Antonio Plaza

Antonio Plaza
Universidad de Extremadura | UNEX · Department of Computers and Communications Technology

About

744
Publications
148,204
Reads
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35,559
Citations
Citations since 2017
260 Research Items
24839 Citations
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201720182019202020212022202301,0002,0003,0004,0005,000
Introduction
My main research interests comprise remotely sensed hyperspectral image analysis, signal processing, and efficient implementations of large-scale scientific problems on high performance computing architectures, including commodity Beowulf clusters, heterogeneous networks of computers and clouds, and specialized computer architectures such as field-programmable gate arrays (FPGAs) or graphical processing units (GPUs).

Publications

Publications (744)
Article
With the development of remote sensing technology, significant progress has been made in the evaluation of the eco-environment. The remote sensing ecological index (RSEI) is one of the most widely used indices for the comprehensive evaluation of eco-environmental quality. This index is entirely based on remote sensing data and can monitor eco-envir...
Article
Subspace learning has been widely applied for feature extraction of hyperspectral images (HSIs) and achieved great success. However, the current methods still leave two problems that need to be further investigated. Firstly, those methods mainly focus on finding one or multiple projection matrices for mapping the high-dimensional data into a low-di...
Article
Full-text available
Deep learning models such as Convolutional Neural Networks (CNNs) have made significant progress in hyperspectral image (HSI) classification. However, these models require a large number of parameters, which occupy a lot of storage space and suffer from overfitting, thus resulting in performance loss. To solve the above problems, in this paper we p...
Article
Most existing techniques consider hyperspectral anomaly detection (HAD) as background modeling and anomaly search problems in the spatial domain. In this article, we model the background in the frequency domain and treat anomaly detection as a frequency-domain analysis problem. We illustrate that spikes in the amplitude spectrum correspond to the b...
Article
With the recent development of remote sensing technology, large image repositories have been collected. In order to retrieve the desired images of massive remote sensing data sets effectively and efficiently, we propose a novel central cohesion gradual hashing (CCGH) mechanism for remote sensing image retrieval. Firstly, we design a deep hashing mo...
Preprint
Full-text available
Spatial–spectral classification (SSC) has become a trend for hyperspectral image (HSI) classification. However, most SSC methods mainly consider local information, so that some correlations may not be effectively discovered when they appear in regions that are not contiguous. Although many SSC methods can acquire spatial-contextual characteristics...
Article
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Machine learning (ML) is influencing the literature in several research fields, often through state-of-theart approaches. In the past several years, ML has been explored for pansharpening, i.e., an image fusion technique based on the combination of a multispectral (MS) image, which is characterized by its medium/low spatial resolution, and higher-s...
Article
Full-text available
While deep learning algorithms have achieved good results in hyperspectral image (HSI) classification, several supervised classification algorithms rely on a large number of labeled samples to get adequate performance. Collecting a large number of labeled samples is expensive in many real applications. To address this issue, a novel semisupervised...
Article
In recent years, supervised learning has been widely used in various tasks of optical remote sensing image (RSI) understanding, including RSI classification, pixel-wise segmentation, change detection, and object detection. The methods based on supervised learning need a large amount of high-quality training data, and their performance highly depend...
Article
Convolutional Neural Networks (CNNs) have been extensively utilized for Hyperspectral (HSI) as well as Light Detection and Ranging (LiDAR) data Classification. However, CNNs have not been much explored for joint HSI and LiDAR image classification. Therefore, this article proposes a joint feature learning (HSI and LiDAR) and fusion mechanism using C...
Preprint
Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for...
Preprint
In recent years, supervised learning has been widely used in various tasks of optical remote sensing image understanding, including remote sensing image classification, pixel-wise segmentation, change detection, and object detection. The methods based on supervised learning need a large amount of high-quality training data and their performance hig...
Article
Full-text available
The classification accuracy of ground objects is improved due to the combined use of the same scene data collected by different sensors. We propose to fuse the spatial planar distribution and spectral information of the hyperspectral images (HSIs) with the spatial 3D information of the objects captured by light detection and ranging (LiDAR). In thi...
Preprint
The problem of effectively exploiting the information multiple data sources has become a relevant but challenging research topic in remote sensing. In this paper, we propose a new approach to exploit the complementarity of two data sources: hyperspectral images (HSIs) and light detection and ranging (LiDAR) data. Specifically, we develop a new dual...
Article
Landslides are highly hazardous geological disasters that can potentially threaten the safety of human life and property. As a result, landslide susceptibility mapping (LSM) plays an important role in the landslide prevention system. Recently, many deep learning (DL) models have been adopted for LSM, but they also face problems such as sensitivity...
