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Geoscience and Remote Sensing Symposium (M2GARSS), Mediterranean and Middle-East
2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS)
9-11 March 2020
Showing 1-25 of 91
Proceedings
The proceedings of this conference will be available for purchase through Curran Associates.
Geoscience and Remote Sensing Symposium (M2GARSS), 2020 Mediterranean and Middle-East
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[Front cover]
[M2GARSS 2020 Title Page]
[Copyright notice]
M2GARSS 2020 Reviewers
M2GARSS 2020 Table of Contents
A New CNN-RNN Framework For Remote Sensing Image Captioning
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (36)
Remote sensing (RS) image captioning has been recently attracting the attention of the community as it provides more semantic information with respect to the traditional tasks such as scene classification. Image captioning aims to generate a coherent and comprehensive description that summarizes the content of an image. The description can be obtained directly from the ground truth descriptions of...Show More
Which Cnn Layer For Which Change? A Cnn Adaptation Approach For Change Detection In Remote Sensing Data
Publication Year: 2020,Page(s):5 - 8
Cited by: Papers (1)
The purpose of this paper is to experimentally study the adaptation of convolutional neural networks (CNN) to the problem of change detection in remote sensing data. Specifically, our goal is to explore the impact of each layer of CNN (low, medium, and high level) in capturing changes. To this end, two types of changes are studied, an artificial change and a real change. The results indicates that...Show More
Deep Learning Models Performance For NDVI Time Series Prediction: A Case Study On North West Tunisia
Publication Year: 2020,Page(s):9 - 12
Cited by: Papers (11)
The main goal of this paper is to analyze the performance of two deep learning models Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM) network for non-stationary Normalized Difference Vegetation Index (NDVI) time-series prediction. Both methods have provided good performances in the different time series. The BiLSTM has shown the best agreement with the lowest root mean square error (...Show More
TLDCNN: A Triplet Low Dimensional Convolutional Neural Networks For High-Resolution Remote Sensing Image Retrieval
Publication Year: 2020,Page(s):13 - 16
Cited by: Papers (4)
Remote Sensing Image Retrieval (RSIR) is quite a challenging topic due to the complexity of High-Resolution Remote Sensing (HRRS) imagery. Recently, most researches are focusing on feature extraction using learned features instead of traditional hand-crafted features for RSIR. However, on the one hand, learning discriminative feature representations is not sufficient due to the limited ability of ...Show More
Observing Changes Due To Urbanization And Anthropogenic Activities Across The Middle East And North Africa Using Cosmo-Skymed Background Mission And Bespoke Spotlight Time Series
Publication Year: 2020,Page(s):17 - 20
Algorithms of amplitude change detection and texture extraction are applied to consistent satellite Synthetic Aperture Radar (SAR) time series collected by the Italian Space Agency (ASI)’s COSMO-SkyMed constellation over the modern town of Shahat (Libya) and the archaeological site of Apamea (Syria). The high spatial resolution and 16- day revisit time of the COSMO-SkyMed Background Mission allowe...Show More
Analysis of Ground Based SAR Images Acquired In A Small Urban Area Using Diverse Polarization Configurations
Publication Year: 2020,Page(s):21 - 24
A Ku band-terrestrial radar interferometer with different polarization combination capability, including left and right circular polarization (CP), has been used to investigate the potential of different co-polar and cross-polar acquisitions for Terrestrial Radar interferometry (TRI), aiming at improving the monitoring of landslides in semi-urban areas. After some preliminary tests carried out to ...Show More
Physics-Based Clutter Model For Geosynchronous Synthetic Aperture Radar
Publication Year: 2020,Page(s):25 - 28
In this paper the land clutter literature is briefly discussed and a need for a new class of clutter models for the Geosynchronous Synthetic Aperture Radar System Performance Assessment is shown. A new physics-based clutter model that uses data on observed vegetation motion is introduced and a wheat database is presented together with an analysis of the plant motion statistics. After this, the cha...Show More
Attribute Profiles Without Thresholds Through Histogram Based Tree Path Description
Publication Year: 2020,Page(s):29 - 32
Cited by: Papers (1)
Despite the repeatedly validated superiority of deep learning methods compared to shallow pixel desription techniques of the near past, they are heavily dependent on the abundance of training data; of which unfortunately the remote sensing field never possesses enough. Consequently, methods such as morphological attribute profiles are still among the best available options for spectral-spatial des...Show More
Unsupervised Hyperspectral Band Selection By Sequentially Clustering A Mahalanobis-Based Dissimilarity Of Spectrally Variable Endmembers
Publication Year: 2020,Page(s):33 - 36
Cited by: Papers (4)
This paper introduces an unsupervised hyperspectral band selection approach based on sequential clustering of a dissimilarity measure between spectrally variable endmembers. The proposed method combines two main techniques. It begins by automatically extracting spectrally variable endmembers, from the considered hyperspectral image, by means of a linear spectral unmixing technique that takes into ...Show More
Spectral unmixing allows to extract endmembers and estimate their proportions in hyperspectral data. Each observed pixel is considered to be a linear combination of several endmembers spectra. Based on a novel hierarchical Bayesian model, change detection into hyperspectral images is achieved by unmixing. A Gibbs sampler is proposed to overcome the complexity of integrating the resulting posterior...Show More
Coupled Nonnegative Matrix Factorization With Local Neighborhood Weights For Data Fusion
