Examinando por Autor "Paoletti, Mercedes Eugenia"
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Publicación AAtt-CNN: Automatic Attention-Based Convolutional Neural Networks for Hyperspectral Image Classification(IEEE, 2023) Paoletti, Mercedes Eugenia; Moreno Álvarez, Sergio; xue, yu; Haut, Juan M.; Plaza, Antonio; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0002-9069-7547; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0002-9613-1659Convolutional models have provided outstanding performance in the analysis of hyperspectral images (HSIs). These architectures are carefully designed to extract intricate information from nonlinear features for classification tasks. Notwithstanding their results, model architectures are manually engineered and further optimized for generalized feature extraction. In general terms, deep architectures are time-consuming for complex scenarios, since they require fine-tuning. Neural architecture search (NAS) has emerged as a suitable approach to tackle this shortcoming. In parallel, modern attention-based methods have boosted the recognition of sophisticated features. The search for optimal neural architectures combined with attention procedures motivates the development of this work. This article develops a new method to automatically design and optimize convolutional neural networks (CNNs) for HSI classification using channel-based attention mechanisms. Specifically, 1-D and spectral–spatial (3-D) classifiers are considered to handle the large amount of information contained in HSIs from different perspectives. Furthermore, the proposed automatic attention-based CNN ( AAtt-CNN ) method meets the requirement to lower the large computational overheads associated with architectural search. It is compared with current state-of-the-art (SOTA) classifiers. Our experiments, conducted using a wide range of HSI images, demonstrate that AAtt-CNN succeeds in finding optimal architectures for classification, leading to SOTA results.Publicación Cloud Implementation of Extreme Learning Machine for Hyperspectral Image Classification(IEEE, 2023) Haut, Juan M.; Moreno Álvarez, Sergio; Moreno Ávila, Enrique; Ayma Quirita, Victor Andrés; Pastor Vargas, Rafael; Paoletti, Mercedes Eugenia; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0003-2987-2761; https://orcid.org/0000-0002-4089-9538; https://orcid.org/0000-0003-1030-3729Classifying remotely sensed hyperspectral images (HSIs) became a computationally demanding task given the extensive information contained throughout the spectral dimension. Furthermore, burgeoning data volumes compound inherent computational and storage challenges for data processing and classification purposes. Given their distributed processing capabilities, cloud environments have emerged as feasible solutions to handle these hurdles. This encourages the development of innovative distributed classification algorithms that take full advantage of the processing capabilities of such environments. Recently, computational-efficient methods have been implemented to boost network convergence by reducing the required training calculations. This letter develops a novel cloud-based distributed implementation of the extreme learning machine ( CC-ELM ) algorithm for efficient HSI classification. The proposal implements a fault-tolerant and scalable computing design while avoiding traditional batch-based backpropagation. CC-ELM has been evaluated over state-of-the-art HSI classification benchmarks, yielding promising results and proving the feasibility of cloud environments for large remote sensing and HSI data volumes processing. The code available at https://github.com/mhaut/scalable-ELM-HSIPublicación Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection(IEEE, 2024) Haut, Juan M.; Moreno Álvarez, Sergio; Pastor Vargas, Rafael; Pérez García, Ámbar; Paoletti, Mercedes Eugenia; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0002-4089-9538; https://orcid.org/0000-0002-2943-6348; https://orcid.org/0000-0003-1030-3729Spectral indices are of fundamental importance in providing insights into the distinctive characteristics of oil spills, making them indispensable tools for effective action planning. The normalized difference oil index (NDOI) is a reliable metric and suitable for the detection of coastal oil spills, effectively leveraging the visible and near-infrared (VNIR) spectral bands offered by commercial sensors. The present study explores the calculation of NDOI with a primary focus on leveraging remotely sensed imagery with rich spectral data. This undertaking necessitates a robust infrastructure to handle and process large datasets, thereby demanding significant memory resources and ensuring scalability. To overcome these challenges, a novel cloud-based approach is proposed in this study to conduct the distributed implementation of the NDOI calculation. This approach offers an accessible and intuitive solution, empowering developers to harness the benefits of cloud platforms. The evaluation of the proposal is conducted by assessing its performance using the scene acquired by the airborne visible infrared imaging spectrometer (AVIRIS) sensor during the 2010 oil rig disaster in the