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Facet segmentation-based line segment extraction for large-scale point clouds / Yangbin Lin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 4839 - 4854 Titre : Facet segmentation-based line segment extraction for large-scale point clouds Type de document : Article/Communication Auteurs : Yangbin Lin, Auteur ; Cheng Wang, Auteur ; Bili Chen, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 4839 - 4854 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] exploration de données [Termes IGN] extraction de traits caractéristiques [Termes IGN] segmentation d'image [Termes IGN] semis de points Résumé : (Auteur) As one of the most common features in the man-made environments, straight lines play an important role in many applications. In this paper, we present a new framework to extract line segments from large-scale point clouds. The proposed method is fast to produce results, easy for implementation and understanding, and suitable for various point cloud data. The key idea is to segment the input point cloud into a collection of facets efficiently. These facets provide sufficient information for determining linear features in the local planar region and make line segment extraction become relatively convenient. Moreover, we introduce the concept “number of false alarms” into 3-D point cloud context to filter the false positive line segment detections. We test our approach on various types of point clouds acquired from different ways. We also compared the proposed method with several other methods and provide both quantitative and visual comparison results. The experimental results show that our algorithm is efficient and effective, and produce more accurate and complete line segments than the comparative methods. To further verify the accuracy of the line segments extracted by the proposed method, we also present a line-based registration framework, which employs these line segments on point clouds registration. Numéro de notice : A2017-656 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2639025 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2639025 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87066 [article]

Critical analysis of model-based incoherent polarimetric decomposition methods and investigation of deorientation effect / Pooja Mishra in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 4868 - 4877 Titre : Critical analysis of model-based incoherent polarimetric decomposition methods and investigation of deorientation effect Type de document : Article/Communication Auteurs : Pooja Mishra, Auteur ; Akanksha Garg, Auteur ; Dharmendra Singh, Auteur Année de publication : 2017 Article en page(s) : pp 4868 - 4877 Note générale : Bibliothèque Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] décomposition d'image [Termes IGN] diffusion du rayonnement [Termes IGN] données polarimétriques [Termes IGN] image ALOS [Termes IGN] image ALOS-PALSAR [Termes IGN] occupation du sol [Termes IGN] polarimétrie radar [Termes IGN] valeur propre [Termes IGN] zone urbaine Résumé : (Auteur) This paper critically analyzes several incoherent model-based decomposition methods for assessing the effect of deorientation in characterization of various land covers. It has been found that even after performing decomposition, ambiguity still occurs in scattering response from various land covers, such as urban and vegetation. Researchers introduced the concept of deorientation to remove this ambiguity. Therefore, in this paper, a critical analysis has been carried out using seven different three- and four-component decomposition methods with and without deorientation and two Eigen decomposition-based methods to investigate the scattering response on various land covers, such as urban, vegetation, bare soil, and water. The comprehensive evaluation of decomposition and deorientation effect has been performed by both visual and quantitative analyses. Two types of quantitative analysis have been performed; first, by observing percentage of scattering power and second, by analyzing the variation in the number of pixels in different land covers for each scattering contribution. The analysis shows that deorientation increases not only the power but also the number of pixels for surface and double bounce scattering. The number of pixels representing volume scattering remain almost the same for all the methods with or without deorientation, whereas volume scattering power reduces after deorientation. Eigen decomposition-based methods are observed to solve the problem of overestimation of volume scattering power. Numéro de notice : A2017-657 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2652060 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2652060 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87067 [article]

Band subset selection for anomaly detection in hyperspectral imagery / Lin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 4887 - 4898 Titre : Band subset selection for anomaly detection in hyperspectral imagery Type de document : Article/Communication Auteurs : Lin Wang, Auteur ; Chein-I Chang, Auteur ; Li-Chien Lee, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 4887 - 4898 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] détection d'anomalie [Termes IGN] détection de cible [Termes IGN] image hyperspectrale [Termes IGN] jeu de données Résumé : (Auteur) This paper presents a new approach, called band subset selection (BSS)-based hyperspectral anomaly detection (AD), which selects multiple bands simultaneously as a band subset rather than selecting multiple bands one at a time as the tradition band selection (BS) does, referred to as sequential multiple BS (SQMBS). Its idea is to first use virtual dimensionality (VD) to determine the number of multiple bands, nBS needed to be selected as a band subset and then develop two iterative process, sequential BSS (SQ-BSS) algorithm and successive BSS (SC-BSS) algorithm to find an optimal band subset numerically among all possible nBS combinations out of the full band set. In order to terminate the search process the averaged least-squares error (ALSE) and 3-D receiver operating characteristic (3D ROC) curves are used as stopping criteria to evaluate performance relative to AD using the full band set. Experimental results demonstrate that BSS generally performs better background suppression while maintaining target detection capability compared to target detection using full band information. Numéro de notice : A2017-658 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2681278 En ligne : https://doi.org/10.1109/TGRS.2017.2681278 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87069 [article]