Preprint
Full-text available
Vision transformer (ViT) has been trending in image classification tasks due to its promising performance when compared to convolutional neural networks (CNNs). As a result, many researchers have tried to incorporate ViT models in hyperspectral image (HSI) classification tasks, but without achieving satisfactory performance. To this paper, we intro...
Article
The existence of clouds affects the quality of optical remote sensing images. Cloud removal is an important preprocessing procedure to effectively improve the utilization of optical remote sensing images. Thin clouds partly obscure the land surfaces beneath them, making it possible to correct the cloudy scenes according to the available information...
Article
The simple linear iterative clustering (SLIC) has been shown as an efficient and widely used superpixel-based algorithm for segmenting marine synthetic aperture radar (SAR) images. However, SLIC does not consider the fact that the density of ship target pixels is significantly lower than that of sea clutter pixels, leading to a waste of computation...
Article
Convolutional neural networks (CNNs) are relevant tools for remote sensing data processing in the last few years. Kernels process and integrate the spatial information of remotely sensed hyperspectral images (HSIs) accurately enough, so as to reduce the noise and spectral variations present in the data. Despite the great efficiency of the CNNs with...
Article
Spectral unmixing plays a vital role in hyperspectral image analysis. It mainly consists of two procedures, i.e., endmember extraction and abundance estimation. Although most algorithms for each of the two procedures may exhibit good performance, few studies have been done considering both problems simultaneously. Therefore, hyperspectral unmixing...
Article
Deep metric learning methods have recently drawn significant attention in the field of remote sensing (RS), owing to their prominent capabilities for modeling relations among RS images based on their semantic contents. In the context of scene classification and large-scale image retrieval, one of the most prominent deep metric learning methods is t...
Article
Full-text available
Remote sensing image change detection (RSICD) is a technique that explores the change of surface coverage in a certain time series by studying the difference between multiple remote sensing images (RSIs) collected over the same area. Traditional RSICD algorithms exhibit poor performance on complex change detection (CD) tasks. In recent years, deep...
Article
Synthetic aperture radar (SAR) images provide all-weather and all-time capabilities for Earth observation, which becomes highly beneficial in the field of intelligent remote sensing (RS) image interpretation. Due to these advantages, SAR images have been widely exploited in automatic building segmentation tasks under poor weather conditions, especi...
Article
Full-text available
Obtaining information on the surface coverage of open-pit mining areas (OPMAs) is of great significance to ecological governance and restoration. The current methods to map the OPMAs face problems such as low mapping accuracy due to complex landscapes. In this article, we propose a hybrid open-pit mining mapping (OPMM) framework with Gaofen-2 (GF-2...
Article
Full-text available
Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for...
Article
Sparse unmixing (SU) has been widely applied to remotely sensed hyperspectral images (HSIs) interpretation. Compared with traditional unmixing algorithms, SU does not need to extract pure signatures (endmembers) from the image. The endmember matrix is constructed by directly selecting spectra from a known library, which is used to estimate the frac...
Article
Although hyperspectral image (HSI) classification methods have gained popularity in the remote sensing community, their performance tends to be limited by the quantity and quality of training samples. Actually, the production (i.e., capture and annotation) of training samples requires abundant labor costs and time consumption. Multiattribute sample...
Article
Full-text available
Hyperspectral image (HSI) is applied to accurately distinguish ground objects in many fields, owing to its abundant structural and spectral information. To fully mine the spectral-spatial information, feature extraction methods are employed to further improve the classification performance in numerous research works. Among them, integrating context...
Article
Standard hyperspectral (HS) pansharpening relies on fusion to enhance low-resolution HS (LRHS) images to the resolution of their matching panchromatic (PAN) images, whose practical implementation is normally under a stipulation of scale invariance of the model across the training phase and the pansharpening phase. By contrast, arbitrary resolution...
Article
Anomaly detection has become an important remote sensing application due to the abundant spectral and spatial information contained in hyperspectral images. Recently, hyperspectral anomaly detection methods based on the collaborative representation (CR) model have attracted significant attention. Nevertheless, these methods have to face two main ch...
Article
Full-text available
Hyperspectral unmixing extracts pure spectral constituents (endmembers) and their corresponding abundance fractions from remotely sensed scenes. Most traditional hyperspectral unmixing methods require the results of other endmember extraction algorithms to complete the abundance estimation step. Due to the impressive learning and data fitting capab...
Article
Full-text available
Medium spatial resolution surface reflectance image series from the combination of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Imager (MSI) observations have great importance to the land surface monitoring tasks, for which great efforts have been paid for blending the two data. However, most of the efforts focus on the imag...