Gulf of Mexico. The catastrophic nature of the event and the subsequent challenges underscore the importance of remote sensing (RS) in facilitating decision-making processes. In this context, cloud-based approaches have emerged as a prominent technological advancement in the RS field. The experimental results demonstrate noteworthy performance by the proposed cloud-based approach and pave the path for future research for fast decision-making applications in scalable environments.Publicación A Comprehensive Survey of Imbalance Correction Techniques for Hyperspectral Data Classification(IEEE, 2023) Paoletti, Mercedes Eugenia; Mogollón Gutiérrez, Óscar; Moreno Álvarez, Sergio; Sancho, José Carlos; Haut, Juan M.; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0003-2980-9236; https://orcid.org/0000-0002-4584-6945; https://orcid.org/0000-0001-6701-961XLand-cover classification is an important topic for remotely sensed hyperspectral (HS) data exploitation. In this regard, HS classifiers have to face important challenges, such as the high spectral redundancy, as well as noise, present in the data, and the fact that obtaining accurate labeled training data for supervised classification is expensive and time-consuming. As a result, the availability of large amounts of training samples, needed to alleviate the so-called Hughes phenomenon, is often unfeasible in practice. The class-imbalance problem, which results from the uneven distribution of labeled samples per class, is also a very challenging factor for HS classifiers. In this article, a comprehensive review of oversampling techniques is provided, which mitigate the aforementioned issues by generating new samples for the minority classes. More specifically, this article pursues a twofold objective. First, it reviews the most relevant oversampling methods that can be adopted according to the nature of HS data. Second, it provides a comprehensive experimental study and comparison, which are useful to derive practical conclusions about the performance of oversampling techniques in different HS image-based applications.Publicación Correlation-Aware Averaging for Federated Learning in Remote Sensing Data Classification(IEEE, 2024) Moreno Álvarez, Sergio; han, lirong; Paoletti, Mercedes Eugenia; Haut, Juan Mario; https://orcid.org/0000-0002-8613-7037; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0001-6701-961XThe increasing volume of remote sensing (RS) data offers substantial benefits for the extraction and interpretation of features from these scenes. Indeed, the detection of distinguishing features among captured materials and objects is crucial for classification purposes, such as in environmental monitoring applications. In these algorithms, the classes characterized by lower correlation often exhibit more distinct and discernible features, facilitating their differentiation in a straightforward manner. Nevertheless, the rise of Big Data provides a wide range of data acquired through multiple decentralized devices, where its susceptibility to be shared among various users or clients presents challenges in safeguarding privacy. Meanwhile, global features for similar classes are required to be learned for generalization purposes in the classification process. To address this, federated learning (FL) emerges as a privacy efficient decentralized solution. Firstly, in such scenarios, proprietary data is held by individual clients participating in the training of a global model. Secondly, clients may encounter challenges in identifying features that are more distinguishable within the data distributions of other clients. In this study, in order to handle these challenges, a novel methodology is proposed that considers the least correlated classes (LCCs) included in each client data distribution. This strategy exploits the distinctive features between classes, thereby enhancing performance and generalization ability in a secure and private environment.Publicación Deep Attention-Driven HSI Scene Classification Based on Inverted Dot-Product(Institute of Electrical and Electronics Engineers Inc., 2022) Paoletti, Mercedes Eugenia; Tao, Xuanwen; han, lirong; Wu, Zhaoyue; Moreno Álvarez, Sergio; Haut, Juan M.; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0003-1093-0079; https://orcid.org/0000-0002-8613-7037; https://orcid.org/0000-0002-6797-2440; https://orcid.org/0000-0001-6701-961XCapsule networks have been a breakthrough in the field of automatic image analysis, opening a new frontier in the art for image classification. Nevertheless, these models were initially designed for RGB images and naively applying these techniques to remote sensing hyperspectral images (HSI) may lead to sub-optimal behaviour, blowing up the number of parameters needed to train the model or not correctly modeling the spectral relations between the different layers of the scene. To overcome this drawback, this work implements a new capsule-based architecture with attention mechanism to improve the HSI data processing. The attention mechanism is applied during the concurrent iterative routing procedure through an inverted dot-product attentionPublicación Deep mixed precision for hyperspectral image classification(Springer, 