Recurrent neural networks to correct satellite image classification maps / Emmanuel Maggiori in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 4962 - 4971 Titre : Recurrent neural networks to correct satellite image classification maps Type de document : Article/Communication Auteurs : Emmanuel Maggiori, Auteur ; Guillaume Charpiat, Auteur ; Yuliya Tarabalka, Auteur ; Pierre Alliez, Auteur Année de publication : 2017 Article en page(s) : pp 4962 - 4971 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] classification par réseau neuronal [Termes IGN] convolution (signal) [Termes IGN] itération [Termes IGN] réseau neuronal convolutif Résumé : (Auteur) While initially devised for image categorization, convolutional neural networks (CNNs) are being increasingly used for the pixelwise semantic labeling of images. However, the proper nature of the most common CNN architectures makes them good at recognizing but poor at localizing objects precisely. This problem is magnified in the context of aerial and satellite image labeling, where a spatially fine object outlining is of paramount importance. Different iterative enhancement algorithms have been presented in the literature to progressively improve the coarse CNN outputs, seeking to sharpen object boundaries around real image edges. However, one must carefully design, choose, and tune such algorithms. Instead, our goal is to directly learn the iterative process itself. For this, we formulate a generic iterative enhancement process inspired from partial differential equations, and observe that it can be expressed as a recurrent neural network (RNN). Consequently, we train such a network from manually labeled data for our enhancement task. In a series of experiments, we show that our RNN effectively learns an iterative process that significantly improves the quality of satellite image classification maps. Numéro de notice : A2017-659 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2697453 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2697453 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87070 [article]

Using landsat surface reflectance data as a reference target for multiswath hyperspectral data collected over mixed agricultural rangeland areas / Cooper McCann in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5002 - 5014 Titre : Using landsat surface reflectance data as a reference target for multiswath hyperspectral data collected over mixed agricultural rangeland areas Type de document : Article/Communication Auteurs : Cooper McCann, Auteur ; Kevin S. Repasky, Auteur ; Mikindra Morin, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 5002 - 5014 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] agriculture [Termes IGN] image hyperspectrale [Termes IGN] image Landsat [Termes IGN] image multibande [Termes IGN] mosaïquage d'images [Termes IGN] paturage [Termes IGN] qualité radiométrique (image) [Termes IGN] réflectance de surface Résumé : (Auteur) Low-cost flight-based hyperspectral imaging systems have the potential to provide important information for ecosystem and environmental studies as well as aide in land management. To realize this potential, methods must be developed to provide large-area surface reflectance data allowing for temporal data sets at the mesoscale. This paper describes a bootstrap method of producing a large-area, radiometrically referenced hyperspectral data set using the Landsat surface reflectance (LaSRC) data product as a reference target. The bootstrap method uses standard hyperspectral processing techniques that are extended to remove uneven illumination conditions between flight passes, allowing for radiometrically self-consistent data after mosaicking. Through selective spectral and spatial resampling, LaSRC data are used as a radiometric reference target. Advantages of the bootstrap method include the need for minimal site access, no ancillary instrumentation, and automated data processing. Data from two hyperspectral flights over the same managed agricultural and unmanaged range land covering approximately 5.8 km2 acquired on June 21, 2014 and June 24, 2015 are presented. Data from a flight over agricultural land collected on June 6, 2016 are compared with concurrently collected ground-based reflectance spectra as a means of validation. Numéro de notice : A2017-665 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2699618 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2699618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87102 [article]