Article
Object detection in remote sensing images (RSIs) poses great difficulties due to arbitrary orientations, various scales and dense location of the targets over the ground. Recent evidence suggests that encoding the orientation information is of great use for training an accurate object detector for oriented object detection (OOD). In this paper, we...
Article
Hyperspectral images (HSIs) contain abundant information in the spatial and spectral domains, allowing for a precise characterization of categories of materials. Convolutional neural networks (CNNs) have achieved great success in HSI classification, owing to their excellent ability in local contextual modeling. However, CNNs suffer from fixed filte...
Article
Convolutional long short-term memory (ConvLSTM) has received much attention for hyperspectral image (HSI) classification due to its ability of modeling long-range correlations, which, however, is vulnerable to too many parameters and insufficient training, limiting its classification accuracy, especially for small samples. Different from it, tradit...
Article
Most state-of-the-art deep learning-based methods for extraction of building footprints are aimed at designing proper convolutional neural network (CNN) architectures or loss functions able to effectively predict building masks from remote sensing (RS) images. To properly train such CNN models, large-scale and pixel-level building annotations are r...
Article
Full-text available
The spread of aquatic invasive plants is a major concern in several zones of the world's geography. These plants, which are not part of the natural ecosystem, cause a negative impact to the environment, as well as to economy and society. In Spain, large areas of Guadiana (the second longest river in Spain) have been invaded by such plants. Among th...
Article
Quantum machine learning has attracted significant attention in recent years due to its capacity to reflect a particle’s aggregation in the quantum domain. In this work, quantum machine learning is used to perform anomaly detection in hyperspectral images (HSIs) by considering pixels as particles in the quantum domain, and using the quantum potenti...
Article
Anomaly detection is an important technique for hyperspectral image processing. It aims to find pixels that are markedly different from the background when the target spectrum is unavailable. Many anomaly detection methods have been proposed over the past years, among which graph-based ones have attracted extensive attention. And they usually just...
Article
Convolutional neural networks (CNNs) are widely used in hyperspectral image (HSI) classification. However, the network architecture of CNNs is often designed manually, which requires careful fine-tuning. Recently, many techniques for neural architecture search (NAS) have been proposed to design the network automatically but most of the methods are...
Article
Full-text available
Monitoring the spatio-temporal distribution of invasive aquatic plants is a challenge in many regions worldwide. One of the most invasive species on earth is the water hyacinth. These plants are harmful to biodiversity and create negative impacts on society and economy. The Guadiana river (one of the most important ones in Spain) has suffered from...
Article
Deep learning methods have demonstrated excellent performance in hyperspectral image (HSI) classification. However, these methods mainly focus on improving the classification accuracy while ignoring their high complexity. By considering that the data formats of both HSIs and network weights can be represented in the form of tensors, we develop a ne...
Article
Nowadays, a variety of different sources can be combined together to measure and monitor maritime human activities. Reliable data fusion techniques are essential to associate the targets from different systems for maritime surveillance. In particular, the fusion of data from Sentinel-2 satellites and the Automatic Identification System (AIS) has at...
Article
Unsupervised hashing for remote sensing (RS) image retrieval first extracts image features and then uses these features to construct supervised information (e.g., pseudolabels) to train hashing networks. Existing methods usually regard RS images as natural images to extract unisource features. However, these features only contain partial informatio...
Article
Full-text available
The fusion of spectral-spatial features based on deep learning (DL) has become the focus of research in hyperspectral image (HSI) classification. However, previous deep frameworks based on spectral-spatial fusion usually performed feature aggregation only at the branch ends. Furthermore, only first-order statistical features are considered in the f...
Article
Traditional hyperspectral (HS) pansharpening aims at fusing a hyperspectral image with its panchromatic (PAN) counterpart, to bring the spatial resolution of the HS image to that of the PAN image. However, in many practical applications, arbitrary resolution HS (ARHS) pansharpening is required, where the HS and PAN images need to be integrated to g...
Article
Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. Due to their excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image class...
Article
Full-text available
Hyperspectral unmixing refers to the process of obtaining endmembers and abundance vectors through linear or nonlinear models. The traditional linear unmixing model assumes that each mixed pixel can be represented by a linear combination of endmembers. Considering real-world situations, a sparse constraint is normally added to the linear unmixing m...
Article
Full-text available
The integration of spectral and spatial information is crucial in remotely sensed hyperspectral image classification. Some available approaches extract spatial features before classification, while other techniques include spatial information as a spatial regularizer. Due to the model complexity, these methods generally assume that a given pixel is...
Article
The hyperspectral images are composed of a variety of textures across the different bands which increase the spectral similarity and make it difficult to predict the pixel-wise labels without inducing additional complexity at the feature level. To extract robust and discriminative features from the different regions of land cover, the hyperspectral...
