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Multi-temporal image change mining based on evidential conflict reasoning / Fatma Haouas in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 59 - 75 Titre : Multi-temporal image change mining based on evidential conflict reasoning Type de document : Article/Communication Auteurs : Fatma Haouas, Auteur ; Basel Solaiman, Auteur ; Zouhour Ben Dhiaf, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 59 - 75 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] conflit d'intégration [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] raisonnement spatial [Termes descripteurs IGN] théorie de Dempster-Shafer [Termes descripteurs IGN] visibilité spatio-temporelle Résumé : (Auteur) Change detection monitoring on multi-temporal remote sensed images is a persistent methodological challenge where the Dempster-Shafer, or evidence, Theory (DST) has been often applied. This paper presents a new method based on the use of DST for mining bi-temporal remotely sensed images change. The main idea is based on the investigation, analysis and interpretation of different types of conflict between two bi-temporal mass distributions. The reasoning process is focused on the conflict significance and its “partial” causes. In fact, the global conflict that occurs during the joint exploitation of multi-temporal images gives general and non-sufficiently concise information. However, the partial conflict provides rich and important information with regards to the disagreement between knowledge sources. For computing the partial conflict between focal elements, the geometric representation of mass distributions is exploited. The obtained conflict measures, caused by change, are analyzed latter by a new algorithm for drifting binary change map and identifying change directions. The effectiveness and reliability of the proposed approach are shown through experimentations on simulated changed images as well as using multi-temporal Landsat satellite images where qualitative criteria as well as quantitative measures are applied. The performances of the proposed approach, in terms of changed area recognition, are compared to three different and widely used conflict measures: the Empty-set mass, the Jousselme’s distance and the Cosine measure. It is shown that the developed change detection approach outperforms these conflict measures. Numéro de notice : A2019-204 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.02.018 date de publication en ligne : 13/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.02.018 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92667 [article]

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Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery / Lichao Mou in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 2 (February 2019) . - pp 924 - 935 Titre : Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery Type de document : Article/Communication Auteurs : Lichao Mou, Auteur ; Lorenzo Bruzzone, Auteur ; Xiao Xiang Zhu, Auteur Année de publication : 2019 Article en page(s) : pp 924 - 935 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] base d'apprentissage [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] réseau neuronal récurrent Résumé : (Auteur) Change detection is one of the central problems in earth observation and was extensively investigated over recent decades. In this paper, we propose a novel recurrent convolutional neural network (ReCNN) architecture, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection in multispectral images. To this end, we bring together a convolutional neural network and a recurrent neural network into one end-to-end network. The former is able to generate rich spectral-spatial feature representations, while the latter effectively analyzes temporal dependence in bitemporal images. In comparison with previous approaches to change detection, the proposed network architecture possesses three distinctive properties: 1) it is end-to-end trainable, in contrast to most existing methods whose components are separately trained or computed; 2) it naturally harnesses spatial information that has been proven to be beneficial to change detection task; and 3) it is capable of adaptively learning the temporal dependence between multitemporal images, unlike most of the algorithms that use fairly simple operation like image differencing or stacking. As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis. The proposed network is validated on real multispectral data sets. Both visual and quantitative analyses of the experimental results demonstrate competitive performance in the proposed mode. Numéro de notice : A2019-110 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2863224 date de publication en ligne : 20/11/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2863224 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92449 [article]

Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors / Pauline Perbet in International Journal of Remote Sensing IJRS, vol 40 n°19 (February 2019)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 40 n°19 (February 2019) . - pp 7439 - 7458 Titre : Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors Type de document : Article/Communication Auteurs : Pauline Perbet, Auteur ; Michelle Fortin, Auteur ; Anouk Ville, Auteur ; Martin Béland, Auteur Année de publication : 2019 Article en page(s) : pp 7439 - 7458 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse vectorielle [Termes descripteurs IGN] capteur spatial [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] défrichement [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] Indonésie [Termes descripteurs IGN] Malaisie Résumé : (auteur) Malaysia and Indonesia have been affected by deforestation caused in great part by the proliferation of oil palm plantations. To survey this loss of forest, several studies have monitored these southeast Asian nations with satellite remote sensing alert systems. The methods used have shown potential for this approach, but they are limited by imagery with coarse spatial resolution, low revisit times, and cloud cover. The objective of this research is to improve near real-time operational deforestation detection by combining three sensors: Sentinel-1, Sentinel-2 and Landsat-8. We used Change Vector Analysis to detect changes between non-affected forest and images under analysis. The results were validated using 166 plots of undisturbed forest and confirmed deforestation events throughout Sabah Malaysian State, and from 70 points from drone pictures in Sumatra, Indonesia. Sentinel-2 and Landsat-8 yielded sufficient results in terms of accuracy (less than 11% of commission and omission error). Sentinel-1 had lower accuracy (14% of commission error and 28% of omission error), probably resulting from geometric distortions and speckle noise. During the high cloud-cover season optical sensors took about twice the time to detect deforestation compared to Sentinel-1 which was not affected by cloud cover. By combining the three sensors, we detected deforestations about 8 days after forest clearing events. Deforestations were only detectable during approximately the first 100 days, before bare soils were often coved by legume crop. Our results indicate that near real-time deforestation detection can reveal most events, but the number of false detections could be improved using a multiple event detection process. Numéro de notice : A2019-321 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2019.1579390 date de publication en ligne : 17/02/2019 En ligne : https://doi.org/10.1080/01431161.2019.1579390 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93295 [article]

