59 research outputs found
Resolution Study of a Hyperspectral Sensor Using Computed Tomography in the Process of Noise
Recently, a new type of hyperspectral imaging sensor has been proposed which simultaneously records both spectral data and multiple spatial dimensions. Unlike dispersive imaging spectrometers, chromo-tomographic hyperspectral imaging sensors (CTHIS) record two spatial dimensions as well as a spectral dimension using computed tomography (CT) techniques with only a finite number of spatially-spectrally diverse images. To date, the factors affecting resolution of these sensors have not been examined. This research examines factors affecting resolution, specifically the number of the focus planes needed to resolve a particular object calculated from a theoretical lower bound, determine a method of reconstructing a hyperspectral object in the presence of noise and background and verify the proposed method of reconstruction and the lower bound applying the proposed reconstruction method to laboratory data. Finally, a simple method is proposed and tested to use this sensor in the presence of atmospheric turbulence with a modified reconstructor to blindly estimate the seeing parameter
Unsupervised processing of hyperspectral images
National audienceThis work is a part of the CNRS project “ALOHA: Analyse en Ligne de dOnnées Hyperspectrales pour l’industrie Agroalimentaire” and of the ANR-OPTIFIN (Agence Nationale de la Recherche-OPTImisation des FINitions). The aim of these projects is to develop analytical tools adapted to the high throughput online analysis of samples by acquisition and processing of hyperspectral images. One output of the ALOHA and ANR OPTIFIN projects consists in the development of sequential algorithms for the deconvolution and on-the-fly unmixing of hyperspectral data. The main goal is to be able to predict and classify the quality of wood pieces renderings
Image Restoration for Remote Sensing: Overview and Toolbox
Remote sensing provides valuable information about objects or areas from a
distance in either active (e.g., RADAR and LiDAR) or passive (e.g.,
multispectral and hyperspectral) modes. The quality of data acquired by
remotely sensed imaging sensors (both active and passive) is often degraded by
a variety of noise types and artifacts. Image restoration, which is a vibrant
field of research in the remote sensing community, is the task of recovering
the true unknown image from the degraded observed image. Each imaging sensor
induces unique noise types and artifacts into the observed image. This fact has
led to the expansion of restoration techniques in different paths according to
each sensor type. This review paper brings together the advances of image
restoration techniques with particular focuses on synthetic aperture radar and
hyperspectral images as the most active sub-fields of image restoration in the
remote sensing community. We, therefore, provide a comprehensive,
discipline-specific starting point for researchers at different levels (i.e.,
students, researchers, and senior researchers) willing to investigate the
vibrant topic of data restoration by supplying sufficient detail and
references. Additionally, this review paper accompanies a toolbox to provide a
platform to encourage interested students and researchers in the field to
further explore the restoration techniques and fast-forward the community. The
toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS
Hyper-Skin: A Hyperspectral Dataset for Reconstructing Facial Skin-Spectra from RGB Images
We introduce Hyper-Skin, a hyperspectral dataset covering wide range of
wavelengths from visible (VIS) spectrum (400nm - 700nm) to near-infrared (NIR)
spectrum (700nm - 1000nm), uniquely designed to facilitate research on facial
skin-spectra reconstruction. By reconstructing skin spectra from RGB images,
our dataset enables the study of hyperspectral skin analysis, such as melanin
and hemoglobin concentrations, directly on the consumer device. Overcoming
limitations of existing datasets, Hyper-Skin consists of diverse facial skin
data collected with a pushbroom hyperspectral camera. With 330 hyperspectral
cubes from 51 subjects, the dataset covers the facial skin from different
angles and facial poses. Each hyperspectral cube has dimensions of
10241024448, resulting in millions of spectra vectors per
image. The dataset, carefully curated in adherence to ethical guidelines,
includes paired hyperspectral images and synthetic RGB images generated using
real camera responses. We demonstrate the efficacy of our dataset by showcasing
skin spectra reconstruction using state-of-the-art models on 31 bands of
hyperspectral data resampled in the VIS and NIR spectrum. This Hyper-Skin
dataset would be a valuable resource to NeurIPS community, encouraging the
development of novel algorithms for skin spectral reconstruction while
fostering interdisciplinary collaboration in hyperspectral skin analysis
related to cosmetology and skin's well-being. Instructions to request the data
and the related benchmarking codes are publicly available at:
\url{https://github.com/hyperspectral-skin/Hyper-Skin-2023}.Comment: Skin spectral datase
Single image super resolution for spatial enhancement of hyperspectral remote sensing imagery
Hyperspectral Imaging (HSI) has emerged as a powerful tool for capturing detailed spectral information across various applications, such as remote sensing, medical imaging, and material identification. However, the limited spatial resolution of acquired HSI data poses a challenge due to hardware and acquisition constraints. Enhancing the spatial resolution of HSI is crucial for improving image processing tasks, such as object detection and classification. This research focuses on utilizing Single Image Super Resolution (SISR) techniques to enhance HSI, addressing four key challenges: the efficiency of 3D Deep Convolutional Neural Networks (3D-DCNNs) in HSI enhancement, minimizing spectral distortions, tackling data scarcity, and improving state-of-the-art performance.