2021-02-03) Paoletti, Mercedes Eugenia; X. Tao; Haut, Juan Mario; Moreno Álvarez, Sergio; Plaza, Antonio; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0002-9613-1659Hyperspectral images (HSIs) record scenes at different wavelength channels, providing detailed spatial and spectral information. How to storage and process this highdimensional data plays a vital role in many practical applications, where classification technologies have emerged as excellent processing tools. However, their high computational complexity and energy requirements bring some challenges. Adopting low-power consumption architectures and deep learning (DL) approaches has to provide acceptable computing capabilities without reducing accuracy demand. However, most DL architectures employ single-precision (FP32) to train models, and some big DL architectures will have a limitation on memory and computation resources. This can negatively affect the network learning process. This letter leads these challenges by using mixed precision into DL architectures for HSI classification to speed up the training process and reduce the memory consumption/access. Proposed models are evaluated on four widely used data sets. Also, low and highpower consumption devices are compared, considering NVIDIA Jetson Xavier and Titan RTX GPUs, to evaluate the proposal viability in on-board processing devices. Obtained results demonstrate the efficiency and effectiveness of these models within HSI classification task for both devices. Source codes: https ://githu b.com/mhaut / CNN-MP-HSI.Publicación Deep Robust Hashing Using Self-Distillation for Remote Sensing Image Retrieval(IEEE, 2024) han,lirong; Paoletti, Mercedes Eugenia; Moreno Álvarez, Sergio; Haut, Juan Mario; Plaza, Antonio; https://orcid.org/0000-0002-8613-7037; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0002-9613-1659This paper presents a novel self-distillation based deep robust hash for fast remote sensing (RS) image retrieval. Specifically, there are two primary processes in our proposed model: teacher learning (TL) and student learning (SL). Two transformed samples are produced from one sample image through nuanced and signalized transformations, respectively. Transformed samples are fed into both the TL and the SL flows. To reduce discrepancies in the processed samples and guarantee a consistent hash code, the parameters are shared by the two modules during the training stage. Then, a resilient module is employed to enhance the image features in order to ensure more dependable hash code production. Lastly, a three-component loss function is developed to train the entire model. Comprehensive experiments are conducted on two common RS datasets: UCMerced and AID. The experimental results validate that the proposed method has competitive performance against other RS image hashing methods.Publicación Deep shared proxy construction hashing for cross-modal remote sensing image fast target retrieval(ELSEVIER, 2024) han, lirong; Paoletti, Mercedes Eugenia; Moreno Álvarez, Sergio; Haut, Juan M.; Plaza, Antonio; https://orcid.org/0000-0002-8613-7037; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0002-9613-1659The diversity of remote sensing (RS) image modalities has expanded alongside advancements in RS technologies. A plethora of optical, multispectral, and hyperspectral RS images offer rich geographic class information. The ability to swiftly access multiple RS image modalities is crucial for fully harnessing the potential of RS imagery. In this work, an innovative method, called Deep Shared Proxy Construction Hashing (DSPCH), is introduced for cross-modal hyperspectral scene target retrieval using accessible RS images such as optical and sketch. Initially, a shared proxy hash code is generated in the hash space for each land use class. Subsequently, an end-to-end deep hash network is built to generate hash codes for hyperspectral pixels and accessible RS images. Furthermore, a proxy hash loss function is designed to optimize the proposed deep hashing network, aiming to generate hash codes that closely resemble the corresponding proxy hash code. Finally, two benchmark datasets are established for cross-modal hyperspectral and accessible RS image retrieval, allowing us to conduct extensive experiments with these datasets. Our experimental results validate that the novel DSPCH method can efficiently and effectively achieve RS image cross-modal target retrieval, opening up new avenues in the field of cross-modal RS image retrievalPublicación Distributed Deep Learning for Remote Sensing Data Interpretation(IEEE, 2021-03-15) Haut, Juan Mario; Paoletti, Mercedes Eugenia; Moreno Álvarez, Sergio; Plaza, Javier; Rico Gallego, Juan Antonio; Plaza, Antonio; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0002-2384-9141; https://orcid.org/0000-0002-4264-7473; https://orcid.org/0000-0002-9613-1659As a newly emerging technology, deep learning (DL) is a very promising field in big data applications. Remote sensing often involves huge data volumes obtained daily by numerous in-orbit satellites. This makes it a perfect target area for data-driven applications. Nowadays, technological advances in terms of software and