PUBLICATIONS

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

2020

G. Cavallaro, D. Willsch, M. Willsch, K. Michielsen and M. Riedel

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

Support Vector Machine (SVM) is a popular supervised Machine Learning (ML) method that is widely used for classification and regression problems. Recently, a method to train SVMs on a D-Wave 2000Q Quantum Annealer (QA) was proposed for binary classification of some biological data. First, ensembles of weak quantum SVMs are generated by training each classifier on a disjoint training subset that can be fit into the QA. Then, the computed weak solutions are fused for making predictions on unseen data. In this work, the classification of Remote Sensing (RS) multispectral images with SVMs trained on a QA is discussed. Furthermore, an open code repository is released to facilitate an early entry into the practical application of QA, a new disruptive compute technology.

Scaling up a Multispectral Resnet-50 to 128 GPUs

2020

R. Sedona, G. Cavallaro, J. Jitsev, A. Strube, M. Riedel and M. Book

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

Similarly to other scientific domains, Deep Learning (DL) holds great promises to fulfil the challenging needs of Remote Sensing (RS) applications. However, the increase in volume, variety and complexity of acquisitions that are carried out on a daily basis by Earth Observation (EO) missions generates new processing and storage challenges within operational processing pipelines. The aim of this work is to show that High-Performance Computing (HPC) systems can speed up the training time of Convolutional Neural Networks (CNNs). Particular attention is put on the monitoring of the classification accuracy that usually degrades when using large batch sizes. The experimental results of this work show that the training of the model scales up to a batch size of 8,000, obtaining classification performances in terms of accuracy in line with those using smaller batch sizes.

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

2020

R. Zhang, G. Cavallaro and J. Jitsev

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

This work proposes a novel distributed deep learning model for RS images super-resolution. HPC systems with GPUs are used to accelerate the learning of the unknown low to high resolution mapping from large volumes of Sentinel-2 data. The proposed deep learning model is based on self-attention mechanism and residual learning. The results demonstrate that state-of-the-art performance can be achieved by keeping the size of the model relatively small. Synchronous data parallelism is applied to scale up the training process without severe performance loss. Distributed training is thus shown to speed up learning substantially while keeping performance intact.

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

2019

E. Erlingsson, G. Cavallaro, H. Neukirchen and M. Riedel

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

The implementation of efficient remote sensing workflows is essential to improve the access to and analysis of the vast amount of sensed data and to provide decision-makers with clear, timely, and useful information. The Dynamical Exascale Entry Platform (DEEP) is an European pre-exascale platform that incorporates heterogeneous High-Performance Computing (HPC) systems, i.e., hardware modules which include specialised accelerators. This paper demonstrates the potential of such diverse modules for the deployment of remote sensing data workflows that include diverse processing tasks. Particular focus is put on pipelines which can use the Network Attached Memory (NAM), which is a novel supercomputer module that allows near processing and/or fast shared storage of big remote sensing datasets.

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

2019

G. Cavallaro, Y. Bazi, F. Melgani and M. Riedel

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

The classification of land-cover classes in remote sensing images can suit a variety of interdisciplinary applications such as the interpretation of natural and man-made processes on the Earth surface. The Convolutional Support Vector Machine (CSVM) network was recently proposed as binary classifier for the detection of objects in Unmanned Aerial Vehicle (UAV) images. The training phase of the CSVM is based on convolutional layers that learn the kernel weights via a set of linear Support Vector Machines (SVMs). This paper proposes the Multi-scale Convolutional Support Vector Machine (MCSVM) network, that is an ensemble of CSVM classifiers which process patches of different spatial sizes and can deal with multi-class classification problems. The experiments are carried out on the EuroSAT Sentinel-2 dataset and the results are compared to the one obtained with recent transfer learning approaches based on pre-trained Convolutional Neural Networks (CNNs).

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

2019

G. Cavallaro, V. Kozlov, M. Götz and M. Riedel

Proceedings of the Conference on Big Data from Space (BiDS)

Abstract

Multi-GPU systems are in continuous development to deal with the challenges of intensive computational big data problems. On the one hand, parallel architectures provide a tremendous computation capacity and outstanding scalability. On the other hand, the production path in multi-user environment faces several roadblocks since they do not grant root privileges to the users. Containers provide flexible strategies for packing, deploying and running isolated application processes within multi-user systems and enable scientific reproducibility. This paper describes the usage and advantages that the uDocker container tool offers for the development of deep learning models in the described context. The experimental results show that uDocker is more transparent to deploy for less tech-savvy researchers and allows the application to achieve processing time with negligible overhead compared to an uncontainerized environment.

