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Sparse distributed multitemporal hyperspectral unmixing / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6069 - 6084 Titre : Sparse distributed multitemporal hyperspectral unmixing Type de document : Article/Communication Auteurs : Jakob Sigurdsson, Auteur ; Magnus Orn Ulfarsson, Auteur ; Johannes R. Sveinsson, Auteur ; José M. Bioucas-Dias, Auteur Année de publication : 2017 Article en page(s) : pp 6069 - 6084 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] image hyperspectrale [Termes IGN] image multitemporelle Résumé : (Auteur) Blind hyperspectral unmixing jointly estimates spectral signatures and abundances in hyperspectral images (HSIs). Hyperspectral unmixing is a powerful tool for analyzing hyperspectral data. However, the usual huge size of HSIs may raise difficulties for classical unmixing algorithms, namely, due to limitations of the hardware used. Therefore, some researchers have considered distributed algorithms. In this paper, we develop a distributed hyperspectral unmixing algorithm that uses the alternating direction method of multipliers and ℓ1 sparse regularization. The hyperspectral unmixing problem is split into a number of smaller subproblems that are individually solved, and then the solutions are combined. A key feature of the proposed algorithm is that each subproblem does not need to have access to the whole HSI. The algorithm may also be applied to multitemporal HSIs with due adaptations accounting for variability that often appears in multitemporal images. The effectiveness of the proposed algorithm is evaluated using both simulated data and real HSIs. Numéro de notice : A2017-742 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2720539 En ligne : https://doi.org/10.1109/TGRS.2017.2720539 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88776 [article]

A fast cloud detection algorithm applicable to monitoring and nowcasting of daytime cloud systems / Xiao-Yong Zhuge in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6111 - 6119 Titre : A fast cloud detection algorithm applicable to monitoring and nowcasting of daytime cloud systems Type de document : Article/Communication Auteurs : Xiao-Yong Zhuge, Auteur ; Xiaolei Zou, Auteur ; Yuan Wang, Auteur Année de publication : 2017 Article en page(s) : pp 6111 - 6119 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] détection des nuages [Termes IGN] Himawari Résumé : (Auteur) The Advanced Himawari Imager (AHI) onboard Japanese geostationary satellite Himawari-8 provides two more visible, three more near-infrared, and six more infrared channels than the only one visible and four infrared channels available from the previous geostationary imager instruments. By taking advantage of AHI's newly added channels 1, 3, and 4 with wavelengths centered at 0.46, 0.64, and $0.86 μm, respectively, a fast cloud detection algorithm is developed. Since the spectral differences of the reflectance between any two of AHI's channels 1, 3, and 4 over clouds are smaller than those over land and ocean, a visible-based cloud index (VCI) for daytime cloud detection can thus be defined by the root mean square of the three differences between any two of these three channels. An AHI pixel is identified as cloudy if the VCI is smaller than a threshold, which has different values over ocean and land. Cloud detection is further adjusted by a bias correction using AHI channels 7 and 13. The average accuracy of the proposed simple cloud detection is comparable with those obtained from a more complicated cloud mask algorithm involving not only more channels but also model simulations. It is also found that the bias correction is needed mostly over cirrus clouds and Gobi. Numéro de notice : A2017-743 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2720664 En ligne : https://doi.org/10.1109/TGRS.2017.2720664 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88777 [article]

Incidence angle dependence of first-year sea ice backscattering coefficient in Sentinel-1 SAR Imagery over the kara sea / Marko P. Mäkynen in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6170 - 6181 Titre : Incidence angle dependence of first-year sea ice backscattering coefficient in Sentinel-1 SAR Imagery over the kara sea Type de document : Article/Communication Auteurs : Marko P. Mäkynen, Auteur ; Juha Karvonen, Auteur Année de publication : 2017 Article en page(s) : pp 6170 - 6181 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] angle d'incidence [Termes IGN] coefficient de rétrodiffusion [Termes IGN] données polarimétriques [Termes IGN] image radar moirée [Termes IGN] polarimétrie radar [Termes IGN] régression linéaire Résumé : (Auteur) We have studied the incidence angle (θ0) dependence of the sea ice backscattering coefficient (0.°) for Sentinel-1 (S-1) extra wide (EW) mode dualpolarization (HH/HV) synthetic aperture radar (SAR) imagery acquired over the Kara Sea under winter and summer melting conditions. The determination of the 0.° versus θ0 dependence was based on SAR image pairs acquired on ascending and descending orbits over the same sea ice area with a short time difference. The SAR noise floor was subtracted from the HV images. From the image pairs 1.1 by 1.1 km windows representing level first-year ice (LFYI) and deformed first-year ice (DFYI) were manually selected, and a linear regression was fit between the resulting 0.° and θ0 differences of the windows to estimate the slope b1 (dB/1°) between 0.° and θ0. For example, under winter condition b1 for DFYI at HHand HV-polarizations was found to be -0.24 and -0.16 dB/1°, respectively, and b1 for LFYI at HH-polarization was -0.25 dB/1°. It was not possible to determine a reliable b1 for LFYI at HV due to a contamination effect of the S-1 noise floor. The b1 values at HH compared well with previous studies. They can be used to compensate the 0.° incidence angle variation in the S-1 EW SAR images with good accuracy. The HH b1 values are applicable to other S-1 imaging modes and other C-band SAR sensors like RADARSAT-2. Unfortunately, the HV b1 values are specific to the S-1 EW mode due to the noise floor problem. Numéro de notice : A2017-744 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2720664 En ligne : https://doi.org/10.1109/TGRS.2017.2720664 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88778 [article]