hardware have a noticeable impact on Earth observation applications, more specifically in remote sensing techniques and procedures, allowing for the acquisition of data sets with greater quality at higher acquisition ratios. This results in the collection of huge amounts of remotely sensed data, characterized by their large spatial resolution (in terms of the number of pixels per scene), and very high spectral dimensionality, with hundreds or even thousands of spectral bands. As a result, remote sensing instruments on spaceborne and airborne platforms are now generating data cubes with extremely high dimensionality, imposing several restrictions in terms of both processing runtimes and storage capacity. In this article, we provide a comprehensive review of the state of the art in DL for remote sensing data interpretation, analyzing the strengths and weaknesses of the most widely used techniques in the literature, as well as an exhaustive description of their parallel and distributed implementations (with a particular focus on those conducted using cloud computing systems). We also provide quantitative results, offering an assessment of a DL technique in a specific case study (source code available: https://github.com/mhaut/cloud-dnn-HSI). This article concludes with some remarks and hints about future challenges in the application of DL techniques to distributed remote sensing data interpretation problems. We emphasize the role of the cloud in providing a powerful architecture that is now able to manage vast amounts of remotely sensed data due to its implementation simplicity, low cost, and high efficiency compared to other parallel and distributed architectures, such as grid computing or dedicated clusters.Publicación Enhancing Distributed Neural Network Training Through Node-Based Communications(IEEE, 2023) Moreno Álvarez, Sergio; Paoletti, Mercedes Eugenia; Cavallaro, Gabriele; Haut, Juan M.; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0002-3239-9904; https://orcid.org/0000-0001-6701-961XThe amount of data needed to effectively train modern deep neural architectures has grown significantly, leading to increased computational requirements. These intensive computations are tackled by the combination of last generation computing resources, such as accelerators, or classic processing units. Nevertheless, gradient communication remains as the major bottleneck, hindering the efficiency notwithstanding the improvements in runtimes obtained through data parallelism strategies. Data parallelism involves all processes in a global exchange of potentially high amount of data, which may impede the achievement of the desired speedup and the elimination of noticeable delays or bottlenecks. As a result, communication latency issues pose a significant challenge that profoundly impacts the performance on distributed platforms. This research presents node-based optimization steps to significantly reduce the gradient exchange between model replicas whilst ensuring model convergence. The proposal serves as a versatile communication scheme, suitable for integration into a wide range of general-purpose deep neural network (DNN) algorithms. The optimization takes into consideration the specific location of each replica within the platform. To demonstrate the effectiveness, different neural network approaches and datasets with disjoint properties are used. In addition, multiple types of applications are considered to demonstrate the robustness and versatility of our proposal. The experimental results show a global training time reduction whilst slightly improving accuracy. Code: https://github.com/mhaut/eDNNcomm.Publicación Evaluación de Rendimiento del Entrenamiento Distribuido de Redes Neuronales Profundas en Plataformas Heterogéneas(Universidad de Extremadura, 2019) Moreno Álvarez, Sergio; Paoletti, Mercedes Eugenia; Haut, Juan Mario; Rico Gallego, Juan Antonio; Plaza, Javier; Díaz Martín, Juan Carlos; Vega Rodriguez, Miguel ángel; Plaza Miguel, Antonio J.; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0002-4264-7473; https://orcid.org/0000-0002-8908-1606; https://orcid.org/0000-0002-8435-3844Asynchronous stochastic gradient descent es una tecnica de optimizacion comunmente utilizada en el entrenamiento distribuido de redes neuronales profundas. En distribuciones basadas en particionamiento de datos, se entrena una replica del modelo en cada unidad de procesamiento de la plataforma, utilizando conjuntos de muestras denominados mini-batches. Este es un proceso iterativo en el que al nal de cada mini-batch, las replicas combinan los gradientes calculados para actualizar su copia local de los parametros. Sin embargo, al utilizar asincronismo, las diferencias en el tiempo de entrenamiento por iteracion entre replicas provocan la aparicion del staleness, esto es, las replicas progresan a diferente velocidad y en el entrenamiento de cada replica se utiliza una vers on no actualizada de los parametros. Un alto gradde staleness tiene un impacto negativo en la precision del modelo resultante. Ademas, las plataformas de computacion de alto rendimiento suelen ser heterogeneas, compuestas por CPUs y GPUs de diferentes capacidades, lo que agrava el problema