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

2018

J. Lange, G. Cavallaro, M. Götz, E. Erlingsson and M. Riedel

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

Supervised image classification is one of the essential techniques for generating semantic maps from remotely sensed images. The lack of labeled ground truth datasets, due to the inherent time effort and cost involved in collecting training samples, has led to the practice of training and validating new classifiers within a single image. In line with that, the dominant approach for the division of the available ground truth into disjoint training and test sets is random sampling. This paper discusses the problems that arise when this strategy is adopted in conjunction with spectral-spatial and pixel-wise classifiers such as 3D Convolutional Neural Networks (3D CNN). It is shown that a random sampling scheme leads to a violation of the independence assumption and to the illusion that global knowledge is extracted from the training set. To tackle this issue, two improved sampling strategies based on the Density-Based Clustering Algorithm (DBSCAN) are proposed. They minimize the violation of the train and test samples independence assumption and thus ensure an honest estimation of the generalization capabilities of the classifier.

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

2018

E. Erlingsson, G. Cavallaro, M. Riedel and H. Neukirchen

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

Progress in sensor technology leads to an ever-increasing amount of remote sensing data which needs to be classified in order to extract information. This big amount of data requires parallel processing by running parallel implementations of classification algorithms, such as Support Vector Machines (SVMs), on High-Performance Computing (HPC) clusters. Tomorrow's supercomputers will be able to provide exascale computing performance by using specialised hardware accelerators. However, existing software processing chains need to be adapted to make use of the best fitting accelerators. To address this problem, a mapping of an SVM remote sensing classification chain to the Dynamical Exascale Entry Platform (DEEP), a European pre-exascale platform, is presented. It will allow to scale SVM-based classifications on tomorrow's hardware towards exascale performance.

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

2018

M. Shahbaz, G. Cavallaro, B. Hagemeier, M. Riedel and H. Neukirchen

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

The progress of remote sensing technologies leads to increased supply of high-resolution image data. However, solutions for processing large volumes of data are lagging behind: desktop computers cannot cope anymore with the requirements of macro-scale remote sensing applications; therefore, parallel methods running in High-Performance Computing (HPC) environments are essential. Managing an HPC processing pipeline is non-trivial for a scientist, especially when the computing environment is heterogeneous and the set of tasks has complex dependencies. This paper proposes an end-to-end scientific workflow approach based on the UNICORE workflow management system for automating the full chain of Support Vector Machine (SVM)-based classification of remotely sensed images. The high-level nature of UNICORE workflows allows to deal with heterogeneity of HPC computing environments and offers powerful workflow operations such as needed for parameter sweeps. As a result, the remote sensing workflow of SVM-based classification becomes re-usable across different computing environments, thus increasing usability and reducing efforts for a scientist.

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

2017

S. Memon, G. Cavallaro, M. Riedel and H. Neukirchen

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

Classification of remote sensing images often use Support Vector Machines (SVMs) that require an n-fold cross-validation phase in order to do model selection. This phase is characterized by sweeping through a wide set of parameter combinations of SVM kernel and cost parameters. As a consequence this process is computationally expensive but represents a principled way of tuning a model for better accuracy and to prevent overfitting together with regularization that is in SVMs inherently solved in the optimization. Since the cross-validation technique is done in a principled way also known as `gridsearch', we aim at supporting remote sensing scientists in two ways. Firstly by reducing the time-to-solution of the cross-validation by applying state-of-the-art parallel processing methods because the sweep of parameters and cross-validation runs itself can be nicely parallelized. Secondly by reducing manual labour by automating the parallel submission processes since manually performing cross-validation is very time consuming, unintuitive, and error-prone especially in large-scale cluster or supercomputing environments (e.g., batch job scripts, node/core/task parameters, etc.).