Sparse bayesian learning-based time-variant deconvolution / Sanyi Yuan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6182 - 6194 Titre : Sparse bayesian learning-based time-variant deconvolution Type de document : Article/Communication Auteurs : Sanyi Yuan, Auteur ; Shangxu Wang, Auteur ; Ming Ma, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 6182 - 6194 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] amélioration du contraste [Termes IGN] apprentissage automatique [Termes IGN] déconvolution Résumé : (Auteur) In seismic exploration, the wavelet-filtering effect and Q-filtering (amplitude attenuation and velocity dispersion) effect blur the reflection image of subsurface layers. Therefore, both wavelet- and Q-filtering effects should be reduced to retrieve a high-quality subsurface image, which is significant for fine reservoir interpretation. We derive a nonlinear time-variant convolution model to sparsely represent nonstationary seismograms in time domain involving these two effects and present a time-variant deconvolution (TVD) method based on sparse Bayesian learning (SBL) to solve the model to obtain a high-quality reflectivity image. The SBL-based TVD essentially obtains an optimum posterior mean of the reflectivity image, which is regarded as the inverted reflectivity result, by iteratively solving a Bayesian maximum posterior and a type-II maximum likelihood. Because a hierarchical Gaussian prior for reflectivity controlled by model-dependent hyper-parameters is adopted to approximately represent the fact that reflectivity is sparse, SBL-based TVD can retrieve a sparse reflectivity image through the principled sequential addition and deletion of Q-dependent time-variant wavelets. In general, strong reflectors are acquired relatively earlier, whereas weak reflectors and deep reflectors are imaged later. The method has the capacity to avoid false artifacts represented by sequential positive or negative reflectivity spikes with short two-way travel time, which typically occur within stationary deconvolution outcomes. Synthetic, laboratorial, and field data examples are used to demonstrate the effectiveness of the method and illustrate its advantages over SBL-based stationary deconvolution and TVD using an l2-norm or an l1-norm regularization. The results show that SBL-based TVD is a potentially effective, stable, and high-quality imaging tool. Numéro de notice : A2017-745 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2722223 En ligne : https://doi.org/10.1109/TGRS.2017.2722223 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88779 [article]

Fusing microwave and optical satellite observations to simultaneously retrieve surface soil moisture, vegetation water content, and surface soil roughness / Yohei Sawada in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6195 - 6206 Titre : Fusing microwave and optical satellite observations to simultaneously retrieve surface soil moisture, vegetation water content, and surface soil roughness Type de document : Article/Communication Auteurs : Yohei Sawada, Auteur ; Toshio Koike, Auteur ; Kentaro Aida, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 6195 - 6206 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] fusion d'images [Termes IGN] humidité du sol [Termes IGN] image Aqua-AMSR [Termes IGN] image Aqua-MODIS [Termes IGN] image optique [Termes IGN] image radar [Termes IGN] rugosité du sol [Termes IGN] teneur en eau de la végétation Résumé : (Auteur) Uncertainty in surface soil roughness strongly degrades the performance of surface soil moisture (SSM) and vegetation water content (VWC) retrieval from passive microwave observations. This paper proposes an algorithm to objectively determine the surface soil roughness parameter of the radiative transfer model by fusing microwave and optical satellite observations. It is then demonstrated in a semiarid in situ observation site. The roughness correction of this new algorithm positively impacted the performance of SSM (root-mean-square error reduced from 0.088 to 0.070) and VWC retrieval from the Advanced Microwave Scanning Radiometer 2 and Moderate Resolution Imaging Spectroradiometer. Since this surface soil roughness correction may be transferrable to other microwave satellite retrieval algorithms such as those for the Soil Moisture and Ocean Salinity and Soil Moisture Active Passive satellites, this new algorithm can contribute to many microwave earth surface observation satellite missions. Numéro de notice : A2017-746 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2722468 En ligne : https://doi.org/10.1109/TGRS.2017.2722468 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88781 [article]

Spatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing / Xinyu Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6287 - 6304 Titre : Spatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing Type de document : Article/Communication Auteurs : Xinyu Wang, Auteur ; Yanfei Zhong, Auteur ; Liangpei Zhang, Auteur ; Yanyan Xu, Auteur Année de publication : 2017 Article en page(s) : pp 6287 - 6304 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] factorisation de matrice non-négative [Termes IGN] image hyperspectrale [Termes IGN] optimisation (mathématiques) [Termes IGN] segmentation d'image Résumé : (Auteur) In recent years, blind source separation (BSS) has received much attention in the hyperspectral unmixing field due to the fact that it allows the simultaneous estimation of both endmembers and fractional abundances. Although great performances can be obtained by the BSS-based unmixing methods, the decomposition results are still unstable and sensitive to noise. Motivated by the first law of geography, some recent studies have revealed that spatial information can lead to an improvement in the decomposition stability. In this paper, the group-structured prior information of hyperspectral images is incorporated into the nonnegative matrix factorization optimization, where the data are organized into spatial groups. Pixels within a local spatial group are expected to share the same sparse structure in the low-rank matrix (abundance). To fully exploit the group structure, image segmentation is introduced to generate the spatial groups. Instead of a predefined group with a regular shape (e.g., a cross or a square window), the spatial groups are adaptively represented by superpixels. Moreover, the spatial group structure and sparsity of the abundance are integrated as a modified mixed-norm regularization to exploit the shared sparse pattern, and to avoid the loss of spatial details within a spatial group. The experimental results obtained with both simulated and real hyperspectral data confirm the high efficiency and precision of the proposed algorithm. Numéro de notice : A2017-747 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2724944 En ligne : https://doi.org/10.1109/TGRS.2017.2724944 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88782 [article]

Fusion of hyperspectral and LiDAR data using sparse and low-rank component analysis / Behnood Rasti in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6354 - 6365 Titre : Fusion of hyperspectral and LiDAR data using sparse and low-rank component analysis Type de document : Article/Communication Auteurs : Behnood Rasti, Auteur ; Pedram Ghamisi, Auteur ; Javier Plaza, Auteur Année de publication : 2017 Article en page(s) : pp 6354 - 6365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse en composantes principales [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] fusion de données [Termes IGN] Houston (Texas) [Termes IGN] image hyperspectrale [Termes IGN] Trente Résumé : (Auteur) The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank technique is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features. The proposed fusion technique consists of two main steps. First, extinction profiles are used to extract spatial and elevation information from hyperspectral and LiDAR data, respectively. Then, the sparse and low-rank technique is utilized to estimate the low-rank fused features from the extracted ones that are eventually used to produce a final classification map. The proposed approach is evaluated over an urban data set captured over Houston, USA, and a rural one captured over Trento, Italy. Experimental results confirm that the proposed fusion technique outperforms the other techniques used in the experiments based on the classification accuracies obtained by random forest and support vector machine classifiers. Moreover, the proposed approach can effectively classify joint LiDAR and hyperspectral data in an ill-posed situation when only a limited number of training samples are available. Numéro de notice : A2017-748 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2726901 En ligne : https://doi.org/10.1109/TGRS.2017.2726901 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88783 [article]

Robust minimum volume simplex analysis for hyperspectral unmixing / Shaoquan Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6431 - 6439 Titre : Robust minimum volume simplex analysis for hyperspectral unmixing Type de document : Article/Communication Auteurs : Shaoquan Zhang, Auteur ; Alexander Agathos, Auteur ; Jun Li, Auteur Année de publication : 2017 Article en page(s) : pp 6431 - 6439 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme du simplexe [Termes IGN] analyse des mélanges spectraux [Termes IGN] factorisation [Termes IGN] image hyperspectrale [Termes IGN] méthode robuste Résumé : (Auteur) Most blind hyperspectral unmixing methods exploit convex geometry properties of hyperspectral data. The minimum volume simplex analysis (MVSA) is one of such methods, which, as many others, estimates the minimum volume (MV) simplex where the measured vectors live. MVSA was conceived to circumvent the matrix factorization step often implemented by MV-based algorithms and also to cope with outliers, which compromise the results produced by MV algorithms. Inspired by the recently proposed robust MV enclosing simplex (RMVES) algorithm, we herein introduce the robust MVSA (RMVSA), which is a version of MVSA robust to noise. As in RMVES, the robustness is achieved by employing chance constraints, which control the volume of the resulting simplex. RMVSA differs, however, substantially from RMVES in the way optimization is carried out. In this paper, we develop a linearization relaxation of the nonlinear chance constraints, which can greatly lighten the computational complex of chance constraint problems. The effectiveness of RMVSA is illustrated by comparing its performance with the state of the art. Numéro de notice : A2017-749 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2728104 En ligne : https://doi.org/10.1109/TGRS.2017.2728104 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88784 [article]