de staleness. En este trabajo, se propone aplicar t ecnicas de equilibrio de carga computacional, bien conocidas en el campo de la Computaci on de Altas Prestaciones, al entrenamiento distribuido de modelos profundos. A cada r eplica se asignar a un n umero de mini-batches en proporci on a su velocidad relativa. Los resultados experimentales obtenidos en una plataforma hete-rog enea muestran que, si bien la precisi on se mantiene constante, el rendimiento del entrenamiento aumenta considerablemente, o desde otro punto de vista, en el mismo tiempo de entrenamiento, se alcanza una mayor precisi on en las estimaciones del modelo. Discutimos las causas de tal incremento en el rendimiento y proponemos los pr oximos pasos para futuras investigaciones.Publicación Federated learning meets remote sensing(ELSEVIER, 2024-12-01) Moreno Álvarez, Sergio; Paoletti, Mercedes Eugenia; Sanchez Fernandez, Andres J.; Rico Gallego, Juan Antonio; han, lirong; Haut, Juan M.; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0001-6743-3570; https://orcid.org/0000-0002-4264-7473; https://orcid.org/0000-0002-8613-7037; https://orcid.org/0000-0001-6701-961XRemote sensing (RS) imagery provides invaluable insights into characterizing the Earth’s land surface within the scope of Earth observation (EO). Technological advances in capture instrumentation, coupled with the rise in the number of EO missions aimed at data acquisition, have significantly increased the volume of accessible RS data. This abundance of information has alleviated the challenge of insufficient training samples, a common issue in the application of machine learning (ML) techniques. In this context, crowd-sourced data play a crucial role in gathering diverse information from multiple sources, resulting in heterogeneous datasets that enable applications to harness a more comprehensive spatial coverage of the surface. However, the sensitive nature of RS data requires ensuring the privacy of the complete collection. Consequently, federated learning (FL) emerges as a privacy-preserving solution, allowing collaborators to combine such information from decentralized private data collections to build efficient global models. This paper explores the convergence between the FL and RS domains, specifically in developing data classifiers. To this aim, an extensive set of experiments is conducted to analyze the properties and performance of novel FL methodologies. The main emphasis is on evaluating the influence of such heterogeneous and disjoint data among collaborating clients. Moreover, scalability is evaluated for a growing number of clients, and resilience is assessed against Byzantine attacks. Finally, the work concludes with future directions and serves as the opening of a new research avenue for developing efficient RS applications under the FL paradigm. The source code is publicly available at https://github.com/hpc-unex/FLmeetsRS.Publicación Hashing for Retrieving Long-Tailed Distributed Remote Sensing Images(IEEE, 2024) han, lirong; Paoletti, Mercedes Eugenia; Moreno Álvarez, Sergio; Haut, Juan M.; Pastor Vargas, Rafael; Plaza, Antonio; https://orcid.org/0000-0002-8613-7037; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0002-4089-9538; https://orcid.org/0000-0002-9613-1659The widespread availability of remotely sensed datasets establishes a cornerstone for comprehensive image retrieval within the realm of remote sensing (RS). In response, the investigation into hashing-driven retrieval methods garners significance, enabling proficient image acquisition within such extensive data magnitudes. Nevertheless, the used datasets in practical applications are invariably less desirable and with long-tailed distribution. The primary hurdle pertains to the substantial discrepancy in class volumes. Moreover, commonly utilized RS datasets for hashing tasks encompass approximately two–three dozen classes. However, real-world datasets exhibit a randomized number of classes, introducing a challenging variability. This article proposes a new centripetal intensive attention hashing (CIAH) mechanism based on intensive attention features for long-tailed distribution RS image retrieval. Specifically, an intensive attention module (IAM) is adopted to enhance the significant features to facilitate the subsequent generation of representative hash codes. Furthermore, to deal with the inherent imbalance of long-tailed distributed datasets, the utilization of a centripetal loss function is introduced. This endeavor constitutes the inaugural effort toward long-tailed distributed RS image retrieval. In pursuit of this objective, a collection of long-tail datasets is meticulously curated using four widely recognized RS datasets, subsequently disseminated as benchmark datasets. The selected fundamental datasets contain 7, 25, 38, and 45 land-use classes to mimic different real RS datasets. Conducted experiments demonstrate that the proposed methodology attains a performance benchmark that surpasses currently existing methodologies.Publicación Heterogeneous gradient computing optimization for scalable deep neural networks(Springer, 2022) Moreno Álvarez, Sergio; Paoletti, Mercedes Eugenia; Rico Gallego, Juan Antonio; Haut, Juan