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

2017

G. Cavallaro, M. Dalla Mura, M. Riedel, and J. A. Benediktsson

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

Self-Dual Attribute Profiles (SDAPs) have proven to be an effective method for extracting spatial features able to improve scene classification of remote sensing images with very high spatial resolution. An SDAP is a multilevel decomposition of an image obtained with a sequence of transformations performed by attribute filters over the Tree of Shapes (ToS). One of the main issues with this technique is the identification of the filter thresholds generating a SDAP composed of features that should be relevant for the classification problem. This paper proposes a tree-based supervised feature extraction strategy, which is based on Fisher's linear discriminant analysis relying on the available class information. The exploitation of the ToS structure in the threshold selection procedure allows one to avoid any prior full image filtering, as in other related techniques. Furthermore, the ToS automates and optimizes the whole process by decreasing the computational time and overcoming the conventional selection procedure based on trial and error attempts. The proposed automatic spatial feature extraction technique has been tested in the classification of a very high resolution image proving its effectiveness with respect to a conventional selection strategy.

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

2016

G. Cavallaro, M. D. Mura, E. Carlinet, T. Geraud, N. Falco and J. A. Benediktsson

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

Satellite image classification is a key task used in remote sensing for the automatic interpretation of a large amount of information. Today there exist many types of classification algorithms using advanced image processing methods enhancing the classification accuracy rate. One of the best state-of-the-art methods which improves significantly the classification of complex scenes relies on Self-Dual Attribute Profiles (SDAPs). In this approach, the underlying representation of an image is the Tree of Shapes, which encodes the inclusion of connected components of the image. The SDAP computes for each pixel a vector of attributes providing a local multiscale representation of the information and hence leading to a fine description of the local structures of the image. Instead of performing a pixel-wise classification on features extracted from the Tree of Shapes, it is proposed to directly classify its nodes. Extending a specific interactive segmentation algorithm enables it to deal with the multi-class classification problem. The method does not involve any statistical learning and it is based entirely on morphological information related to the tree. Consequently, a very simple and effective region-based classifier relying on basic attributes is presented.

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

2016

N. Falco, G. Cavallaro, P. R. Marpu and J. A. Benediktsson

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

A novel unsupervised change detection approach for multi-spectral remote sensing data based on morphological transformation is presented. Profiles obtained by attribute filters can provide a rich multi-level analysis of the contextual information. The proposed method is based on the assumption that pixels belonging to changed areas exhibit profiles with significant differences due to a variation in their geometry, whereas pixels within unchanged areas result in similar profiles due to their similar spatial characteristics. The extension to the multi-spectral scenario is performed by applying the morphological analysis on the available bands that compose a given data set. In such scenario radiometric normalization results mandatory in order to minimize the effect due to different acquisition's conditions. To this purpose, IR-MAD is performed as pre-processing. In the paper, preliminary results obtained considering a multi-temporal Landsat ETM+ data set acquired over an agriculture area are shown.

On Scalable Data Mining Techniques for Earth Science

2015

M. Goetz, M. Richerzhagen, C. Bodenstein, G. Cavallaro, P. Glock, M. Riedel and J. A. Benediktsson

International Conference On Computational Science

Abstract

One of the observations made in earth data science is the massive increase of data volume (e.g, higher resolution measurements) and dimensionality (e.g. hyper-spectral bands). Traditional data mining tools (Matlab, R, etc.) are becoming redundant in the analysis of these datasets, as they are unable to process or even load the data. Parallel and scalable techniques, though, bear the potential to overcome these limitations. In this contribution we therefore evaluate said techniques in a High Performance Computing (HPC) environment on the basis of two earth science case studies: (a) Density-based Spatial Clustering of Applications with Noise (DBSCAN) for automated outlier detection and noise reduction in a 3D point cloud and (b) land cover type classification using multi-class Support Vector Machines (SVMs) in multi- spectral satellite images. The paper compares implementations of the algorithms in traditional data mining tools with HPC realizations and ’big data’ technology stacks. Our analysis reveals that a wide variety of them are not yet suited to deal with the coming challenges of data mining tasks in earth sciences.

Automatic Morphological Attribute Profiles

2015

G. Cavallaro, M. Dalla Mura, N. Falco and J. A. Benediktsson

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

Attribute profiles (APs) have increasingly been receiving more attention over the last years, as they are able to extract and model spatial information that is useful for the analysis of remote sensing images of very high spatial resolution (VHR). However, one of the major issues in employing APs is the choice of a proper range of thresholds, able to provide a representative and non-redundant multi-level image decomposition. This paper presents a novel method for the automatic selection of adequate thresholds to compute the AP. A new concept of cumulative function, which can be seen as an extension of the basic notion of granulometry, is introduced. In particular, different information on the spatial context is achieved according to the measure used for computing the cumulative function, which is computed on the AP composed by considering all possible values of the attribute. The proposed approach aims at selecting the set of thresholds that provides the best approximation of the resulting cumulative function based on the chosen measure. Experimental analysis carried out on a very high resolution image shows the effectiveness of the presented strategy in providing a set of thresholds able to retain the salient spatial structures in the scene.