Remote sensing scene classification by unsupervised representation learning / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5148 - 5157 Titre : Remote sensing scene classification by unsupervised representation learning Type de document : Article/Communication Auteurs : Xiaoqiang Lu, Auteur ; Xiangtao Zheng, Auteur ; Yuan Yuan, Auteur Année de publication : 2017 Article en page(s) : pp 5148 - 5157 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage non-dirigé [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] déconvolution [Termes IGN] image à haute résolution [Termes IGN] réseau neuronal artificiel [Termes IGN] scène [Termes IGN] Sydney (Nouvelle-Galles du Sud) Résumé : (Auteur) With the rapid development of the satellite sensor technology, high spatial resolution remote sensing (HSR) data have attracted extensive attention in military and civilian applications. In order to make full use of these data, remote sensing scene classification becomes an important and necessary precedent task. In this paper, an unsupervised representation learning method is proposed to investigate deconvolution networks for remote sensing scene classification. First, a shallow weighted deconvolution network is utilized to learn a set of feature maps and filters for each image by minimizing the reconstruction error between the input image and the convolution result. The learned feature maps can capture the abundant edge and texture information of high spatial resolution images, which is definitely important for remote sensing images. After that, the spatial pyramid model (SPM) is used to aggregate features at different scales to maintain the spatial layout of HSR image scene. A discriminative representation for HSR image is obtained by combining the proposed weighted deconvolution model and SPM. Finally, the representation vector is input into a support vector machine to finish classification. We apply our method on two challenging HSR image data sets: the UCMerced data set with 21 scene categories and the Sydney data set with seven land-use categories. All the experimental results achieved by the proposed method outperform most state of the arts, which demonstrates the effectiveness of the proposed method. Numéro de notice : A2017-664 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2702596 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2702596 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87103 [article]

Tectonic and anthropogenic deformation at the Cerro Prieto geothermal step-over revealed by sentinel-1A InSAR / Xiaohua Xu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5284 - 5292 Titre : Tectonic and anthropogenic deformation at the Cerro Prieto geothermal step-over revealed by sentinel-1A InSAR Type de document : Article/Communication Auteurs : Xiaohua Xu, Auteur ; David T. Sandwell, Auteur ; Ekaterina Tymofyeyeva, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 5284 - 5292 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] alignement [Termes IGN] déformation de la croute terrestre [Termes IGN] faille géologique [Termes IGN] image Sentinel-SAR [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] Mexique [Termes IGN] série temporelle [Termes IGN] tectonique Résumé : (Auteur) The Cerro Prieto geothermal field (CPGF) lies at the step-over between the imperial and the Cerro Prieto faults in northern Baja California, Mexico. While tectonically this is the most active section of the southern San Andreas Fault system, the spatial and temporal deformation in the area is poorly resolved by the sparse global positioning system (GPS) network coverage. Moreover, interferograms from satellite observations spanning more than a few months are decorrelated due to the extensive agricultural activity in this region. Here we investigate the use of frequent, short temporal baseline interferograms offered by the new Sentinel-1A satellite to recover two components of deformation time series across these faults. Following previous studies, we developed a purely geometric approach for image alignment that achieves better than 1/200 pixel alignment needed for accurate phase recovery. We construct interferometric synthetic aperture radar time series using a coherence-based small baseline subset method with atmospheric corrections by means of common-point stacking. We did not apply enhanced spectral diversity because the burst discontinuities are generally small ( Numéro de notice : A2017-662 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2704593 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2704593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87104 [article]

Forest change detection in incomplete satellite images with deep neural networks / Salman H. Khan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5407 - 5423 Titre : Forest change detection in incomplete satellite images with deep neural networks Type de document : Article/Communication Auteurs : Salman H. Khan, Auteur ; Xuming He, Auteur ; Fatih Porikli, Auteur ; Mohammed Bennamoun, Auteur Année de publication : 2017 Article en page(s) : pp 5407 - 5423 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] analyse multirésolution [Termes IGN] apprentissage profond [Termes IGN] détection de changement [Termes IGN] forêt [Termes IGN] réflectance de surface [Termes IGN] réseau neuronal artificiel [Termes IGN] retouche [Termes IGN] surveillance de la végétation Résumé : (Auteur) Land cover change monitoring is an important task from the perspective of regional resource monitoring, disaster management, land development, and environmental planning. In this paper, we analyze imagery data from remote sensing satellites to detect forest cover changes over a period of 29 years (1987-2015). Since the original data are severely incomplete and contaminated with artifacts, we first devise a spatiotemporal inpainting mechanism to recover the missing surface reflectance information. The spatial filling process makes use of the available data of the nearby temporal instances followed by a sparse encoding-based reconstruction. We formulate the change detection task as a region classification problem. We build a multiresolution profile (MRP) of the target area and generate a candidate set of bounding-box proposals that enclose potential change regions. In contrast to existing methods that use handcrafted features, we automatically learn region representations using a deep neural network in a data-driven fashion. Based on these highly discriminative representations, we determine forest changes and predict their onset and offset timings by labeling the candidate set of proposals. Our approach achieves the state-of-the-art average patch classification rate of 91.6% (an improvement of ~16%) and the mean onset/offset prediction error of 4.9 months (an error reduction of five months) compared with a strong baseline. We also qualitatively analyze the detected changes in the unlabeled image regions, which demonstrate that the proposed forest change detection approach is scalable to new regions. Numéro de notice : A2017-663 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2707528 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2707528 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87105 [article]