro-mechanical Sigma.7. We perform two complementary analyses to quantitatively assess the performance of each control method: a metric-based comparison and a novel unsupervised spatiotemporal trajectory clustering. We show that contactless control does not offer as good manipulability as the contact-based. Where part of the metric-based evaluation presents the mechanical control better than the contactless one, the unsupervised spatiotemporal trajectory clustering from the surgical tool motions highlights specific signature inferred by the human-machine interfaces. Even if the current implementation of contactless control does not overtake manipulation with high-standard mechanical interface, we demonstrate that using the optical sensor complete control of the surgical instruments is feasible. The proposed method allows fine tracking of the trainee's hands in order to execute dexterous laparoscopic training gestures. This work is promising for development of future human-machine interfaces dedicated to robotic surgical training systems.
Publisher: Springer Science and Business Media LLC
Date: 24-10-2023
Publisher: Informa UK Limited
Date: 11-12-2012
Publisher: Springer Science and Business Media LLC
Date: 12-03-2008
DOI: 10.1155/2008/374095
Publisher: MDPI AG
Date: 27-12-2022
DOI: 10.3390/RS15010151
Abstract: In the context of global change, up-to-date land use land cover (LULC) maps is a major challenge to assess pressures on natural areas. These maps also allow us to assess the evolution of land cover and to quantify changes over time (such as urban sprawl), which is essential for having a precise understanding of a given territory. Few studies have combined information from Sentinel-1 and Sentinel-2 imagery, but merging radar and optical imagery has been shown to have several benefits for a range of study cases, such as semantic segmentation or classification. For this study, we used a newly produced dataset, MultiSenGE, which provides a set of multitemporal and multimodal patches over the Grand-Est region in France. To merge these data, we propose a CNN approach based on spatio-temporal and spatio-spectral feature fusion, ConvLSTM+Inception-S1S2. We used a U-Net base model and ConvLSTM extractor for spatio-temporal features and an inception module for the spatio-spectral features extractor. The results show that describing an overrepresented class is preferable to map urban fabrics (UF). Furthermore, the addition of an Inception module on a date allowing the extraction of spatio-spectral features improves the classification results. Spatio-spectro-temporal method (ConvLSTM+Inception-S1S2) achieves higher global weighted F1Score than all other methods tested.
Publisher: Elsevier BV
Date: 12-2014
Publisher: Springer Science and Business Media LLC
Date: 23-09-2016
DOI: 10.1038/SREP33322
Abstract: Scattered inflammatory cells are commonly observed in mammary gland tissue, most likely in response to normal cell turnover by proliferation and apoptosis, or as part of immunosurveillance. In contrast, lymphocytic lobulitis (LLO) is a recurrent inflammation pattern, characterized by lymphoid cells infiltrating lobular structures, that has been associated with increased familial breast cancer risk and immune responses to clinically manifest cancer. The mechanisms and pathogenic implications related to the inflammatory microenvironment in breast tissue are still poorly understood. Currently, the definition of inflammation is mainly descriptive, not allowing a clear distinction of LLO from physiological immunological responses and its role in oncogenesis remains unclear. To gain insights into the prognostic potential of inflammation, we developed an agent-based model of immune and epithelial cell interactions in breast lobular epithelium. Physiological parameters were calibrated from breast tissue s les of women who underwent reduction mammoplasty due to orthopedic or cosmetic reasons. The model allowed to investigate the impact of menstrual cycle length and hormone status on inflammatory responses to cell turnover in the breast tissue. Our findings suggested that the immunological context, defined by the immune cell density, functional orientation and spatial distribution, contains prognostic information previously not captured by conventional diagnostic approaches.