Scalable Developments for Big Data Analytics in Remote Sensing

2015

G. Cavallaro, M. Riedel, M. Goetz, C. Bodenstein, M. Richerzhagen, P. Glock and J. A. Benediktsson

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

Big Data Analytics methods take advantage of techniques from the fields of data mining, machine learning, or statistics with a focus on analysing large quantities of data (aka `big datasets') with modern technologies. Big data sets appear in remote sensing in the sense of large volumes, but also in the sense of an ever increasing amount of spectral bands (i.e., high-dimensional data). The remote sensing has traditionally used the above described techniques for a wide variety of application such as classification (e.g., land cover analysis using different spectral bands from satellite data), but more recently scalability challenges occur when using traditional (often serial) methods. This paper addresses observed scalability limits when using support vector machines (SVMs) for classification and discusses scalable and parallel developments used in concrete application areas of remote sensing. Different approaches that are based on massively parallel methods are discussed as well as recent developments in parallel methods.

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

2015

G. Cavallaro, N. Falco, M. Dalla Mura, L. Bruzzone and J. A. Benediktsson

Proceedings of the 12th International Symposium on Mathematical Morphology (ISMM)

Abstract

Morphological attribute filters have been widely exploited for characterizing the spatial structures in remote sensing images. They have proven their effectiveness especially when computed in multi-scale architectures, such as for Attribute Profiles. However, the question how to choose a proper set of filter thresholds in order to build a representative profile remains one of the main issues. In this paper, a novel methodology for the selection of the filters’ parameters is presented. A set of thresholds is selected by analysing granulometric characteristic functions, which provide information on the image decomposition according to a given measure. The method exploits a tree (i.e., min-, max- or inclusion-tree) representation of an image, which allows us to avoid the filtering steps usually required prior the threshold selection, making the process computationally effective. The experimental analysis performed on two real remote sensing images shows the effectiveness of the proposed approach in providing representative and non-redundant multi-level image decompositions.

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

2015

G. Cavallaro, M. Dalla Mura and J. A. Benediktsson

Proceedings of the Joint Urban Remote Sensing Event (JURSE)

Abstract

The application of remote sensing to the study of human settlements relies on the availability of different types of image sources which provide complementary measurements for the characterization of urban areas. By analyzing images of very high spatial resolution (metric and submetric pixel size) it is possible to retrieve information on buildings (e.g., characterizing their size and shape) and districts (e.g., assessing settlement density and urban sprawl). In this context, mathematical morphology provides a set of tools that are useful for the characterization of geometrical features in urban images. Among those tools, attribute filters (AF) have proven to effectively extract these spatial characteristics. In this paper, we propose AF based on the inclusion tree structure as an efficient technique for generating features suitable for structure extraction in an urban environment. We address the issue by combining the area and moment of inertia attributes and proving the potential of this filter in the analysis of the data acquired by different types of sensors (i.e., Optical, LiDAR and SAR images).

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

2015

P. Ghamisi, D. Wu, G. Cavallaro, J. A. Benediktsson, S. Phinn and N. Falco

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

With respect to the exponential increase in the number of available remote sensors in recent years, the possibility of having different types of data captured over the same scene, has resulted in many research works related to the joint use of passive and active sensors for the accurate classification of different materials. However, until now, there is a small number of research works related to the integration of highly valuable information obtained from the joint use of LiDAR and hyperspectral data. This paper proposes an efficient classification approach in terms of accuracies and demanded CPU processing time for integrating big data sets (e.g., LiDAR and hyperspectral) to provide land cover mapping capabilities at a range of spatial scales. In addition, the proposed approach is fully automatic and is able to efficiently handle big data containing a huge number of features with very limited number of training samples in few seconds.