Article
In hyperspectral anomaly detection, anomalies are rare targets that exhibit distinct spectral signatures from the background. Thus, anomalies are with low probabilities of occurrence in hyperspectral images. In this article, we develop a new technique for hyperspectral anomaly detection that adopts a new information theory perspective, to fully uti...
Article
Full-text available
Building footprint segmentation from highresolution remote sensing (RS) images plays a vital role in urban planning, disaster response and population density estimation. Convolutional neural networks (CNNs) have been recently used as a workhorse for effectively generating building footprints. However, to completely exploit the prediction power of C...
Article
Full-text available
Landslide detection mapping (LDM) is the basis of the field of landslide disaster prevention; however, it has faced certain difficulties. The Three Gorges Reservoir area of the Yangtze River has been one of the most intensively evaluated areas for landslide prevention in the world, due to the high frequency of landslide disasters here. In this stud...
Article
Hyperspectral image (HSI) classification is essential in remote sensing image analysis. The classification methods based on deep learning have attracted more and more attention. However, classification accuracy is seriously affected by the quantity of labeled data and redundant information. Therefore, a deep semisupervised shared subspace learning...
Article
Advanced remote-sensing instruments produce massively large scenes from the surface of the earth, with very high spatial resolution and dimensionality. Developing methods for efficiently localizing specific objects in a large-scale scene presents a significant challenge, mainly because of the high computational requirements involved. To tackle this...
Article
Full-text available
Recently, the inclusion of spatial information has drawn increasing attention in hyperspectral image (HSI) applications due to its effectiveness in terms of improving classification accuracy. However, most of the techniques that include such spatial knowledge in the analysis are based on spatial-spectral weak assumptions. This paper proposes a nove...
Article
Hyperspectral unmixing is a critical step to process hyperspectral images (HSIs). Nonnegative matrix factorization (NMF) has drawn extensive attention in remotely sensed hyperspectral unmixing since it does not require prior knowledge about the pure spectral constituents (endmembers) in the scene. However, this approach is normally implemented as a...
Article
Land-cover information is of paramount importance in a wide range of environmental and socioeconomic applications. Deep learning (DL) provides a large variety of potential models for extracting useful information from raw images. However, remote sensing image (RSI) classification remains a challenging goal due to the intrinsic features of the data,...
Article
Spatiotemporal fusion (STF) aims at generating remote-sensing data with both high spatial and temporal resolution. In the literature, one of the most widely used strategies to accomplish this goal is to fuse high temporal resolution images collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) with images with finer spatial resoluti...
Presentation
Full-text available
Remote sensing and Earth observation (EO) are a priority for all space agencies. Several satellite missions are dedicated to this task, and more are planned to deployment in the near future. As it becomes grater the need to acquire data with more spatial resolution, less revisiting time and more spectral resolution, multi and hyper-spectral imaging...
Article
Endmember estimation consists of two tasks, that is, determining the number of pure spectral constituents (endmembers) and extracting their spectral signatures. We present a new geometric distance-based method for endmember estimation from hyperspectral images (HSIs), which does not need to know the number of endmembers in advance. Our strategy opt...
Article
Full-text available
Hyperspectral images (HSIs) record scenes at different wavelength channels, providing detailed spatial and spectral information. How to storage and process this high-dimensional data plays a vital role in many practical applications, where classification technologies have emerged as excellent processing tools. However, their high computational comp...
Article
For deep networks, accurate image similarities cannot be well characterized with limited iterations, so the latent relationships between images can be embedded to enhance image retrieval performance. In this article, we propose a method named DFLLR to learn deep image features and accurate image similarities for remote sensing image retrieval (RSIR...
Conference Paper
Hyperspectral imaging (HSI) systems collect electromagnetic radiation, emitted or reflected by the scene under observation at, typically, hundreds of contiguous and regularly spaced narrow spectral bands, from the visible to the infra-red region of the spectrum. The result is a 3D data cube with two spatial dimensions and a wavelength dimension, co...
Preprint
Full-text available
Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. Owing to their excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image cla...
Article
Endowing convolutional neural networks (CNNs) with the rotation-invariant capability is important for characterizing the semantic contents of remote sensing (RS) images since they do not have typical orientations. Most of the existing deep methods for learning rotation-invariant CNN models are based on the design of proper convolutional or pooling...
Presentation
Full-text available
Machine Learning (ML) for Hyperspectral Imaging (HSI) computing on Single Board Computers (SBC). Result from the experiments with three different ML algorithms (SVM, MLR & RF), using two public HSI datasets (Indian Pines & Pavia University), with four hardware platforms, show that SBC are good candidates for the SAIoT (Satellite Artificial Intellig...