Publisher: IEEE
Date: 07-2010
Publisher: IEEE
Date: 11-2021
Publisher: Elsevier BV
Date: 11-2010
Publisher: ACM
Date: 11-08-2013
Publisher: Elsevier BV
Date: 02-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2013
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 11-2020
Publisher: IEEE
Date: 09-2017
Publisher: IEEE
Date: 07-2019
Publisher: IEEE
Date: 2008
Publisher: Elsevier BV
Date: 11-2014
DOI: 10.1016/J.ARTMED.2014.10.007
Abstract: Surgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest. Desires to improve patient outcomes and surgeon training, and to reduce the costs of surgery, all motivate a better understanding of surgical practices. To facilitate this, surgeons have started recording the activities that are performed during surgery. New methods have to be developed to be able to make the most of this extremely rich and complex data. The objective of this work is to enable the simultaneous comparison of a set of surgeries, in order to be able to extract high-level information about surgical practices. We introduce non-linear temporal scaling (NLTS): a method that finds a multiple alignment of a set of surgeries. Experiments are carried out on a set of lumbar disc neurosurgeries. We assess our method both on a highly standardised phase of the surgery (closure) and on the whole surgery. Experiments show that NLTS makes it possible to consistently derive standards of surgical practice and to understand differences between groups of surgeries. We take the training of surgeons as the common theme for the evaluation of the results and highlight, for ex le, the main differences between the practices of junior and senior surgeons in the removal of a lumbar disc herniation. NLTS is an effective and efficient method to find a multiple alignment of a set of surgeries. NLTS realigns a set of sequences along their intrinsic timeline, which makes it possible to extract standards of surgical practices.
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Science and Business Media LLC
Date: 13-07-2018
DOI: 10.1007/S11548-018-1824-5
Abstract: The development of common ontologies has recently been identified as one of the key challenges in the emerging field of surgical data science (SDS). However, past and existing initiatives in the domain of surgery have mainly been focussing on in idual groups and failed to achieve widespread international acceptance by the research community. To address this challenge, the authors of this paper launched a European initiative-OntoSPM Collaborative Action-with the goal of establishing a framework for joint development of ontologies in the field of SDS. This manuscript summarizes the goals and the current status of the international initiative. A workshop was organized in 2016, gathering the main European research groups having experience in developing and using ontologies in this domain. It led to the conclusion that a common ontology for surgical process models (SPM) was absolutely needed, and that the existing OntoSPM ontology could provide a good starting point toward the collaborative design and promotion of common, standard ontologies on SPM. The workshop led to the OntoSPM Collaborative Action-launched in mid-2016-with the objective to develop, maintain and promote the use of common ontologies of SPM relevant to the whole domain of SDS. The fundamental concept, the architecture, the management and curation of the common ontology have been established, making it ready for wider public use. The OntoSPM Collaborative Action has been in operation for 24 months, with a growing dedicated membership. Its main result is a modular ontology, undergoing constant updates and extensions, based on the experts' suggestions. It remains an open collaborative action, which always welcomes new contributors and applications.
Publisher: Springer Science and Business Media LLC
Date: 16-10-2021
Publisher: IEEE
Date: 07-2020
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 30-05-2018
Publisher: Springer Science and Business Media LLC
Date: 07-09-2020
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 10-2007
Publisher: Elsevier BV
Date: 09-2016
Publisher: Elsevier BV
Date: 10-2017
DOI: 10.1016/J.ARTMED.2017.09.002
Abstract: Surgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest to understand and analyze surgical behaviors. This article addresses the issue of identifying discriminative patterns of surgical practice from recordings of surgeries. These recordings are sequences of low-level surgical activities representing the actions performed by surgeons during surgeries. To discover patterns that are specific to a group of surgeries, we use the vector space model (VSM) which is originally an algebraic model for representing text documents. We split long sequences of surgical activities into subsequences of consecutive activities. We then compute the relative frequencies of these subsequences using the tf*idf framework and we use the Cosine similarity to classify the sequences. This process makes it possible to discover which patterns discriminate one set of surgeries recordings from another set. Experiments were performed on 40 neurosurgeries of anterior cervical discectomy (ACD). The results demonstrate that our method accurately identifies patterns that can discriminate between (1) locations where the surgery took place, (2) levels of expertise of surgeons (i.e., expert vs. intermediate) and even (3) in idual surgeons who performed the intervention. We also show how the tf*idf weight vector can be used to both visualize the most interesting patterns and to highlight the parts of a given surgery that are the most interesting. Identifying patterns that discriminate groups of surgeon is a very important step in improving the understanding of surgical processes. The proposed method finds discriminative and interpretable patterns in sequences of surgical activities. Our approach provides intuitive results, as it identifies automatically the set of patterns explaining the differences between the groups.
