460 research outputs found
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Deep learning methods applied to digital elevation models: state of the art
Deep Learning (DL) has a wide variety of applications in various
thematic domains, including spatial information. Although with
limitations, it is also starting to be considered in operations
related to Digital Elevation Models (DEMs). This study aims to
review the methods of DL applied in the field of altimetric spatial
information in general, and DEMs in particular. Void Filling (VF),
Super-Resolution (SR), landform classification and hydrography
extraction are just some of the operations where traditional methods
are being replaced by DL methods. Our review concludes
that although these methods have great potential, there are
aspects that need to be improved. More appropriate terrain information
or algorithm parameterisation are some of the challenges
that this methodology still needs to face.Functional Quality of Digital Elevation Models in Engineering’ of the State Agency Research of SpainPID2019-106195RB- I00/AEI/10.13039/50110001103
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
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
Transfer Learning of Deep Learning Models for Cloud Masking in Optical Satellite Images
Los satélites de observación de la Tierra proporcionan una oportunidad sin precedentes para monitorizar nuestro planeta a alta resolución tanto espacial como temporal. Sin embargo, para procesar toda esta cantidad creciente de datos, necesitamos desarrollar modelos rápidos y precisos adaptados a las características específicas de los datos de cada sensor. Para los sensores ópticos, detectar las nubes en la imagen es un primer paso inevitable en la mayoría de aplicaciones tanto terrestres como oceánicas. Aunque detectar nubes brillantes y opacas es relativamente fácil, identificar automáticamente nubes delgadas semitransparentes o diferenciar nubes de nieve o superficies brillantes es mucho más difícil. Además, en el escenario actual, donde el número de sensores en el espacio crece constantemente, desarrollar metodologías para transferir modelos que funcionen con datos de nuevos satélites es una necesidad urgente. Por tanto, los objetivos de esta tesis son desarrollar modelos precisos de detección de nubes que exploten las diferentes propiedades de las imágenes de satélite y desarrollar metodologías para transferir esos modelos a otros sensores. La tesis está basada en cuatro trabajos los cuales proponen soluciones a estos problemas. En la primera contribución, "Multitemporal cloud masking in the Google Earth Engine", implementamos un modelo de detección de nubes multitemporal que se ejecuta en la plataforma Google Earth Engine y que supera los modelos operativos de Landsat-8. La segunda contribución, "Transferring deep learning models for Cloud Detection between Landsat-8 and Proba-V", es un caso de estudio de transferencia de un algoritmo de detección de nubes basado en aprendizaje profundo de Landsat-8 (resolución 30m, 12 bandas espectrales y muy buena calidad radiométrica) a Proba-V, que tiene una resolución de 333m, solo cuatro bandas y una calidad radiométrica peor. El tercer artículo, "Cross sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection", propone aprender una transformación de adaptación de dominios que haga que las imágenes de Proba-V se parezcan a las tomadas por Landsat-8 con el objetivo de transferir productos diseñados con datos de Landsat-8 a Proba-V. Finalmente, la cuarta contribución, "Towards global flood mapping onboard low cost satellites with machine learning", aborda simultáneamente la detección de inundaciones y nubes con un único modelo de aprendizaje profundo, implementado para que pueda ejecutarse a bordo de un CubeSat (ϕSat-I) con un chip acelerador de aplicaciones de inteligencia artificial. El modelo está entrenado en imágenes Sentinel-2 y demostramos cómo transferir este modelo a la cámara del ϕSat-I. Este modelo se lanzó en junio de 2021 a bordo de la misión WildRide de D-Orbit para probar su funcionamiento en el espacio.Remote sensing sensors onboard Earth observation satellites provide a great opportunity to monitor our planet at high spatial and temporal resolutions. Nevertheless, to process all this ever-growing amount of data, we need to develop fast and accurate models adapted to the specific characteristics of the data acquired by each sensor. For optical sensors, detecting the clouds present in the image is an unavoidable first step for most of the land and ocean applications. Although detecting bright and opaque clouds is relatively easy, automatically identifying thin semi-transparent clouds or distinguishing clouds from snow or bright surfaces is much more challenging. In addition, in the current scenario where the number of sensors in orbit is constantly growing, developing methodologies to transfer models across different satellite data is a pressing need. Henceforth, the overreaching goal of this