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

2014

G. Cavallaro, M. Dalla Mura, J. A. Benediktsson and L. Bruzzone

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

In this paper we compare features obtained by different filtering strategies for morphological attribute filters by considering non-increasing attributes. The Attribute profiles (APs) and Self Dual Attribute Profiles (SDAPs) are obtained by sequentially applying attribute filters on tree-based image representations, such as Min- or Max-trees and Inclusion tree, respectively. This work aims to study the effects of using the filtering rules max, min, direct and subtractive, when considering the non-increasing attributes moment of inertia and standard deviation. A very high spatial resolution data set is used in the experiments, and the extracted information obtained by the profiles is analyzed. This is done by studying the effects on the classification accuracy by using the profiles as additional input features to a Random Forest classifier.

Smart Data Analytics Methods for Remote Sensing Applications

2014

G. Cavallaro, M. Riedel, J. A. Benediktsson, M. Goetz, T. Runarsson, K. Jonasson and T. Lippert

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Abstract

The big data analytics approach emerged that can be interpreted as extracting information from large quantities of scientific data in a systematic way. In order to have a more concrete understanding of this term we refer to its refinement as smart data analytics in order to examine large quantities of scientific data to uncover hidden patterns, unknown correlations, or to extract information in cases where there is no exact formula (e.g. known physical laws). Our concrete big data problem is the classification of classes of land cover types in image-based datasets that have been created using remote sensing technologies, because the resolution can be high (i.e. large volumes) and there are various types such as panchromatic or different used bands like red, green, blue, and nearly infrared (i.e. large variety). We investigate various smart data analytics methods that take advantage of machine learning algorithms (i.e. support vector machines) and state-of-the-art parallelization approaches in order to overcome limitations of big data processing using non-scalable serial approaches.

Detection of Hedges Based on Attribute Filters

2012

G. Cavallaro, B. Arbelot, M. Fauvel, M. D. Mura, J. A. Benediktsson, L. Bruzzone, J. Chanussot and D. Sheeren

SPIE 8537 - Image and Signal Processing for Remote Sensing XVIII

Abstract

The detection of hedges is a very important task for the monitoring of a rural environment and aiding the management of their related natural resources. Hedges are narrow vegetated areas composed of shrubs and/or trees that are usually present at the boundaries of adjacent agricultural fields. In this paper, a technique for detecting hedges is presented. It exploits the spectral and spatial characteristics of hedges. In detail, spatial features are extracted with attribute filters, which are connected operators defined in the mathematical morphology framework. Attribute filters are flexible operators that can perform a simplification of a grayscale image driven by an arbitrary measure. Such a measure can be related to characteristics of regions in the scene such as the scale, shape, contrast etc. Attribute filters can be computed on tree representations of an image (such as the component tree) which either represent bright or dark regions (with respect to their surroundings graylevels). In this work, it is proposed to compute attribute filters on the inclusion tree which is an hierarchical dual representation of an image, in which nodes of the tree corresponds to both bright and dark regions. Specifically, attribute filters are employed to aid the detection of woody elements in the image, which is a step in the process aimed at detecting hedges. In order to perform a characterization of the spatial information of the hedges in the image, different attributes have been considered in the analysis. The final decision is obtained by fusing the results of different detectors applied to the filtered image.

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

2020

R. Sedona, L. Hoffmann, R. Spang, G. Cavallaro, S. Griessbach, M. Höpfner, M. Book, M. Riedel

Atmospheric Measurement Techniques

Abstract

Polar stratospheric clouds (PSCs) play a key role in polar ozone depletion in the stratosphere. Improved observations and continuous monitoring of PSCs can help to validate and improve chemistry–climate models that are used to predict the evolution of the polar ozone hole. In this paper, we explore the potential of applying machine learning (ML) methods to classify PSC observations of infrared limb sounders. Two datasets were considered in this study. The first dataset is a collection of infrared spectra captured in Northern Hemisphere winter 2006/2007 and Southern Hemisphere winter 2009 by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument on board the European Space Agency's (ESA) Envisat satellite. The second dataset is the cloud scenario database (CSDB) of simulated MIPAS spectra. We first performed an initial analysis to assess the basic characteristics of the CSDB and to decide which features to extract from it. Here, we focused on an approach using brightness temperature differences (BTDs). From both the measured and the simulated infrared spectra, more than 10 000 BTD features were generated. Next, we assessed the use of ML methods for the reduction of the dimensionality of this large feature space using principal component analysis (PCA) and kernel principal component analysis (KPCA) followed by a classification with the support vector machine (SVM). The random forest (RF) technique, which embeds the feature selection step, has also been used as a classifier. All methods were found to be suitable to retrieve information on the composition of PSCs. Of these, RF seems to be the most promising method, being less prone to overfitting and producing results that agree well with established results based on conventional classification methods.