The thesis establishes a solid theoretical foundation and conducts an in-depth literature review to identify trends, gaps, and future directions in the field of HSI enhancement. Four chapters present novel research targeting each of the aforementioned challenges. All experiments are performed using publicly available datasets, and the results are evaluated both qualitatively and quantitatively using various commonly used metrics.
The findings of this research contribute to the development of a novel 3D-CNN architecture known as 3D Super Resolution CNN 333 (3D-SRCNN333). This architecture demonstrates the capability to enhance HSI with minimal spectral distortions while maintaining acceptable computational cost and training time. Furthermore, a Bayesian-optimized hybrid spectral spatial loss function is devised to improve the spatial quality and minimize spectral distortions, combining the best characteristics of both domains.
Addressing the challenge of data scarcity, this thesis conducts a thorough study on Data Augmentation techniques and their impact on the spectral signature of HSI. A new Data Augmentation technique called CutMixBlur is proposed, and various combinations of Data Augmentation techniques are evaluated to address the data scarcity challenge, leading to notable enhancements in performance.
Lastly, the 3D-SRCNN333 architecture is extended to the frequency domain and wavelet domain to explore their advantages over the spatial domain. The experiments reveal promising results with the 3D Complex Residual SRCNN (3D-CRSRCNN), surpassing the performance of 3D-SRCNN333.
The findings presented in this thesis have been published in reputable conferences and journals, indicating their contribution to the field of HSI enhancement. Overall, this thesis provides valuable insights into the field of HSI-SISR, offering a thorough understanding of the advancements, challenges, and potential applications. The developed algorithms and methodologies contribute to the broader goal of improving the spatial resolution and spectral fidelity of HSI, paving the way for further advancements in scientific research and practical implementations.Hyperspectral Imaging (HSI) has emerged as a powerful tool for capturing detailed spectral information across various applications, such as remote sensing, medical imaging, and material identification. However, the limited spatial resolution of acquired HSI data poses a challenge due to hardware and acquisition constraints. Enhancing the spatial resolution of HSI is crucial for improving image processing tasks, such as object detection and classification. This research focuses on utilizing Single Image Super Resolution (SISR) techniques to enhance HSI, addressing four key challenges: the efficiency of 3D Deep Convolutional Neural Networks (3D-DCNNs) in HSI enhancement, minimizing spectral distortions, tackling data scarcity, and improving state-of-the-art performance.
The thesis establishes a solid theoretical foundation and conducts an in-depth literature review to identify trends, gaps, and future directions in the field of HSI enhancement. Four chapters present novel research targeting each of the aforementioned challenges. All experiments are performed using publicly available datasets, and the results are evaluated both qualitatively and quantitatively using various commonly used metrics.
The findings of this research contribute to the development of a novel 3D-CNN architecture known as 3D Super Resolution CNN 333 (3D-SRCNN333). This architecture demonstrates the capability to enhance HSI with minimal spectral distortions while maintaining acceptable computational cost and training time. Furthermore, a Bayesian-optimized hybrid spectral spatial loss function is devised to improve the spatial quality and minimize spectral distortions, combining the best characteristics of both domains.
Addressing the challenge of data scarcity, this thesis conducts a thorough study on Data Augmentation techniques and their impact on the spectral signature of HSI. A new Data Augmentation technique called CutMixBlur is proposed, and various combinations of Data Augmentation techniques are evaluated to address the data scarcity challenge, leading to notable enhancements in performance.
Lastly, the 3D-SRCNN333 architecture is extended to the frequency domain and wavelet domain to explore their advantages over the spatial domain. The experiments reveal promising results with the 3D Complex Residual SRCNN (3D-CRSRCNN), surpassing the performance of 3D-SRCNN333.