Thesis is to develop accurate cloud detection models that exploit the different properties of the satellite images, and to develop methodologies to transfer those models across different sensors. The four contributions of this Thesis are stepping stones in that direction. In the first contribution,"Multitemporal cloud masking in the Google Earth Engine", we implemented a lightweight multitemporal cloud detection model that runs on the Google Earth Engine platform and which outperforms the operational models for Landsat-8. The second contribution, "Transferring deep learning models for Cloud Detection between Landsat-8 and Proba-V", is a case-study of transferring a deep learning based cloud detection algorithm from Landsat-8 (30m resolution, 12 spectral bands and very good radiometric quality) to Proba-V, which has a lower{333m resolution, only four bands and a less accurate radiometric quality. The third paper, "Cross sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection", proposes a learning-based domain adaptation transformation of Proba-V images to resemble those taken by Landsat-8, with the objective of transferring products designed on Landsat-8 to Proba-V. Finally, the fourth contribution, "Towards global flood mapping onboard low cost satellites with machine learning", tackles simultaneously cloud and flood water detection with a single deep learning model, which was implemented to run onboard a CubeSat (ϕSat-I) with an AI accelerator chip. In this case, the model is trained on Sentinel-2 and transferred to theϕSat-I camera. This model was launched in June 2021 onboard the Wild Ride D-Orbit mission in order to test its performance in space
Visual localisation of electricity pylons for power line inspection
Inspection of power infrastructure is a regular maintenance event. To date the inspection process has mostly been done manually, but there is growing interest in automating the process. The automation of the inspection process will require an accurate means for the localisation of the power infrastructure components. In this research, we studied the visual localisation of a pylon. The pylon is the most prominent component of the power infrastructure and can provide a context for the inspection of the other components. Point-based descriptors tend to perform poorly on texture less objects such as pylons, therefore we explored the localisation using convolutional neural networks and geometric constraints. The crossings of the pylon, or vertices, are salient points on the pylon. These vertices aid with recognition and pose estimation of the pylon. We were successfully able to use a convolutional neural network for the detection of the vertices. A model-based technique, geometric hashing, was used to establish the correspondence between the stored pylon model and the scene object. We showed the effectiveness of the method as a voting technique to determine the pose estimation from a single image. In a localisation framework, the method serves as the initialization of the tracking process. We were able to incorporate an extended Kalman filter for subsequent incremental tracking of the camera relative to the pylon. Also, we demonstrated an alternative tracking using heatmap details from the vertex detection. We successfully demonstrated the proposed algorithms and evaluated their effectiveness using a model pylon we built in the laboratory. Furthermore, we revalidated the results on a real-world outdoor electricity pylon. Our experiments illustrate that model-based techniques can be deployed as part of the navigation aspect of a robot
A weakly supervised learning pipeline for profiled fibre inspection
Automatic profiled fibre recognition and analysis can accelerate quality inspection and contributes to the upgrade of the textile industry. However, these tasks often require significant manual effort to generate instance‐level annotations for fully supervised training. In this paper, the authors propose a weakly supervised pipeline for profiled fibre inspection using electron‐microscopic (EM) images with only image‐level annotations. It automatically identifies fibre instances and estimates shape factors to facilitate fibre quality inspection. As the core of the pipeline, the weakly supervised network (WesNet) is designed to localize hundreds of crowded fibre samples by raw patch generation and fibre sample sifting. Particularly, the composite similarity measurement integrates different patch‐wise similarities, enabling the network to distinguish fibre from background robustly. For quality inspection, the pipeline further analyzes the fibre instances, utilizing several efficient techniques to estimate the shape factors. Experiments on the real fibre electron‐microscopic images demonstrate the efficacy and efficiency of the pipeline. Results show that WesNet outperforms several supervised and weakly supervised methods, including two state‐of‐the‐art weakly supervised networks
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