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Photogrammetric point clouds: quality assessment, filtering, and change detection / Zhenchao Zhang (2022)
Titre : Photogrammetric point clouds: quality assessment, filtering, and change detection Type de document : Thèse/HDR Auteurs : Zhenchao Zhang, Auteur ; M. George Vosselman, Auteur ; Markus Gerke, Auteur ; Michael Ying Yang, Auteur Editeur : Enschede [Pays-Bas] : International Institute for Geo-Information Science and Earth Observation ITC Année de publication : 2022 Note générale : bibliographie
NB : EMBARGO SUR LE TEXTE JUSQU'AU 1ER JUILLET 2022Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] appariement dense
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] qualité des données
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) 3D change detection draws more and more attention in recent years due to the increasing availability of 3D data. It can be used in the fields of land use / land cover (LULC) change detection, 3D geographic information updating, terrain deformation analysis, urban construction monitoring et al. Our motivation to study 3D change detection is mainly related to the practical need to update the outdated point clouds captured by Airborne Laser Scanning (ALS) with new point clouds obtained by dense image matching (DIM).
The thesis has three main parts. The first part, chapter 1, explains the motivation, providing a review of current ALS and airborne photogrammetry techniques. It also presents the research objectives and questions. The second part including chapter 2 and chapter 3 evaluates the quality of photogrammetric products and investigates their potential for change detection. The third part including chapter 4 and chapter 5 proposes two methods for change detection that meet different requirements.
To investigate the potential of using point clouds derived by dense matching for change detection, we propose a framework for evaluating the quality of 3D point clouds and DSMs generated by dense image matching. Our evaluation framework based on a large number of square patches reveals the distribution of dense matching errors in the whole photogrammetric block. Robust quality measures are proposed to indicate the DIM accuracy and precision quantitatively. The overall mean offset to the reference is 0.1 Ground Sample Distance (GSD); the maximum mean deviation reaches 1.0 GSD. We also find that the distribution of dense matching errors is homogenous in the whole block and close to a normal distribution based on many patch-based samples. However, in some locations, especially along narrow alleys, the mean deviations may get worse. In addition, the profiles of ALS points and DIM points reveal that the DIM profile fluctuates around the ALS profile. We find that the accuracy of DIM point cloud improves and that the noise level decreases on smooth ground areas when oblique images are used in dense matching together with nadir images.
Then we evaluate whether the standard LiDAR filters are effective to filter dense matching points in order to derive accurate DTMs. Filtering results on a city block show that LiDAR filters perform well on the grassland, along bushes and around individual trees if the point cloud is sufficiently precise. When a ranking filter is used on the point clouds before filtering, the filtering will identify fewer but more reliable ground points. However, some small objects on the terrain will be filtered out. Since we aim at obtaining accurate DTMs, the ranking filter shows its value in identifying reliable ground points. Based on the previous findings in DIM quality, we propose a method to detect building changes between ALS and photogrammetric data. Firstly, the ALS points and DIM points are split out and concatenated with the orthoimages. The multimodal data are normalized to feed into a pseudo-Siamese Neural network for change detection. Then, the changed objects are delineated through per-pixel classification and artefact removal. The change detection module based on a pseudo-Siamese CNN can quickly localize the changes and generate coarse change maps. The next module can be used in precise mapping of change boundaries. Experimental results show that the proposed pseudo-Siamese Neural network can cope with the DIM errors and output plausible change detection results. Although the point cloud quality from dense matching is not as fine as laser scanning points, the spectral and textural information provided by the orthoimages serve as a supplement.
Considering that the tasks of semantic segmentation and change detection are correlated, we propose SiamPointNet++ model to combine the two tasks in one framework. The method outputs a pointwise joint label for each ALS point. If an ALS point is unchanged, it is assigned a semantic label; If an ALS point is changed, it is assigned a change label. The sematic and change information are included in the joint labels with minimum information redundancy. The combined Siamese network learns both intra-epoch and inter-epoch features. Intra-epoch features are extracted at multiple scales to embed the local and global information. Inter-epoch features are extracted by Conjugated Ball Sampling (CBS) and concatenated to make change inference. Experiments on the Rotterdam data set indicate that the network is effective in learning multi-task features. It is invariant to the permutation and noise of inputs and robust to the data difference between ALS and DIM data. Compared with a sophisticated object-based method and supervised change detection, this method requires much less hyper-parameters and human intervention but achieves superior performance.