The findings presented in this thesis have been published in reputable conferences and journals, indicating their contribution to the field of HSI enhancement. Overall, this thesis provides valuable insights into the field of HSI-SISR, offering a thorough understanding of the advancements, challenges, and potential applications. The developed algorithms and methodologies contribute to the broader goal of improving the spatial resolution and spectral fidelity of HSI, paving the way for further advancements in scientific research and practical implementations
END OF LIFE MANAGEMENT OF ELECTRONIC WASTE
Electronic products are becoming obsolete at a very high rate due to rapid changes in consumer demand and technological advancements. However, on other hand End-of-Life (EOL) management of electronic products is not effectively approached while these products offer huge opportunities for effective recycling. In this context, this thesis has highlighted the current practices and issues related to EOL management of electronic products focusing on their different material compositions, the uses of their raw materials in the circular economy perspective.
The thesis proposes the introduction of digital technologies into the recycling process to improve efficiency. More specifically, this thesis has focused on the corona electrostatic separation process and the improvement of efficiency based on the simulation of the particle trajectories to identify the most effective parameters. Thus, in this frame, a numerical model to predict the particle trajectories in a corona electrostatic separator is developed using COMSOL Multiphysics and MATLAB software and validated with experimental trials.
The recycling of electronic waste is becoming challenging due to its diverse and constantly changing material composition. In this regard, this thesis illustrates the use of non-destructive visible near-infrared hyperspectral imaging (VNIR-HSI) technique to identify material accurately; the effectiveness of VNIR-HSI is demonstrated through an experimental campaign combined with machine learning models, such as Support Vector Machine, K-Nearest Neighbors and Neural Network.Nonostante i prodotti elettronici diventino obsoleti ad un ritmo molto elevato, a causa dei rapidi cambiamenti nella domanda dei consumatori e dei progressi tecnologici, la gestione del loro fine vita (End-of-Life (EOL)) non viene affrontata in modo efficace benché offra, invece, grandi opportunità di riciclo. In questo contesto, questa tesi ha evidenziato le attuali pratiche e problematiche relative alla gestione del fine vita dei prodotti elettronici concentrandosi sulla loro diversa composizione, l’utilizzo delle materie prime seconde ricavabili in una prospettiva di economia circolare.
La tesi propone l’introduzione di tecnologie digitali nel processo di riciclo per migliorarne l'efficienza. In particolare, questa tesi si è concentrata sul processo di separazione elettrostatica a corona e sul miglioramento dell'efficienza grazie alla simulazione delle traiettorie delle particelle per identificare i parametri più efficaci. Pertanto, in questo studio, utilizzando i software COMSOL Multiphysics e MATLAB, è stato sviluppato un modello numerico per prevedere le traiettorie delle particelle in un separatore elettrostatico a corona; il modello è stato poi validato con prove sperimentali.
Il riciclo dei rifiuti elettronici sta diventando sempre più complesso a causa della presenza di mix di materiali diversificati e in continua evoluzione. A questo proposito, la tecnologia di visione iperspettrale non distruttiva basata su lunghezze d’onda nel visibile e nel vicino infrarosso (VNIR-HSI) è stata utilizzata in questo lavoro di tesi per identificare il materiale in modo preciso; l'efficacia di VNIR-HSI, combinato con modelli di apprendimento automatico, come la Support Vector Machine, K-Nearest Neighbors e Neural Network, viene dimostrata attraverso una campagna sperimentale
Review on Active and Passive Remote Sensing Techniques for Road Extraction
Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe
Learnable Reconstruction Methods from RGB Images to Hyperspectral Imaging: A Survey
Hyperspectral imaging enables versatile applications due to its competence in
capturing abundant spatial and spectral information, which are crucial for
identifying substances. However, the devices for acquiring hyperspectral images
are expensive and complicated. Therefore, many alternative spectral imaging
methods have been proposed by directly reconstructing the hyperspectral
information from lower-cost, more available RGB images. We present a thorough
investigation of these state-of-the-art spectral reconstruction methods from
the widespread RGB images. A systematic study and comparison of more than 25
methods has revealed that most of the data-driven deep learning methods are
superior to prior-based methods in terms of reconstruction accuracy and quality
despite lower speeds. This comprehensive review can serve as a fruitful
reference source for peer researchers, thus further inspiring future
development directions in related domains
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