Publications | Prof. Gabriele Cavallaro

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2023

A Single-Step Multiclass SVM based on Quantum Annealing for Remote Sensing Data Classification [preprint]

Amer Delilbasic, Bertrand Le Saux, Morris Riedel, Kristel Michielsen, Gabriele Cavallaro

arXiv

In recent years, the development of quantum annealers has enabled experimental demonstrations and has increased research interest in applications of quantum annealing, such as in quantum machine learning and in particular for the popular quantum SVM. Several versions of the quantum SVM have been proposed, and quantum annealing has been shown to be effective in them. Extensions to multiclass problems have also been made, which consist of an ensemble of multiple binary classifiers. This work proposes a novel quantum SVM formulation for direct multiclass classification based on quantum annealing, called Quantum Multiclass SVM (QMSVM). The multiclass classification problem is formulated as a single Quadratic Unconstrained Binary Optimization (QUBO) problem solved with quantum annealing. The main objective of this work is to evaluate the feasibility, accuracy, and time performance of this approach. Experiments have been performed on the D-Wave Advantage quantum annealer for a classification problem on remote sensing data. The results indicate that, despite the memory demands of the quantum annealer, QMSVM can achieve accuracy that is comparable to standard SVM methods and, more importantly, it scales much more efficiently with the number of training examples, resulting in nearly constant time. This work shows an approach for bringing together classical and quantum computation, solving practical problems in remote sensing with current hardware.

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Recent Publications

Journals

A Single-Step Multiclass SVM based on Quantum Annealing for Remote Sensing Data Classification [preprint]

Few-Shot Remote Sensing Image Classification with Meta-Learning [preprint]

Kernel Approximation on a Quantum Annealer for Remote Sensing Regression Tasks [preprint]

Predicting Classification Performance for Benchmark Hyperspectral Datasets

Remote Sensing Image Classification Using CNNs With Balanced Gradient for Distributed Heterogeneous Computing

Quantum SVR for Chlorophyll Concentration Estimation in Water with Remote Sensing

Learning from Data for Remote Sensing Image Analysis

A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 images

Exploration of Machine Learning Methods for the Classification of Infrared Limb Spectra of Polar Stratospheric Clouds

Cloud Deep Networks for Hyperspectral Image Analysis

Remote Sensing Big Data Classification with High Performance Distributed Deep Learning

Parallel Computation of Component Trees on Distributed Memory Machines

Automatic Attribute Profiles

Integration of LiDAR and Hyperspectral Data for Land-cover Classification: A Case Study

Remote Sensing Image Classification Using Attribute Filters Defined over the Tree of Shapes

Extended Self-Dual Attribute Profiles for the Classification of Hyperspectral Images

On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods

Automatic Framework for Spectral–Spatial Classification Based on Supervised Feature Extraction and Morphological Attribute Profiles

Conference Papers

Practice and Experience using High Performance Computing and Quantum Computing to Speed-up Data Science Methods in Scientific Applications

Accelerating Hyperparameter Tuning of a Deep Learning Model for Remote Sensing Image Classification

An Automatic Approach for the production of a Time Series of Consistent Land-cover Maps Based on Long-short Term Memory

Optimizing Distributed Deep Learning in Heterogeneous Computing Platforms for Remote Sensing Data Classification

Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing

Improving Generalization for Few-Shot Remote Sensing Classification with Meta-learning

Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification

Practice and Experience in using Parallel and Scalable Machine Learning in Remote Sensing from HPC over Clouds to Quantum Computing

Enhancing Large Batch Size Training of Deep Models for Remote Sensing Applications

Practice and Experience in using Parallel and Scalable Machine Learning with Heterogenous Modular Supercomputing Architectures

Design and Evaluation of an HPC-based Expert System to speed-up Retail Data Analysis using Residual Networks Combined with Parallel Association Rule Mining and Scalable Recommenders

JUWELS Booster–A Supercomputer for Large-Scale AI Research

Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-WAVE Quantum Annealer

Super-Resolution of Large Volumes of Sentinel-2 Images with High Performance Distributed Deep Learning

Scaling up a Multispectral Resnet-50 to 128 GPUs

Multi-Scale Convolutional SVM Networks for Multi-Class Classification Problems of Remote Sensing Images

Scalable workflows for Remote Sensing Data Processing with the DEEP-EST Modular Supercomputing Architecture

Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems

Automated Analysis of Remotely Sensed Images Using the UNICORE Workflow Management System

The Influence of Sampling Methods on Pixel-Wise Hyperspectral Image Classification with 3D Convolutional Neural Networks

Scaling Support Vector Machines Towards Exascale Computing for Classification of Large-Scale High-Resolution Remote Sensing Images

Facilitating Efficient Data Analysis of Remotely Sensed Images Using Standards-Based Parameter Sweep Models

Tree-Based Supervised Feature Extraction Method Based on Self-Dual Attribute Profiles

Unsupervised Change Detection Analysis to Multi-Channel Scenario based on Morphological Contextual Analysis

Region-Based Classification of Remote Sensing Images with the Morphological Tree of Shapes

On Scalable Data Mining Techniques for Earth Science

An Advanced Classifier for the Joint Use of LiDAR and Hyperspectral Data: Case Study in Queensland, Australia

Processing High Resolution Images of Urban Areas with Self-Dual Attribute Filters (Invited Paper)

Automatic Threshold Selection for Profiles of Attribute Filters Based on Granulometric Characteristic Functions

Automatic Morphological Attribute Profiles

Scalable Developments for Big Data Analytics in Remote Sensing

A Comparison of Self-Dual Attribute Profiles Based on Different Filter Rules for Classification

Smart Data Analytics Methods for Remote Sensing Applications

Detection of Hedges Based on Attribute Filters

Magazine articles

High-Performance and Disruptive Computing in Remote Sensing: HDCRS-A New Working Group of the GRSS Earth Science Informatics Technical Committee

Book chapters

Remote Sensing Data Fusion: Markov Models and Mathematical Morphology for Multisensor, Multiresolution, and Multiscale Image Classification

Analyzing Remote Sensing Images with Hierarchical Morphological Representations