M.; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0002-4264-7473; https://orcid.org/0000-0001-6701-961XNowadays, data processing applications based on neural networks cope with the growth in the amount of data to be processed and with the increase in both the depth and complexity of the neural networks architectures, and hence in the number of parameters to be learned. High-performance computing platforms are provided with fast computing resources, including multi-core processors and graphical processing units, to manage such computational burden of deep neural network applications. A common optimization technique is to distribute the workload between the processes deployed on the resources of the platform. This approach is known as data-parallelism. Each process, known as replica, trains its own copy of the model on a disjoint data partition. Nevertheless, the heterogeneity of the computational resources composing the platform requires to unevenly distribute the workload between the replicas according to its computational capabilities, to optimize the overall execution performance. Since the amount of data to be processed is different in each replica, the influence of the gradients computed by the replicas in the global parameter updating should be different. This work proposes a modification of the gradient computation method that considers the different speeds of the replicas, and hence, its amount of data assigned. The experimental results have been conducted on heterogeneous high-performance computing platforms for a wide range of models and datasets, showing an improvement in the final accuracy with respect to current techniques, with a comparable performance.Publicación Heterogeneous model parallelism for deep neural networks(ELSEVIER, 2021-06-21) Moreno Álvarez, Sergio; Haut, Juan M.; Paoletti, Mercedes Eugenia; Rico Gallego, Juan Antonio; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0002-4264-7473Deep neural networks (DNNs) have transformed computer vision, establishing themselves as the current state-of-the-art for image processing. Nevertheless, the training of current large DNN models is one of the main challenges to be solved. In this sense, data-parallelism has been the most widespread distributed training strategy since it is easy to program and can be applied to almost all cases. However, this solution suffers from several limitations, such as its high communication requirements and the memory constraints when training very large models. To overcome these limitations model-parallelism has been proposed, solving the most substantial problems of the former strategy. However, describing and implementing the parallelization of the training of a DNN model across a set of processes deployed on several devices is a challenging task. Current proposed solutions assume a homogeneous distribution, being impractical when working with devices of different computational capabilities, which is quite common on high performance computing platforms. To address previous shortcomings, this work proposes a novel model-parallelism technique considering heterogeneous platforms, where a load balancing mechanism between uneven devices of an HPC platform has been implemented. Our proposal takes advantage of the Google Brain’s Mesh-TensorFlow for convolutional networks, splitting computing tensors across filter dimension in order to balance the computational load of the available devices. Conducted experiments show an improvement in the exploitation of heterogeneous computational resources, enhancing the training performance. The code is available on: https://github.com/mhaut/HeterogeneusModelDNN.Publicación Hyperspectral Image Analysis Using Cloud-Based Support Vector Machines(Springer, 2024) Haut, Juan M.; Franco Valiente, José M.; Paoletti, Mercedes Eugenia; Moreno Álvarez, Sergio; Pardo-Diaz, Alfonso; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0002-3880-6697; https://orcid.org/0000-0003-1030-3729Hyperspectral image processing techniques involve time-consuming calculations due to the large volume and complexity of the data. Indeed, hyperspectral scenes contain a wealth of spatial and spectral information thanks to the hundreds of narrow and continuous bands collected across the electromagnetic spectrum. Predictive models, particularly supervised machine learning classifiers, take advantage of this information to predict the pixel categories of images through a training set of real observations. Most notably, the Support Vector Machine (SVM) has demonstrate impressive accuracy results for image classification. Notwithstanding the performance offered by SVMs, dealing with such a large volume of data is computationally challenging. In this paper, a scalable and high-performance cloud-based approach for distributed training of SVM is proposed. The proposal address the overwhelming amount of remote sensing (RS) data information through a parallel training allocation. The implementation is performed over a memory-efficient Apache Spark distributed environment. Experiments are performed on a benchmark of real hyperspectral scenes to show the robustness of the proposal. Obtained results demonstrate efficient classification