Publisher: IEEE
Date: 04-2019
Publisher: IEEE
Date: 09-2013
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 02-03-2019
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 12-2017
Publisher: IEEE
Date: 12-2009
Publisher: Elsevier BV
Date: 03-2020
DOI: 10.1016/J.REVMED.2019.12.014
Abstract: Clinical reasoning is at the heart of physicians' competence, as it allows them to make diagnoses. However, diagnostic errors are common, due to the existence of reasoning biases. Artificial intelligence is undergoing unprecedented development in this context. It is increasingly seen as a solution to improve the diagnostic performance of physicians, or even to perform this task for them, in a totally autonomous and more efficient way. In order to understand the challenges associated with the development of artificial intelligence, it is important to understand how the machine works to make diagnoses, what are the similarities and differences with the physician's diagnostic reasoning, and what are the consequences for medical training and practice.
Publisher: Elsevier BV
Date: 03-2017
DOI: 10.1016/J.JBI.2017.02.001
Abstract: Each surgical procedure is unique due to patient's and also surgeon's particularities. In this study, we propose a new approach to distinguish surgical behaviors between surgical sites, levels of expertise and in idual surgeons thanks to a pattern discovery method. The developed approach aims to distinguish surgical behaviors based on shared longest frequent sequential patterns between surgical process models. To allow clustering, we propose a new metric called SLFSP. The approach is validated by comparison with a clustering method using Dynamic Time Warping as a metric to characterize the similarity between surgical process models. Our method outperformed the existing approach. It was able to make a perfect distinction between surgical sites (accuracy of 100%). We reached an accuracy superior to 90% and 85% for distinguishing levels of expertise and in idual surgeons. Clustering based on shared longest frequent sequential patterns outperformed the previous study based on time analysis. The proposed method shows the feasibility of comparing surgical process models, not only by their duration but also by their structure of activities. Furthermore, patterns may show risky behaviors, which could be an interesting information for surgical training to prevent adverse events.
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 30-07-2019
DOI: 10.1007/S11548-019-02039-4
Abstract: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of trainees' surgical skills in order to improve surgical practice. In this paper, we designed a convolutional neural network (CNN) to classify surgical skills by extracting latent patterns in the trainees' motions performed during robotic surgery. The method is validated on the JIGSAWS dataset for two surgical skills evaluation tasks: classification and regression. Our results show that deep neural networks constitute robust machine learning models that are able to reach new competitive state-of-the-art performance on the JIGSAWS dataset. While we leveraged from CNNs' efficiency, we were able to minimize its black-box effect using the class activation map technique. This characteristic allowed our method to automatically pinpoint which parts of the surgery influenced the skill evaluation the most, thus allowing us to explain a surgical skill classification and provide surgeons with a novel personalized feedback technique. We believe this type of interpretable machine learning model could integrate within "Operation Room 2.0" and support novice surgeons in improving their skills to eventually become experts.
Publisher: Copernicus GmbH
Date: 03-08-2020
DOI: 10.5194/ISPRS-ANNALS-V-3-2020-549-2020
Abstract: Abstract. Landscape reconstruction is crucial to measure the effects of climate change or past land use on current bio ersity. In particular, retracing past phenological changes can serve as a basis for explaining current patterns of plant communities and predict the future extinction of species. Old spatial data are currently used to reconstruct vegetation changes, both morphologically (with landscape metrics) and semantically (grasslands to crops for instance). However, poor radiometric properties (single panchromatic channel, illumination variation, etc.) do not offer the possibility to compute environmental variables (e.g. NDVI and color indices), which strongly limits long-term phenological reconstruction. In this study, we propose a workflow for reconstructing phenological trajectories of grasslands from 1958 to 2011, in the French central Vosges, from old aerial black and white (B& W) photographs. Noise and vignetting corruptions were first corrected in B& W photographs with non-local filtering algorithms. Panchromatic scans were then colorized with a Generative Adversarial Network (GAN). Based on the predicted channels, we finally computed digital greenness metrics (Green Chromatic Coordinate, Excess Greenness) to measure vegetation activity in grasslands. Our results demonstrated the feasibility of reconstructing long-term phenological trajectories from legacy photographs with insights at different levels: (1) the proposed correction methods provided radiometric improvements in old aerial missions (2) the colorization process led to promising and plausible colorized historical products (3) digital greenness metrics were useful for describing past vegetation activity.
