Persona: Moreno Álvarez, Sergio
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Moreno Álvarez
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Publicación 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 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 Training deep neural networks: a static load balancing approach(Springer, 2020-03-02) Moreno Álvarez, Sergio; Haut, Juan Mario; Paoletti, Mercedes Eugenia; Rico Gallego, Juan Antonio; Díaz Martín, Juan Carlos; Plaza, Javier; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0002-4264-7473; https://orcid.org/0000-0002-8435-3844; https://orcid.org/0000-0002-8908-1606Deep neural networks are currently trained under data-parallel setups on high-performance computing (HPC) platforms, so that a replica of the full model is charged to each computational resource using non-overlapped subsets known as batches. Replicas combine the computed gradients to update their local copies at the end of each batch. However, differences in performance of resources assigned to replicas in current heterogeneous platforms induce waiting times when synchronously combining gradients, leading to an overall performance degradation. Albeit asynchronous communication of gradients has been proposed as an alternative, it suffers from the so-called staleness problem. This is due to the fact that the training in each replica is computed using a stale version of the parameters, which negatively impacts the accuracy of the resulting model. In this work, we study the application of well-known HPC static load balancing techniques to the distributed training of deep models. Our approach is assigning a different batch size to each replica, proportional to its relative computing capacity, hence minimizing the staleness problem. Our experimental results (obtained in the context of a remotely sensed hyperspectral image processing application) show that, while the classification accuracy is kept constant, the training time substantially decreases with respect to unbalanced training. This is illustrated using heterogeneous computing platforms, made up of CPUs and GPUs with different performance.Publicación A tool to assess the communication cost of parallel kernels on heterogeneous platforms(Springer, 2020) Rico Gallego, Juan Antonio; Moreno Álvarez, Sergio; Díaz Martín, Juan Carlos; Lastovetsky, Alexey L.; https://orcid.org/0000-0002-4264-7473; https://orcid.org/0000-0002-8435-3844; https://orcid.org/0000-0001-9460-3897Ensuring applications to achieve an efficient usage of resources and fast execution time in the complex current heterogeneous high-performance computing platforms is a paramount problem. Essential efforts to reach the goal are the optimal partitioning of the data space between the processes composing a typical task/data-parallel application, and their right mapping and deployment on the platform. The computational and communication performance modeling describing the platform and the application behaviors is an increasingly recognized approach. This paper discusses the utility of the τ–Lop analytic communication performance model in facing these issues and contributes with a practical symbolic computation tool that represents, manipulates and accurately evaluates the formal communication cost expression derived from a hybrid kernel. We identify a set of scenarios where the tool could be applied, provide with both basic and advanced use examples and evaluate the tool on real-life kernels.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 Self-Supervised Learning on Small In-Domain Datasets Can Overcome Supervised Learning in Remote Sensing(IEEE, 2024) Sanchez-Fernandez, Andres J.; Moreno Álvarez, Sergio; Rico Gallego, Juan Antonio; Tabik, Siham; https://orcid.org/0000-0001-6743-3570; https://orcid.org/0000-0002-4264-7473; https://orcid.org/0000-0003-4093-5356The availability of high-resolution satellite images has accelerated the creation of new datasets designed to tackle broader remote sensing (RS) problems. Although popular tasks, such as scene classification, have received significant attention, the recent release of the Land-1.0 RS dataset marks the initiation of endeavors to estimate land-use and land-cover (LULC) fraction values per RGB satellite image. This challenging problem involves estimating LULC composition, i.e., the proportion of different LULC classes from satellite imagery, with major applications in environmental monitoring, agricultural/urban planning, and climate change studies. Currently, supervised deep learning models—the state-of-the-art in image classification—require large volumes of labeled training data to provide good generalization. To face the challenges posed by the scarcity of labeled RS data, self-supervised learning (SSL) models have recently emerged, learning directly from unlabeled data by leveraging the underlying structure. This is the first article to investigate the performance of SSL in LULC fraction estimation on RGB satellite patches using in-domain knowledge. We also performed a complementary analysis on LULC scene classification. Specifically, we pretrained Barlow Twins, MoCov2, SimCLR, and SimSiam SSL models with ResNet-18 using the Sentinel2GlobalLULC small RS dataset and then performed transfer learning to downstream tasks on Land-1.0. Our experiments demonstrate that SSL achieves competitive or slightly better results when trained on a smaller high-quality in-domain dataset of 194 877 samples compared to the supervised model trained on ImageNet-1k with 1 281 167 samples. This outcome highlights the effectiveness of SSL using in-distribution datasets, demonstrating efficient learning with fewer but more relevant data.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 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 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 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.
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