As a conclusion, the thesis evaluates the quality of dense matching points and investigates its potential of updating outdated ALS points. The two change detection methods developed for different applications show their potential in the automation of topographic change detection and point cloud updating. Future work may focus on improving the generalizability and interpretability of the proposed models.Numéro de notice : 20403 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Geo-Information Science and Earth Observation : Enschede, university of Twente : 2022 DOI : 10.3990/1.9789036552653 Date de publication en ligne : 14/01/2022 En ligne : https://research.utwente.nl/en/publications/photogrammetric-point-clouds-quality [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100963
Titre : Radar backscatter contribution to tropical forest disturbance monitoring Type de document : Thèse/HDR Auteurs : Bertrand Ygorra, Auteur ; Jean-Pierre Wigneron, Directeur de thèse ; Serge Riazanoff, Directeur de thèse ; Frédéric Frappart, Directeur de thèse Editeur : Bordeaux : Université de Bordeaux Année de publication : 2022 Importance : 253 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de BordeauxLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] couvert forestier
[Termes IGN] déboisement
[Termes IGN] détection de changement
[Termes IGN] forêt tropicale
[Termes IGN] image Sentinel-SAR
[Termes IGN] nébulosité
[Termes IGN] télédétection en hyperfréquenceIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Earth Observations are increasingly used to monitor environmental problems. Its interests lie in the ability of sensors aboard satellites to provide information at global, regional and local scales. Optical remote sensing has shown great potential for the monitoring of forest disturbances. Until recently, deforestation monitoring systems were mainly based on remotely sensed optical images. In the intertropical latitudes, such images often face limitations of frequent cloud cover, leading to late detection or misdetections due to the low temporal availability of new images uncontaminated by clouds. In tropical humid forests, regrowth can close canopy gaps between two non-cloud-contaminated optical images used for detection.New SAR (Synthetic Aperture Radar) systems have opened new perspectives for forest disturbance monitoring in tropical humid forests (Sentinel-1, PALSAR-2). These active sensors penetrate the clouds. The availability of Sentinel-1 C-band images at high spatial and temporal resolutions makes it a potential substitute of optical systems for monitoring disturbances in forest covers.This work is articulated around three parts. The first part consists in the development of a new change detection method for monitoring disturbances in forest cover, based on the Cumulative Sum algorithm (CuSum) combined with a bootstrap analysis. The method was applied to time-series of Sentinel-1 Ground-Range Detected (GRD) dual polarization (VV, VH) images obtained in a legal forest concession near Kisangani in the Democratic Republic of the Congo. The results from VV and VH polarization were intersected in VV x VH result map, and a spatial recombination of a high Critical Threshold (Tc) with a low critical threshold was performed. The second part of this work is to develop a multiple-breakpoints version of the CuSum cross-Tc called ReCuSum to further enhance the ability to monitor changes in forest cover. The development was made by applying the CuSum cross-Tc over a time-series in an iterative manner, in the State of Parà, Brazilian Amazon. The third axis of this thesis is to develop a Near-Real-Time (NRT) version of the CuSum cross-Tc and to compare it with the state-of-the-art NRT algorithms (RADD, JJ-FAST GLAD, DETER-B, DETER-R). Note de contenu :
Chapter 1. General introduction
1.1. Introduction
1.2. Thesis objectives and outline
Chapter 2. Radar remote sensing
2.1. The RADAR technique
2.2. Instrumental parameters
2.3. Scattering mechanisms
2.4. Synthetic Aperture Radar
2.5. Sentinel-1
Chapter 3. Methods for monitoring forest cover change using spaceborne SAR sensors
3.1. Introduction
3.2. Publication
3.3. Contribution and perspectives
Chapter 4. Monitoring forest disturbances from Sentinel-1 time-series: a CuSum?based approach
4.1. Introduction
4.2. Publication
4.3. Conference note: IGARSS 2021
4.4. Contribution to this work and perspectives in the PhD course
Chapter 5. Multiple breakpoints Evolution of the cross-Tc CuSum: ReCuSum
5.1. Introduction
5.2. Publication
5.3. Conference note: IGARSS 2022
5.4. Contribution to this work and perspective
Chapter 6. Development of the CuSum cross-Tc as an NRT algorithm
6.1. Introduction
6.2. Publication
6.3. Contribution and perspectives
Chapter 7. Conclusion and perspectives
7.1. Conclusion