Publisher: IEEE
Date: 07-2019
Publisher: Elsevier BV
Date: 07-2016
DOI: 10.1016/J.COMPBIOMED.2016.05.004
Abstract: Ongoing research into inflammatory conditions raises an increasing need to evaluate immune cells in histological sections in biologically relevant regions of interest (ROIs). Herein, we compare different approaches to automatically detect lobular structures in human normal breast tissue in digitized whole slide images (WSIs). This automation is required to perform objective and consistent quantitative studies on large data sets. In normal breast tissue from nine healthy patients immunohistochemically stained for different markers, we evaluated and compared three different image analysis methods to automatically detect lobular structures in WSIs: (1) a bottom-up approach using the cell-based data for subsequent tissue level classification, (2) a top-down method starting with texture classification at tissue level analysis of cell densities in specific ROIs, and (3) a direct texture classification using deep learning technology. All three methods result in comparable overall quality allowing automated detection of lobular structures with minor advantage in sensitivity (approach 3), specificity (approach 2), or processing time (approach 1). Combining the outputs of the approaches further improved the precision. Different approaches of automated ROI detection are feasible and should be selected according to the in idual needs of biomarker research. Additionally, detected ROIs could be used as a basis for quantification of immune infiltration in lobular structures.
Publisher: IEEE
Date: 07-2013
Publisher: Elsevier BV
Date: 02-2010
Publisher: Springer Science and Business Media LLC
Date: 09-04-2018
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 09-2012
DOI: 10.1109/HISB.2012.35
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2011
DOI: 10.1142/S0129065711003024
Abstract: Satellite Image Time Series (SITS) provide us with precious information on land cover evolution. By studying these series of images we can both understand the changes of specific areas and discover global phenomena that spread over larger areas. Changes that can occur throughout the sensing time can spread over very long periods and may have different start time and end time depending on the location, which complicates the mining and the analysis of series of images. This work focuses on frequent sequential pattern mining (FSPM) methods, since this family of methods fits the above-mentioned issues. This family of methods consists of finding the most frequent evolution behaviors, and is actually able to extract long-term changes as well as short term ones, whenever the change may start and end. However, applying FSPM methods to SITS implies confronting two main challenges, related to the characteristics of SITS and the domain's constraints. First, satellite images associate multiple measures with a single pixel (the radiometric levels of different wavelengths corresponding to infra-red, red, etc.), which makes the search space multi-dimensional and thus requires specific mining algorithms. Furthermore, the non evolving regions, which are the vast majority and overwhelm the evolving ones, challenge the discovery of these patterns. We propose a SITS mining framework that enables discovery of these patterns despite these constraints and characteristics. Our proposal is inspired from FSPM and provides a relevant visualization principle. Experiments carried out on 35 images sensed over 20 years show the proposed approach makes it possible to extract relevant evolution behaviors.
Publisher: Copernicus GmbH
Date: 17-05-2022
DOI: 10.5194/ISPRS-ANNALS-V-3-2022-635-2022
Abstract: Abstract. This paper presents MultiSenGE that is a new large scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. MultiSenGE contains 8,157 patches of 256 × 256 pixels for the Sentinel-2 L2A , Sentinel-1 GRD images in VV-VH polarization and a Regional large scale Land Use/Land Cover (LULC) topographic reference database. With MultiSenGE, we contribute to the recents developments towards shared data use and machine learning methods in the field of environmental science. The purpose of this dataset is to propose relevant and easy-access dataset to explore deep learning methods. We use MultiSenGE to evaluate the performance for urban areas using well-known deep learning techniques. These results serve as a baseline for future research on remote sensing applications using the multi-temporal and multimodal aspects of MultiSenGE. With all patches georeferenced at a 10 meters spatial resolution covering the whole Grand-Est Region, MultiSenGE provides an opportunity for environmental benchmark dataset will help to advance data-driven techniques for land use/land cover remote sensing applications.
Publisher: IEEE
Date: 09-2019
Publisher: IEEE
Date: 06-2011
Publisher: CMA Impact Inc.
Date: 12-2019
DOI: 10.1503/CMAJ.190506
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Elsevier BV
Date: 09-2021
Location: France
No related grants have been discovered for Germain Forestier.