Remote Sensing Big Data Classification with High Performance Distributed Deep Learning

2019

R. Sedona, G. Cavallaro, J. Jitsev, A. Strube, M. Riedel, J. A. Benediktsson

Remote Sensing

Abstract

High-Performance Computing (HPC) has recently been attracting more attention in remote sensing applications due to the challenges posed by the increased amount of open data that are produced daily by Earth Observation (EO) programs. The unique parallel computing environments and programming techniques that are integrated in HPC systems are able to solve large-scale problems such as the training of classification algorithms with large amounts of Remote Sensing (RS) data. This paper shows that the training of state-of-the-art deep Convolutional Neural Networks (CNNs) can be efficiently performed in distributed fashion using parallel implementation techniques on HPC machines containing a large number of Graphics Processing Units (GPUs). The experimental results confirm that distributed training can drastically reduce the amount of time needed to perform full training, resulting in near linear scaling without loss of test accuracy.

Cloud Deep Networks for Hyperspectral Image Analysis

2019

J. M. Haut, J. A. Gallardo, M. E. Paoletti, G. Cavallaro, J. Plaza, A. Plaza and M. Riedel

IEEE Transactions on Geoscience and Remote Sensing

Abstract

Advances in remote sensing hardware have led to a significantly increased capability for high-quality data acquisition, which allows the collection of remotely sensed images with very high spatial, spectral, and radiometric resolution. This trend calls for the development of new techniques to enhance the way that such unprecedented volumes of data are stored, processed, and analyzed. An important approach to deal with massive volumes of information is data compression, related to how data are compressed before their storage or transmission. For instance, hyperspectral images (HSIs) are characterized by hundreds of spectral bands. In this sense, high-performance computing (HPC) and high-throughput computing (HTC) offer interesting alternatives. Particularly, distributed solutions based on cloud computing can manage and store huge amounts of data in fault-tolerant environments, by interconnecting distributed computing nodes so that no specialized hardware is needed. This strategy greatly reduces the processing costs, making the processing of high volumes of remotely sensed data a natural and even cheap solution. In this paper, we present a new cloud-based technique for spectral analysis and compression of HSIs. Specifically, we develop a cloud implementation of a popular deep neural network for non-linear data compression, known as autoencoder (AE). Apache Spark serves as the backbone of our cloud computing environment by connecting the available processing nodes using a master-slave architecture. Our newly developed approach has been tested using two widely available HSI data sets. Experimental results indicate that cloud computing architectures offer an adequate solution for managing big remotely sensed data sets.

Parallel Computation of Component Trees on Distributed Memory Machines

2018

M. Goetz, G. Cavallaro, T. Geraud, M. Book and M. Riedel

IEEE Transactions on Parallel and Distributed Systems (TPDS)

Abstract

Component trees are region-based representations that encode the inclusion relationship of the threshold sets of an image. These representations are one of the most promising strategies for the analysis and the interpretation of spatial information of complex scenes as they allow the simple and efficient implementation of connected filters. This work proposes a new efficient hybrid algorithm for the parallel computation of two particular component trees-the max- and min-tree-in shared and distributed memory environments. For the node-local computation a modified version of the flooding-based algorithm of Salembier is employed. A novel tuple-based merging scheme allows to merge the acquired partial images into a globally correct view. Using the proposed approach a speed-up of up to 44.88 using 128 processing cores on eight-bit gray-scale images could be achieved. This is more than a five-fold increase over the state-of-the-art shared-memory algorithm, while also requiring only one-thirty-second of the memory.

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

2017

P. Ghamisi, G. Cavallaro, Dan, Wu, J. A. Benediktsson and A. Plaza

Computer Vision and Pattern Recognition

Abstract

In this paper, an approach is proposed to fuse LiDAR and hyperspectral data, which considers both spectral and spatial information in a single framework. Here, an extended self-dual attribute profile (ESDAP) is investigated to extract spatial information from a hyperspectral data set. To extract spectral information, a few well-known classifiers have been used such as support vector machines (SVMs), random forests (RFs), and artificial neural networks (ANNs). The proposed method accurately classify the relatively volumetric data set in a few CPU processing time in a real ill-posed situation where there is no balance between the number of training samples and the number of features. The classification part of the proposed approach is fully-automatic.