7.2. PerspectivesNuméro de notice : 26964 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Physique de l’environnement : Bordeaux : 2022 Organisme de stage : INRAE nature-HAL : Thèse DOI : sans Date de publication en ligne : 16/02/2023 En ligne : https://theses.hal.science/tel-03991973v1/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103001 The long-term development of temperate woodland creation sites: from tree saplings to mature woodlands / Elisa Fuentes-Montemayor in Forestry, an international journal of forest research, vol 95 n° 1 (January 2022)
[article]
Titre : The long-term development of temperate woodland creation sites: from tree saplings to mature woodlands Type de document : Article/Communication Auteurs : Elisa Fuentes-Montemayor, Auteur ; Kirsty J. Park, Auteur ; Kypfer Cordts, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 28 - 37 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] adaptation (biologie)
[Termes IGN] aménagement forestier
[Termes IGN] boisement artificiel
[Termes IGN] détection de changement
[Termes IGN] forêt ancienne
[Termes IGN] parcelle forestière
[Termes IGN] plantation forestière
[Termes IGN] résilience écologique
[Termes IGN] Royaume-Uni
[Termes IGN] sous-étage
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Tree planting is at the forefront of the current environmental agenda to mitigate climate change and tackle the biodiversity crisis. In the United Kingdom (UK), tree planting has been a priority for more than a century and has helped increase woodland cover from a historic low of 5 per cent at the beginning of the 20th century to a current figure of 13 per cent. However, we still know relatively little about the long-term development of woodland creation sites (particularly of native woodlands) over ecologically realistic timescales. We surveyed a chronosequence of 133 temperate woodland patches encompassing 106 woodland creation sites (10–160 years old) and 27 mature ‘ancient’ woodlands (>250 years old), using a combination of field surveys and remote sensing techniques to quantify vegetation structural changes associated with woodland development. Woodland creation sites displayed similar vegetation development patterns to those described for other woodland systems, i.e. a gradual transition as woodlands undergo ‘stand initiation’, ‘stem exclusion’ and ‘understorey re-initiation’ stages, and became more similar to ‘ancient’ woodlands over time. Structural heterogeneity, average tree size and tree density were the attributes that varied the most among woodland developmental stages. In general, structural heterogeneity and average tree size increased with woodland age, whilst tree density decreased as would be expected. Younger sites in stand initiation were strongly dominated by short vegetation, stem exclusion sites by taller trees and older sites had a more even vegetation height distribution. There was a large degree of overlap between the vegetation characteristics of woodlands in understorey re-initiation stages and older ancient woodlands (partly driven by a lack of regeneration in the understorey); these results suggest that it takes between 80 and 160 years for woodland creation sites to develop certain vegetation attributes similar to those of mature ancient woodlands included in this study. Woodland management practices to create canopy gaps and reducing grazing/browsing pressure to promote natural regeneration are likely to accelerate this transition, increase the structural heterogeneity and biodiversity value of woodland creation sites and enable adaptation and resilience to climate change. Numéro de notice : A2022-115 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.1093/forestry/cpab027 Date de publication en ligne : 03/06/2021 En ligne : https://doi.org/10.1093/forestry/cpab027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99639
in Forestry, an international journal of forest research > vol 95 n° 1 (January 2022) . - pp 28 - 37[article]The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data / Ana-Maria Olteanu-Raimond (2022)
Titre : The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data : EuroSDR and LandSense Workshop, November 24th - 25th 2020, Online Conference Type de document : Actes de congrès Auteurs : Ana-Maria Olteanu-Raimond , Auteur ; Joep Crompvoets, Auteur ; Inian Moorthy, Auteur ; Clément Mallet , Auteur ; Bénédicte Bucher , Auteur Editeur : Dublin : European Spatial Data Research EuroSDR Année de publication : 2022 Collection : EuroSDR Workshop report Projets : Landsense / Raimond, Ana-Maria Conférence : VGI4LULC 2020, The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data 24/11/2020 25/11/2020 online OA Proceedings Importance : 40 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] approche participative
[Termes IGN] cartographie collaborative
[Termes IGN] collecte de données
[Termes IGN] Corine Land Cover
[Termes IGN] détection de changement
[Termes IGN] données localisées des bénévoles
[Termes IGN] intégration de données
[Termes IGN] occupation du sol
[Termes IGN] OpenStreetMap
[Termes IGN] science citoyenne