Publication Year: 2020,Page(s):41 - 44
Cited by: Papers (1)
Unmixing based hyperspectral (HS) – multispectral (MS) data fusion is a relatively recent addition to data fusion literature, and has been shown to provide robust and stable performance. Coupled nonnegative matrix factorization (CNMF) is an unmixing based data fusion method based on alternating unmixing of the HS and MS data while relating the results by point spread function (PSF) and spectral re...Show More
An NMF-Based Method For Hyperspectral Unmixing Using A Structured Additively-Tuned Linear Mixing Model To Address Spectral Variability
Salah Eddine Brezini;Moussa Sofiane Karoui;Fatima Zohra Benhalouche;Yannick Deville;Abdelaziz Ouamri
Publication Year: 2020,Page(s):45 - 48
Cited by: Papers (5)
Remote sensing hyperspectral sensors are often limited in their spatial resolutions, which results in mixed pixels. The mixture is, usually, assumed to be linear and linear spectral unmixing techniques are employed to unmix observed pixel spectra. Most of these techniques consider that each endmember is represented by the same spectrum in the whole image. Nevertheless, in various situations, each ...Show More
Constrained Nonnegative Matrix Factorization For Hyperspectral Change Detection
Publication Year: 2020,Page(s):49 - 52
Cited by: Papers (2)
This paper presents an unmixing based change detection (UBCD) approach based on constrained nonnegative matrix factorization (NMF) for hyperspectral images. UBCD provides not only multi-output change detection, but also subpixel level information about the nature of the changes that occur in the scene. The proposed method utilizes constrained NMF with the sparsity constraint for the abundances and...Show More
Development Of Algorithms For The Estimation Of Hydrological Parameters Combining Cosmo-Skymed And Sentinel Time Series With In Situ Measurements
Deodato Tapete;Francesca Cigna;Simonetta Paloscia;Emanuele Santi;Simone Pettinato;Giacomo Fontanelli;Eugenia Chiarito;Claudia Notarnicola;Giovanni Cuozzo;Alexander Jacob;Ludovica De Gregorio;Mattia Rossi
Publication Year: 2020,Page(s):53 - 56
Cited by: Papers (14)
The collaborative research project “ALGORITHMS” (20192021) between the Italian Space Agency (ASI) and the Institute of Applied Physics of the National Research Council of Italy (IFAC–CNR) aims to develop innovative algorithms to estimate the main hydrological parameters (e.g. soil moisture content, vegetation properties, snow water equivalent). The proposed algorithms combine Synthetic Aperture Ra...Show More
Development Of Algorithms For The Estimation Of Hydrological Parameters Combining Cosmo-Skymed And Sentinel Time Series With In Situ Measurements
Deodato Tapete;Francesca Cigna;Simonetta Paloscia;Emanuele Santi;Simone Pettinato;Giacomo Fontanelli;Eugenia Chiarito;Claudia Notarnicola;Giovanni Cuozzo;Alexander Jacob;Ludovica De Gregorio;Mattia Rossi
Temporal Decorrelation At C- And L-Band Over Olive Tree Plantations:First Insights From The Marocscat Campaigns
L. Villard;A. Chakir;P. Fanise;N. Ouaadi;J. Ezzahar;S. Khabba;M. Kasbani;V. Le Dantec;M. Zribi;S. Er-Raki;P.L. Frison;L. Jarlan
Publication Year: 2020,Page(s):57 - 60
This paper addresses the question of temporal decorrelation at C and L-band in the case of olive tree plantations, considering the very recent tower-based experiments in Morocco, as well as the preliminary results conducted in preparation phases. Based on the expertise gained from the TropiScat-1&2 campaigns, the presented experiments also consist in the acquisition of dense time series using a Ve...Show More
Compact and Full Polarimetric SAR Imaging for Target Characterization
Hafidha Bouzerar;Siham Mebrek;Boularbah Souissi;Abdelhamid Daamouche
Publication Year: 2020,Page(s):61 - 64
Cited by: Papers (3)
Recent interest in dual polarization Synthetic Aperture Radar (SAR) systems, in which a single polarization is transmitted (e.g. linear horizontal or right circular), followed by reception of two orthogonal polarization, has lead to a novel approach to dual-pol SAR, the so-called compact polarimetric imaging (CPSAR) mode. This paper provides techniques that allow construction of pseudo quad-pol in...Show More
Assessment Of GPU-Based Enhanced Wiener Filter On Very High Resolution Images
Publication Year: 2020,Page(s):65 - 68
Cited by: Papers (1)
Despeckling of Synthetic Aperture Radar (SAR) images is still an open issue in the remote sensing community. Many filtering approaches have been adopted by the scientific community in order to reduce speckle noise effects including, such as spatial-based and non-local techniques. Mainly all existing approaches present some limitation in terms of accuracy or computational time or edge preservation....Show More
An Approach To Super-Resolution Of Sentinel-2 Images Based On Generative Adversarial Networks
Publication Year: 2020,Page(s):69 - 72
Cited by: Papers (8)
This paper presents a generative adversarial network based super-resolution (SR) approach (which is called as S2GAN) to enhance the spatial resolution of Sentinel-2 spectral bands. The proposed approach consists of two main steps. The first step aims to increase the spatial resolution of the bands with 20m and 60m spatial resolutions by the scaling factors of 2 and 6, respectively. To this end, we...Show More
Multifrequency Polsar Image Classification Using Dual-Band 1D Convolutional Neural Networks
Publication Year: 2020,Page(s):73 - 76
Cited by: Papers (4)
In this work, we propose a novel classification approach based on dual-band one-dimensional Convolutional Neural Networks (1D-CNNs) for classification of multifrequency polarimetric SAR (PolSAR) data. The proposed approach can jointly learn from C- and L-band data and improve the single band classification accuracy. To the best of our knowledge, this is the first study that introduces 1D-CNNs to l...Show More
Proceedings
The proceedings of this conference will be available for purchase through Curran Associates.
Geoscience and Remote Sensing Symposium (M2GARSS), 2020 Mediterranean and Middle-East
- Print on DemandPurchase at Partner
- External Hard-drivePurchase at Partner
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