Automatic Attribute Profiles

2017

G. Cavallaro, N. Falco, M. D. Mura and J. A. Benediktsson

IEEE Transactions on Image Processing (TIP)

Abstract

Morphological attribute profiles are multilevel decompositions of images obtained with a sequence of transformations performed by connected operators. They have been extensively employed in performing multiscale and region-based analysis in a large number of applications. One main, still unresolved, issue is the selection of filter parameters able to provide representative and non-redundant threshold decomposition of the image. This paper presents a framework for the automatic selection of filter thresholds based on Granulometric Characteristic Functions (GCFs). GCFs describe the way that non-linear morphological filters simplify a scene according to a given measure. Since attribute filters rely on a hierarchical representation of an image (e.g., the Tree of Shapes) for their implementation, GCFs can be efficiently computed by taking advantage of the tree representation. Eventually, the study of the GCFs allows the identification of a meaningful set of thresholds. Therefore, a trial and error approach is not necessary for the threshold selection, automating the process and in turn decreasing the computational time. It is shown that the redundant information is reduced within the resulting profiles (a problem of high occurrence, as regards manual selection). The proposed approach is tested on two real remote sensing data sets, and the classification results are compared with strategies present in the literature.

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

2016

G. Cavallaro, M. Dalla Mura, J. A. Benediktsson and A. Plaza

IEEE Transactions on Geoscience and Remote Sensing (TGRS)

Abstract

Remotely sensed images with very high spatial resolution provide a detailed representation of the surveyed scene with a geometrical resolution that, at the present, can be up to 30 cm (WorldView-3). A set of powerful image processing operators have been defined in the mathematical morphology framework. Among those, connected operators [e.g., attribute filters (AFs)] have proven their effectiveness in processing very high resolution images. AFs are based on attributes which can be efficiently implemented on tree-based image representations. In this paper, we considered the definition of min, max, direct, and subtractive filter rules for the computation of AFs over the tree-of-shapes representation. We study their performance on the classification of remotely sensed images. We compare the classification results over the tree of shapes with the results obtained when the same rules are applied on the component trees. The random forest is used as a baseline classifier, and the experiments are conducted using multispectral data sets acquired by QuickBird and IKONOS sensors over urban areas.

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

2015

G. Cavallaro, M. Riedel, M. Richerzhagen, J. A. Benediktsson and A. Plaza

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS)

Abstract

Owing to the recent development of sensor resolutions onboard different Earth observation platforms, remote sensing is an important source of information for mapping and monitoring natural and man-made land covers. Of particular importance is the increasing amounts of available hyperspectral data originating from airborne and satellite sensors such as AVIRIS, HyMap, and Hyperion with very high spectral resolution (i.e., high number of spectral channels) containing rich information for a wide range of applications. A relevant example is the separation of different types of land-cover classes using the data in order to understand, e.g., impacts of natural disasters or changing of city buildings over time. More recently, such increases in the data volume, velocity, and variety of data contributed to the term big data that stand for challenges shared with many other scientific disciplines. On one hand, the amount of available data is increasing in a way that raises the demand for automatic data analysis elements since many of the available data collections are massively underutilized lacking experts for manual investigation. On the other hand, proven statistical methods (e.g., dimensionality reduction) driven by manual approaches have a significant impact in reducing the amount of big data toward smaller smart data contributing to the more recently used terms data value and veracity (i.e., less noise, lower dimensions that capture the most important information). This paper aims to take stock of which proven statistical data mining methods in remote sensing are used to contribute to smart data analysis processes in the light of possible automation as well as scalable and parallel processing techniques. We focus on parallel support vector machines (SVMs) as one of the best out-of-the-box classification methods.

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

2015

G. Cavallaro, M. Dalla Mura, J. A. Benediktsson and L. Bruzzone

IEEE Geoscience and Remote Sensing Letters (GRSL)

Abstract

In this letter, we explore the use of self-dual attribute profiles (SDAPs) for the classification of hyperspectral images. The hyperspectral data are reduced into a set of components by nonparametric weighted feature extraction (NWFE), and a morphological processing is then performed by the SDAPs separately on each of the extracted components. Since the spatial information extracted by SDAPs results in a high number of features, the NWFE is applied a second time in order to extract a fixed number of features, which are finally classified. The experiments are carried out on two hyperspectral images, and the support vector machines and random forest are used as classifiers. The effectiveness of SDAPs is assessed by comparing its results against those obtained by an approach based on extended APs.