whilst optimising data processing in terms of training times.Publicación Multiple Attention-Guided Capsule Networks for Hyperspectral Image Classification(IEEE, 2022) Paoletti, Mercedes Eugenia; Moreno Álvarez, Sergio; Haut, Juan M.; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0001-6701-961XThe profound impact of deep learning and particularly of convolutional neural networks (CNNs) in automatic image processing has been decisive for the progress and evolution of remote sensing (RS) hyperspectral imaging (HSI) processing. Indeed, CNNs have stated themselves as the current state of the art, reaching unparalleled results in HSI classification. However, most CNNs were designed for RGB images, and their direct application to HSI data analysis could lead to nonoptimal solutions. Moreover, CNNs perform classification based on the identification of specific features, neglecting the spatial relationships between different features (i.e., their arrangement) due to pooling techniques. The capsule network (CapsNet) architecture is an attempt to overcome this drawback by nesting several neural layers within a capsule, connected by dynamic routing, both to identify not only the presence of a feature but also its instantiation parameters and to learn the relationships between different features. Although this mechanism improves the data representations, enhancing the classification of HSI data, it still acts as a black box, without control of the most relevant features for classification purposes. Indeed, important features could be discriminated against. In this article, a new multiple attention-guided CapsNet is proposed to improve feature processing for RS-HSIs’ classification, both to improve computational efficiency (in terms of parameters) and increase accuracy. Hence, the most representative visual parts of the images are identified using a detailed feature extractor coupled with attention mechanisms. Extensive experimental results have been obtained on five real datasets, demonstrating the great potential of the proposed method compared to other state-of-the-art classifiers.Publicación Optimizing Distributed Deep Learning in Heterogeneous Computing Platforms for Remote Sensing Data Classification(IEEE, 2022) Moreno Álvarez, Sergio; Paoletti, Mercedes Eugenia; Rico Gallego, Juan Antonio; Cavallaro, Gabriele; Haut, Juan M.; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0002-4264-7473; https://orcid.org/0000-0002-3239-9904; https://orcid.org/0000-0001-6701-961XApplications from Remote Sensing (RS) unveiled unique challenges to Deep Learning (DL) due to the high volume and complexity of their data. On the one hand, deep neural network architectures have the capability to automatically ex-tract informative features from RS data. On the other hand, these models have massive amounts of tunable parameters, re-quiring high computational capabilities. Distributed DL with data parallelism on High-Performance Computing (HPC) sys-tems have proved necessary in dealing with the demands of DL models. Nevertheless, a single HPC system can be al-ready highly heterogeneous and include different computing resources with uneven processing power. In this context, a standard data parallelism strategy does not partition the data efficiently according to the available computing resources. This paper proposes an alternative approach to compute the gradient, which guarantees that the contribution to the gradi-ent calculation is proportional to the processing speed of each DL model's replica. The experimental results are obtained in a heterogeneous HPC system with RS data and demon-strate that the proposed approach provides a significant training speed up and gain in the global accuracy compared to one of the state-of-the-art distributed DL framework.Publicación Parameter-Free Attention Network for Spectral–Spatial Hyperspectral Image Classification(IEEE, 2023) Paoletti, Mercedes Eugenia; Tao, Xuanwen; han, lirong; Wu, Zhaoyue; Moreno Álvarez, Sergio; Kumar Roy, Swalpa; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0003-1093-0079; https://orcid.org/0000-0002-8613-7037; https://orcid.org/0000-0002-6797-2440; https://orcid.org/0000-0002-6580-3977Hyperspectral images (HSIs) comprise plenty of information in the spatial and spectral domain, which is highly beneficial for performing classification tasks in a very accurate way. Recently, attention mechanisms have been widely used in the HSI classification due to their ability to extract relevant spatial and spectral features. Notwithstanding their positive results, most of the attentional strategies usually introduce a significant number of parameters to be trained, making the models more complex and increasing the computational load. In this article, we develop a new parameter-free attention network for HSI classification. The main advantage of our model is that it does not add parameters to the original network (as opposed to other state-of-the-art approaches) while providing higher classification accuracies. Extensive experimental validations and quantitative comparisons are conducted—using different benchmark HSIs—to illustrate these advantages. The code is available on https://github.com/mhaut/Free2Resnet