Synergetic efficiency of Lidar and WorldView-2 for 3D urban cartography in Northeast Mexico / Fabiola D. Yepez-Rincon in Geocarto international, vol 34 n° 2 (February 2019)  

Public [article]  inGeocarto international > vol 34 n° 2 (February 2019) . - pp 164 - 178 Titre : Synergetic efficiency of Lidar and WorldView-2 for 3D urban cartography in Northeast Mexico Type de document : Article/Communication Auteurs : Fabiola D. Yepez-Rincon, Auteur ; Diego Fabian Lozano Garcia, Auteur Année de publication : 2019 Article en page(s) : pp 164 - 178 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] bande infrarouge [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] cartographie urbaine [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] Mexico (Mexique) [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Three-dimensional urban cartography is needed for city changes’ assessment. The variety of studies using 3D calculations of urban elements grows each year. Building and vegetation volumes are necessary to assess and understand spatio-temporal urban changeable environments. However, there are technical questions as to which method can improve 3D urban cartographic accuracy. The innovative part of this current study is the creation of a six-band hybrid obtained from LIDAR and WorldView2 synergy. Two different enhancement algorithms demonstrated the most important spectral features for the urban development and vegetation classes. Results indicated an improvement in accuracy by up to 21.3%, according to the Kappa coefficient. Both infra-red band and intensity band were the most significant, according to the principal components analysis. The synergy delimited classes and polygons, as well as the direct display of information regarding heights of elements and improving the extraction of roads, buildings and vegetation classes. Numéro de notice : A2019-220 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1377774 date de publication en ligne : 21/09/2017 En ligne : https://doi.org/10.1080/10106049.2017.1377774 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92738 [article]

TanDEM-X digital surface models in boreal forest above-ground biomass change detection / Kirsi Karila in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 148 (February 2019) . - pp 174 - 183 Titre : TanDEM-X digital surface models in boreal forest above-ground biomass change detection Type de document : Article/Communication Auteurs : Kirsi Karila, Auteur ; Xiaowei Yu, Auteur ; Mikko Vastaranta, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 174 - 183 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] biomasse forestière [Termes descripteurs IGN] densité de la végétation [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Finlande [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] interféromètrie par radar à antenne synthétique [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] MNS TerraSAR & TanDEM-X [Termes descripteurs IGN] modèle numérique de surface Résumé : (Auteur) Satellite images provide spatially explicit information on forest change covering wide areas. In this study, bistatic TanDEM-X (TDX) synthetic aperture radar (SAR) satellite data were used to derive digital surface models (DSMs) of forest areas using SAR interferometry (InSAR). The capability of change features derived from bi-temporal InSAR DSMs to detect forest height (90th percentile of canopy height distribution, H90) and density variations was investigated. Moreover, changes in the forest above-ground biomass (AGB) were estimated from height changes between two InSAR DSMs. Bi-temporal airborne laser scanning (ALS) data, aerial orthoimages and an ALS-based AGB change map from a study area in Southern Finland were used as references. The results indicate that the InSAR height change of a forested area correlates more with vegetation density change than with height change. The correlation between the InSAR mean height change and the height change feature from ALS was 0.76 at stand level. Correspondingly, the correlation between the InSAR mean height change and the ALS penetration rate change was 0.89. The AGB changes predicted based on InSAR height change agreed well with the reference data; the root-mean-square error (RMSE) was 20.7 Mg/ha (18.5% of the mean biomass in 2012) at stand level and 27.4 Mg/ha (27.0%) for 16 × 16 m grid cells. The results show that TDX DSMs can be used to detect biomass changes of different orders of magnitude, e.g. due to logging and thinning. Numéro de notice : A2019-073 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.002 date de publication en ligne : 11/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92160 [article]

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A spatiotemporal calculus for reasoning about land-use trajectories / Adeline Marinho Maciel in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)  

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Separating the influence of vegetation changes in polarimetric differential SAR interferometry / Virginia Brancato in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)  

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Change detection based on stacked generalization system with segmentation constraint / Kun Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 11 (November 2018)  

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How to calibrate historical aerial photographs : a change analysis of naturally dynamic boreal forest landscapes / Niko Kulha in Forests, vol 9 n° 10 (October 2018)  

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Towards a polyalgorithm for land use change detection / Rishu Saxena in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)  

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An experimental framework for integrating citizen and community science into land cover, land use, and land change detection processes in a national mapping agency / Ana-Maria Olteanu-Raimond in Land, vol 7 n° 3 (September 2018)  

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An improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data / Li Zhuo in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

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Sensitivity analysis of pansharpening in hyperspectral change detection / Seyd Teymoor Seydi in Applied geomatics, vol 10 n° 1 (March 2018)  

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Détection de changement par imagerie radar sur les zones naturelles et agricoles en milieu tropical / Jérôme Lebreton (2018) 

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From Google Maps to a fine-grained catalog of street trees / Steve Branson in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)  

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