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

2014

P. Ghamisi, J. A. Benediktsson, G. Cavallaro and A. Plaza

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS)

Abstract

Supervised classification plays a key role in terms of accurate analysis of hyperspectral images. Many applications can greatly benefit from the wealth of spectral and spatial information provided by these kind of data, including land-use and land-cover mapping. Conventional classifiers treat hyperspectral images as a list of spectral measurements and do not consider spatial dependencies of the adjacent pixels. To overcome these limitations, classifiers need to use both spectral and spatial information. In this paper, a framework for automatic spectral-spatial classification of hyperspectral images is proposed. In order to extract the spatial information, Extended Multi-Attribute Profiles (EMAPs) are taken into account. In addition, in order to reduce the redundancy of features and address the so-called curse of dimensionality, different supervised feature extraction (FE) techniques are considered. The final classification map is provided by using a random forest classifier. The proposed automatic framework is tested on two widely used hyperspectral data sets; Pavia University and Indian Pines. Experimental results confirm that the proposed framework automatically provides accurate classification maps in acceptable CPU processing times.

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

2018

J. A. Benediktsson, G. Cavallaro, N. Falco, I. Hedhli, V. A. Krylov, G. Moser, S. B. Serpico and J. Zerubia

Mathematical Models for Remote Sensing Image Processing

Abstract

Current and forthcoming sensor technologies and space missions are providing remote sensing scientists and practitioners with an increasing wealth and variety of data modalities. They encompass multisensor, multiresolution, multiscale, multitemporal, multipolarization, and multifrequency imagery. While they represent remarkable opportunities for the applications, they pose important challenges to the development of mathematical methods aimed at fusing the information conveyed by the input multisource data. In this framework, the present chapter continues the discussion of remote sensing data fusion, which was opened in the previous chapter. Here, the focus is on data fusion for image classification purposes. Both methodological issues of feature extraction and supervised classification are addressed. On both respects, the focus is on hierarchical image models rooted in graph theory. First, multilevel feature extraction is addressed through the latest advances in Mathematical Morphology and attribute profile theory with respect to component trees and trees of shapes. Then, joint supervised classification of multisensor, multiscale, multiresolution, and multitemporal imagery is formulated through hierarchical Markov random fields on quad-trees. Examples of experimental results with data from current VHR optical and SAR missions are shown and analysed.

Analyzing Remote Sensing Images with Hierarchical Morphological Representations

2016

G. Cavallaro, M. Dalla Mura and J. A. Benediktsson

Handbook of Pattern Recognition and Computer Vision, 5th edition

Abstract

Very high resolution (VHR) images provide a detailed representation of the surveyed scene with a geometrical resolution that at present can be up to 30 cm (WorldView-3). One of the most promising strategy for the analysis and the interpretation of a scene relies on hierarchical representations of the spatial content of an image. The hierarchical structure of a tree is useful for gathering the heterogeneous characteristics of the objects among different spatial scales (i.e., from the pixel level up to the entire image). In the remote sensing literature, there are several contributions addressing the use of hierarchical representations in many tasks such as filtering, segmentation, classification, object detection, change detection, and considering different types of images (e.g., panchromatic, multispectral, hyperspectral). The structure of each representation can vary significantly and can be efficiently adopted for a specific remote sensing application. After providing an overview of the different hierarchical representations, this chapter focuses on the tree structures on which image filtering (here we consider morphological attribute filters) can be efficiently implanted. Particular attention is paid to the tree of shapes, which is an important morphological structure that represents images in a self-dual way. Moreover, the identification of structures with heterogeneous characteristics (i.e., scale and shape) can be effectively done when computing attribute filters in multi-scale architectures, for instance Self-Dual Attribute Profiles (SDAPs). In this chapter we focus on the use of multilevel filtering based on hierarchical representations of the image for land cover classification. In particular, the experimental results reported in the literature for the classification of SDAPs computed on remotely sensed images are presented. Since the effectiveness of SDAPs is strictly correlated to the set of the filter parameters selected for the filtering we report on a technique for the automatic selection on the filter's parameters in order to obtain a profile that is representative and a non-redundant.