[Termes IGN] utilisation du solRésumé : (éditeur) The report refers to the workshop that was organized on behalf of EuroSDR and the LandSense project (24-25 November 2020). LandSense aims to build a citizen observatory for Land Use and Land Cover (LULC) monitoring by proposing innovate technologies for data collection, change detection, data quality assessment and offering tools and systems to empower different communities (e.g., private companies, Non Governmental Organisation, National Mapping Agencies, research, public authorities) to monitor and report on LULC. The workshop was co-organized by the LASTIG laboratory of the University Gustave Eiffel and IGN-ENSG, the French National Mapping agency (Ana-Maria Olteanu-Raimond, Clément Mallet, Bénédicte Bucher), the Katholieke Universiteit Leuven (Joep Crompvoets), the International Institute for Applied Systems Analysis (Inian Moorthy) and EuroSDR. Note de contenu : INTRODUCTION GENERALE
1. Introduction
1.1 Land Use and Land Cover data: specificities and challenges
1.2 VGI and citizen science for LULC monitoring
2. Session 1: Use of VGI for LULC data production
2.1 National Land Cover and Land Use Information System of Spain (SIOSE)- Coordination,
production, maintenance and VGI
2.2 A fusion data approach for integrating VGI to update and enrich authoritative LULC data
2.3 OpenStreetMap for Earth Observation (OSM4EO) land use application and benchmark
2.4 Using OpenStreetMap as a data source for training classifiers to generate LULC maps
3. Session 2: Data collection and validation
3.1 A mapping prototype for land use mapping by land users
3.2 A mobile application for collecting snow data in support to satellite remote sensing
3.3 Global land cover monitoring, validation and participation: experiences from several case studies
4. Session 3: Sustainability
4.1 Crowdsourcing reference data collection for land cover and land use mapping: Findings from Picture Pile and FotoquestGo
4.2 Land Cover Monitoring System with Sentinel-Hub and Python Machine Learning Library eo-learn. Is it possible to build a fast and cost-effective LCMS?
4.3 Regular monitoring of landscape changes with Copernicus data- The German land cover change detection service
4.4 Authentication as a Service - A LandSense contribution to improve the FAIR principle in Citizen Science
5. ConclusionNuméro de notice : 28680 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Actes nature-HAL : DirectOuvrColl/Actes DOI : sans En ligne : http://www.eurosdr.net/sites/default/files/uploaded_files/eurosdr_vgi4lulc.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99973 Use of multi-temporal and multi-sensor data for continental water body extraction in the context of the SWOT mission / Nicolas Gasnier (2022)
Titre : Use of multi-temporal and multi-sensor data for continental water body extraction in the context of the SWOT mission Type de document : Thèse/HDR Auteurs : Nicolas Gasnier, Auteur ; Florence Tupin, Directeur de thèse ; Loïc Denis, Directeur de thèse Editeur : Paris : Institut Polytechnique de Paris Année de publication : 2022 Importance : 213 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de doctorat présentée à l’Institut Polytechnique de Paris, spécialité ImagesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] base de données localisées
[Termes IGN] détection d'objet
[Termes IGN] détection de changement
[Termes IGN] données hydrographiques
[Termes IGN] hauteurs de mer
[Termes IGN] image multitemporelle
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] image SWOT
[Termes IGN] lac
[Termes IGN] rivière
[Termes IGN] série temporelle
[Termes IGN] télédétection en hyperfréquenceIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Spaceborne remote sensing provides hydrologists and decision-makers with data that are essential for understanding the water cycle and managing the associated resources and risks. The SWOT satellite, which is a collaboration between the French (CNES) and American (NASA, JPL) space agencies, is scheduled for launch in 2022 and will measure the height of lakes, rivers, and oceans with high spatial resolution. It will complement existing sensors, such as the SAR and optical constellations Sentinel-1 and 2, and in situ measurements. SWOT represents a technological breakthrough as it is the first satellite to carry a near-nadir swath altimeter. The estimation of water levels is done by interferometry on the SAR images acquired by SWOT. Detecting water in these images is therefore an essential step in processing SWOT data, but it can be very difficult, especially with low signal-to-noise ratios, or in the presence of unusual radiometries. In this thesis, we seek to develop new methods to make water detection more robust. To this end, we focus on the use of exogenous data to guide detection, the combination of multi-temporal and multi-sensor data and denoising approaches. The first proposed method exploits information from the river database used by SWOT (derived from GRWL) to detect narrow rivers in the image in a way that is robust to both noise in the image, potential errors in the database, and temporal changes. This method relies on a new linear structure detector, a least-cost path algorithm, and a new Conditional Random Field segmentation method that combines data attachment and regularization terms adapted to the problem. We also proposed a method derived from GrabCut that uses an a priori polygon containing a lake to detect it on a SAR image or a time series of SAR images. Within this framework, we also studied the use of a multi-temporal and multi-sensor combination between Sentinel-1 SAR and Sentinel-2 optical images. Finally, as part of a preliminary study on denoising methods applied to water detection, we studied the statistical properties of the geometric temporal mean and proposed an adaptation of the variational method MuLoG to denoise it. Note de contenu : 1. Introduction
