Antonio Plaza

Full Professor

Universidad de Extremadura

Spain

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Antonio Plaza is a Full Professor at the University of Extremadura, Spain. His research primarily focuses on hyperspectral image classification, remote sensing image analysis, and anomaly detection in hyperspectral images. Recent contributions include advancements in semisupervised graph U-Net methodologies and innovative approaches to spectral super-resolution using deep learning techniques.

Antonio Plaza

Research Interests

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Articles (29)

Semisupervised graph U-Net with G-ConvLSTM for hyperspectral image classification

PFS3F: Probabilistic Fusion of Superpixel-Wise and Semantic-Aware Structural Features for Hyperspectral Image Classification

Fully Tensorized Lightweight ConvLSTM Neural Networks for Hyperspectral Image Classification

HSACT: A hierarchical semantic-aware CNN-Transformer for remote sensing image spectral super-resolution

Learning Disentangled Priors for Hyperspectral Anomaly Detection: A Coupling Model-Driven and Data-Driven Paradigm

A Comparative Study of Model Interpretability Considering the Decision Differentiation of Landslide Susceptibility Models

A New Multiscale Superpixel Mamba for Hyperspectral Image Classification

A Selective Semantic Transformer for Spectral Super-Resolution of Multispectral Imagery

A Spectral-Spatial Attention Network for Hyperspectral Unmixing

44th IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2024)—Athens, Greece, 7–12 July 2024: Impressions of the first days [Conference Reports]

GRSS Awards Presented at the IGARSS 2024 Banquet [Conference Reports]

2023 GRSS Awards Presented at the IGARSS Night 2023 “Space and Magic” [Conference Reports]

Advancements in remote sensing for invasive plant mapping along the Guadiana River: The role of CNN2D

End-to-End Convolutional Network and Spectral-Spatial Transformer Architecture for Hyperspectral Image Classification

A Mask Guided Oriented Object Detector Based on Rotated Size-Adaptive Tricube Kernel

An Abundance-Guided Attention Network for Hyperspectral Unmixing

Anomaly Detection in Hyperspectral Images Using Adaptive Graph Frequency Location

BisDeNet: A New Lightweight Deep Learning-Based Framework for Efficient Landslide Detection

Cloud-Edge Selective Background Energy Constrained Filter for Real-Time Hyperspectral Target Detection

NSCKL: Normalized Spectral Clustering With Kernel-Based Learning for Semisupervised Hyperspectral Image Classification

CE-RX: A Collaborative Cloud-Edge Anomaly Detection Approach for Hyperspectral Images

Spectral‐spatial sequence characteristics‐based convolutional transformer for hyperspectral change detection

Multi-Scale Spectral-Spatial Attention Network for Hyperspectral Image Classification Combining 2D Octave and 3D Convolutional Neural Networks

A Cluster-Analysis and Convex Hull-Based Fast Large-Scale Phase Unwrapping Method for Single- and Multibaseline SAR Interferograms

A New Context-Aware Framework for Defending Against Adversarial Attacks in Hyperspectral Image Classification

AAtt-CNN: Automatic Attention-Based Convolutional Neural Networks for Hyperspectral Image Classification

ACGT-Net: Adaptive Cuckoo Refinement-Based Graph Transfer Network for Hyperspectral Image Classification

BS3LNet: A New Blind-Spot Self-Supervised Learning Network for Hyperspectral Anomaly Detection

Central Cohesion Gradual Hashing for Remote Sensing Image Retrieval

Collaborators (3)

Alberto Moreira

Juan M. Haut

Mercedes E. Paoletti

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