Bayesian data combination for the estimation of ionospheric effects in SAR interferograms / Giorgio Gomba in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6582 - 6593 Titre : Bayesian data combination for the estimation of ionospheric effects in SAR interferograms Type de document : Article/Communication Auteurs : Giorgio Gomba, Auteur ; Francesco De Zan, Auteur Année de publication : 2017 Article en page(s) : pp 6582 - 6593 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] image ALOS-PALSAR [Termes IGN] image radar moirée [Termes IGN] problème inverse [Termes IGN] retard ionosphèrique Résumé : (Auteur) The ionospheric propagation path delay is a major error source in synthetic aperture radar (SAR) interferograms and, therefore, has to be estimated and corrected. Various methods can be used to extract different kinds of information about the ionosphere from SAR images, with different accuracies. This paper presents a general technique, based on a Bayesian inverse problem, that combines various information sources in order to increase the estimation accuracy, and thus the correction. A physically realistic fractal modeling of the ionosphere turbulence and a data-based estimation of the model parameters allow the avoidance of arbitrary filtering windows and coefficients. To test the technique, the differential ionospheric phase screen was estimated by combining the split-spectrum method with the azimuth mutual shifts between interferometric pair images. This combination is convenient since it can benefit from the strengths of both sources: range and azimuth variations from the split-spectrum method and small-scale azimuth variations from more sensitive azimuth shifts. Therefore, the two methods can recover the long and short wavelength components of the ionospheric phase screen, respectively. A theoretical comparison between the Faraday rotation method and the split-spectrum method is also reported. For the use in the combination, precedence was then given to the split-spectrum method because of the comparable precision level, lower susceptibility to biases, and wider applicability. Finally, Advanced Land Observing Satellite Phased Array type L-band SAR L-band images are used to show how the combined result is more accurate than that obtained with the simple split-spectrum method. Numéro de notice : A2017-759 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2730438 En ligne : https://doi.org/10.1109/TGRS.2017.2730438 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88787 [article]

A batch-mode regularized multimetric active learning framework for classification of hyperspectral images / Zhou Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6594 - 6609 Titre : A batch-mode regularized multimetric active learning framework for classification of hyperspectral images Type de document : Article/Communication Auteurs : Zhou Zhang, Auteur ; Melba M. Crawford, Auteur Année de publication : 2017 Article en page(s) : pp 6594 - 6609 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] classification barycentrique [Termes IGN] image hyperspectrale [Termes IGN] précision de la classification Résumé : (Auteur) Techniques that combine multiple types of features, such as spectral and spatial features, for hyperspectral image classification can often significantly improve the classification accuracy and produce a more reliable thematic map. However, the high dimensionality of the input data and the typically limited quantity of labeled samples are two key challenges that affect classification performance of supervised methods. In order to simultaneously deal with these issues, a regularized multimetric active learning (AL) framework is proposed which consists of three main parts. First, a regularized multimetric learning approach is proposed to jointly learn distinct metrics for different types of features. The regularizer incorporates the unlabeled data based on the neighborhood relationship, which helps avoid overfitting at early stages of AL, when the quantity of training data is particularly small. Then, as AL proceeds, the regularizer is also updated through similarity propagation, thus taking advantage of informative labeled samples. Finally, multiple features are projected into a common feature space, in which a new batch-mode AL strategy combining uncertainty and diversity is utilized in conjunction with k-nearest neighbor classification to enrich the set of labeled samples. In order to evaluate the effectiveness of the proposed framework, the experiments were conducted on two benchmark hyperspectral data sets, and the results were compared to those achieved by several other state-of-the-art AL methods. Numéro de notice : A2017-760 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2730583 En ligne : https://doi.org/10.1109/TGRS.2017.2730583 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88788 [article]