1.1 Context
1.2 Contributions
1.3 Organization of the manuscript
I BACKGROUND ON SAR REMOTE SENSING AND WATER SURFACE MONITORING WITH SAR IMAGES
2. SAR images
2.1 Physics and statistics of SAR images
2.2 The SWOT mission
2.3 Sentinel-1
3. SAR water detection and hydrological prior
3.1 Water detection in SAR images
3.2 SWOT processing and products
3.3 Prior water masks and databases
4. Methodological background
4.1 Markov random fields
4.2 Variational methods for image denoising
PROPOSED APPROACHES
5. Guided extraction of narrow rivers on SAR images using an exogenous river database
5.1 Introduction
5.2 Proposed river segmentation pipeline
5.3 Experimental results
5.4 Conclusion
6. Adaptation of the GrabCut method to SAR images: lake detection from a priori polygon
6.1 Single-date GrabCut method for lake detection from a priori polygon
6.2 Multitemporal and multi-sensor adaptations of the method
6.3 2D+T GrabCut of SAR images with temporal regularization for lake detection within an a priori mask
6.4 Joint 2D+T segmentation of SAR and optical images
7. Denoising of the temporal geometric mean
7.1 Introduction
7.2 Statistics of the temporal geometric mean of SAR intensities
7.3 Denoising method
7.4 Experiments
7.5 Application to change detection
7.6 Application to ratio-based denoising of single SAR images within a time series
7.7 Conclusion
8 Conclusion and perspectivesNuméro de notice : 26762 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Images : Palaiseau : 2022 Organisme de stage : Télécom Paris nature-HAL : Thèse DOI : sans Date de publication en ligne : 17/02/2022 En ligne : https://tel.hal.science/tel-03578831/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99823 Vegetation changes in the understory of nitrogen-sensitive temperate forests over the past 70 years / Marina Roth in Forest ecology and management, vol 503 (January-1 2022)PermalinkAdaptive feature weighted fusion nested U-Net with discrete wavelet transform for change detection of high-resolution remote sensing images / Congcong Wang in Remote sensing, vol 13 n° 24 (December-2 2021)PermalinkEarly detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany / Kathrin Einzmann in Remote sensing of environment, vol 266 (December 2021)PermalinkThe use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space / Renato César Dos santos in Applied geomatics, vol 13 n° 4 (December 2021)PermalinkFeature matching for multi-epoch historical aerial images: A new pipeline feature detection pipeline in open-source MicMac / Lulin Zhang in Blog de la RFPT, sans n° ([17/11/2021])PermalinkEfficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine / Yongjing Mao in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)PermalinkA repeatable change detection approach to map extreme storm-related damages caused by intense surface runoff based on optical and SAR remote sensing: Evidence from three case studies in the South of France / Arnaud Cerbelaud in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)PermalinkSeven decades of coastal change at Barter Island, Alaska: Exploring the importance of waves and temperature on erosion of coastal permafrost bluffs / Ann E. Gibbs in Remote sensing, vol 13 n° 21 (November-1 2021)PermalinkBi- and three-dimensional urban change detection using sentinel-1 SAR temporal series / Meiqin Che in Geoinformatica, vol 25 n° 4 (October 2021)PermalinkDisaster intensity-based selection of training samples for remote sensing building damage classification / Luis Moya in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkA feature based change detection approach using multi-scale orientation for multi-temporal SAR images / R. Vijaya Geetha in European journal of remote sensing, vol 54 sup 2 (2021)PermalinkGeomorphological mapping and anthropogenic landform change in an urbanizing watershed using structure-from-motion photogrammetry and geospatial modeling techniques / Peter G. Chirico in Journal of maps, vol 17 n° 4 (October 2021)PermalinkQuantifying historical landscape change with repeat photography: an accuracy assessment of geospatial data obtained through monoplotting / Ulrike Bayr in International journal of geographical information science IJGIS, vol 35 n° 10 (October 2021)PermalinkConiferous and broad-leaved forest distinguishing using L-band polarimetric SAR data / Fang Shang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)PermalinkA deep translation (GAN) based change detection network for optical and SAR remote sensing images / Xinghua Li in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)PermalinkThree-dimensional building change detection using object-based image analysis (case study: Tehran) / Fatemeh Tabib Mahmoudi in Applied geomatics, vol 13 n° 3 (September 2021)PermalinkMonitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico / Yao Gao in Geocarto international, vol 36 n° 15 ([15/08/2021])PermalinkDetecting structural changes induced by Heterobasidion root rot on Scots pines using terrestrial laser scanning / Timo P Pitkänen in Forest ecology and management, vol 492 (July-15 2021)PermalinkGlacier elevation change in the Western Qilian mountains as observed by TerraSAR-X/TanDEM-X images / Qibing Zhang in Geocarto international, vol 36 n° 12 ([01/07/2021])PermalinkJUST: MATLAB and python software for change detection and time series analysis / Ebrahim Ghaderpour in GPS solutions, vol 25 n° 3 (July 2021)PermalinkMapping sandy land using the new sand differential emissivity index from thermal infrared emissivity data / Shanshan Chen in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkSemantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images / Donato Amitrano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkSemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images / Daifeng Peng in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkMapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine / Tongxi Hu in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)PermalinkRapid ecosystem change at the southern limit of the Canadian Arctic, Torngat Mountains National Park / Emma L. Davis in Remote sensing, vol 13 n° 11 (June-1 2021)PermalinkUncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery / Mahmoud Salah in Applied geomatics, vol 13 n° 2 (June 2021)PermalinkA novel unsupervised change detection method from remotely sensed imagery based on an improved thresholding algorithm / Sara Khanbani in Applied geomatics, vol 13 n° 1 (May 2021)PermalinkA novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery / Ting Bai in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)PermalinkL'oeil de l'espace / Anonyme in Géomètre, n° 2190 (avril 2021)PermalinkDes pixels et des peuples / Laurent Polidori in Géomètre, n° 2190 (avril 2021)PermalinkShoreline changes along Northern Ibaraki Coast after the great East Japan earthquake of 2011 / Quang Nguyen Hao in Remote sensing, vol 13 n° 7 (April-1 2021)PermalinkSpectral–spatial-aware unsupervised change detection with stochastic distances and support vector machines / Rogério Galante Negri in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkUsing a fully polarimetric SAR to detect landslide in complex surroundings: Case study of 2015 Shenzhen landslide / Chaoyang Niu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkApport des images Landsat à l’étude de l’évolution de l’occupation du sol dans la plaine de Saïss au Maroc, pour la période 1987-2018 / Abdelkader El Garouani in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)Permalink3D change detection using adaptive thresholds based on local point cloud density / Dan Liu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkAssessing land use–land cover change and soil erosion potential using a combined approach through remote sensing, RUSLE and random forest algorithm / Siddhartho Shekhar Paul in Geocarto international, vol 36 n° 4 ([01/03/2021])PermalinkAutomated registration of SfM‐MVS multitemporal datasets using terrestrial and oblique aerial images / Luigi Parente in Photogrammetric record, vol 36 n° 173 (March 2021)PermalinkCharacterizing urban land changes of 30 global megacities using nighttime light time series stacks / Qiming Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)PermalinkRobust unsupervised small area change detection from SAR imagery using deep learning / Xinzheng Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)PermalinkToward a yearly country-scale CORINE land-cover map without using images: A map translation approach / Luc Baudoux in Remote sensing, Vol 13 n° 6 (March 2021)PermalinkMonitoring the coastal changes of the Po river delta (Northern Italy) since 1911 using archival cartography, multi-temporal aerial photogrammetry and LiDAR data: implications for coastline changes in 2100 A.D. / Massimo Fabris in Remote sensing, Vol 13 n° 3 (February 2021)PermalinkAnalyse spatio-temporaire des dégradations et évolution des forêts par télédétection : cas du Parc National de Theniet El Had (Algérie) / Faouzi Berrichi in Bulletin des sciences géographiques, n° 32 (2019 - 2021)PermalinkApport des données Sentinel-1 pour le suivi continu de la forêt tropicale : Cas de la Guyane / Marie Ballère (2021)PermalinkApports des méthodes d'apprentissage profond pour la reconnaissance automatique des modes d'occupation des sols et d'objets par télédétection en milieu tropical / Guillaume Rousset (2021)PermalinkAssessing the interest of a multi-modal gap-filling strategy for monitoring changes in grassland parcels / Anatol Garioud (2021)Permalink