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CSVM architectures for pixel-wise object detection in high-resolution remote sensing images / Youyou Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6059 - 6070 Titre : CSVM architectures for pixel-wise object detection in high-resolution remote sensing images Type de document : Article/Communication Auteurs : Youyou Li, Auteur ; Farid Melgani, Auteur ; Binbin He, Auteur Année de publication : 2020 Article en page(s) : pp 6059 - 6070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] processeur graphique Résumé : (auteur) Detecting objects becomes an increasingly important task in very high resolution (VHR) remote sensing imagery analysis. With the development of GPU-computing capability, a growing number of deep convolutional neural networks (CNNs) have been designed to address the object detection challenge. However, compared with CPU, GPU is much more costly. Therefore, GPU-based methods are less attractive in practical applications. In this article, we propose a CPU-based method that is based on convolutional support vector machines (CSVMs) to address the object detection challenge in VHR images. Experiments are conducted on three VHR and two unmanned aerial vehicle (UAV) data sets with very limited training data. Results show that the proposed CSVM achieves competitive performance compared to U-Net which is an efficient CNN-based model designed for small training data sets. Numéro de notice : A2020-527 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2972289 date de publication en ligne : 02/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2972289 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95705 [article]

Evaluation of crop mapping on fragmented and complex slope farmlands through random forest and object-oriented analysis using unmanned aerial vehicles / Re-Yang Lee in Geocarto international, vol 35 n° 12 ([01/09/2020])  

Public [article]  inGeocarto international > vol 35 n° 12 [01/09/2020] . - pp 1293 - 1310 Titre : Evaluation of crop mapping on fragmented and complex slope farmlands through random forest and object-oriented analysis using unmanned aerial vehicles Type de document : Article/Communication Auteurs : Re-Yang Lee, Auteur ; Kuo-Chen Chang, Auteur ; Deng-Yuan Ou, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1293 - 1310 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] interprétation automatique [Termes descripteurs IGN] pente [Termes descripteurs IGN] Taïwan Résumé : (auteur) Conducting field research in Taiwan can be challenging because of the abundance of steep slopes. This study aimed to establish an automatic interpretation procedure applicable to exploring images of large-scale slope land taken using UAVs. The proposed method was compared with traditional field surveying and manual image interpretation techniques to determine the advantages and disadvantages of the proposed procedure in terms of efficiency. The object-based image analysis (OBIA) and texture features were first combined and the random forest (RF) classifier was then employed to interpret crop types. This study selected three sites of slope land and plains for experimentation. The obtained results indicated that the overall accuracy of the proposed classification method exceeded 91%, and the Kappa value was approximately 0.9 for all sites. In addition, interpretation of the proposed method was more efficient than that of the two traditional methods. Numéro de notice : A2020-479 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1559886 date de publication en ligne : 04/06/2019 En ligne : https://doi.org/10.1080/10106049.2018.1559886 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95628 [article]

Homogeneous tree height derivation from tree crown delineation using Seeded Region Growing (SRG) segmentation / Muhamad Farid Ramli in Geo-spatial Information Science, vol 23 n° 3 (September 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 3 (September 2020) . - pp 195 - 208 Titre : Homogeneous tree height derivation from tree crown delineation using Seeded Region Growing (SRG) segmentation Type de document : Article/Communication Auteurs : Muhamad Farid Ramli, Auteur ; Khairul Nizam Tahar, Auteur Année de publication : 2020 Article en page(s) : pp 195 - 208 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] diamètre des arbres [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] houppier [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] Malaisie [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] orthophotographie [Termes descripteurs IGN] palmier [Termes descripteurs IGN] pas d'échantillonnage au sol [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] positionnement cinématique en temps réel [Termes descripteurs IGN] QGIS [Termes descripteurs IGN] SAGA GIS [Termes descripteurs IGN] segmentation par croissance de régions Résumé : (auteur) The demand for tree height derivation is increasing year by year, especially for large plantation and forest area. The conventional method needs a long time to complete tree measurement for large forest area, especially when using a pole, measuring tape, rangefinder, clinometer, and tree climbing. This study aims to evaluate the height of oil palm tree based on crown diameter by using a multi-rotor Unmanned Aerial Vehicle (UAV). Digital Elevation Model (DEM) and orthophoto were generated by using Agisoft software, while oil palm tree crown diameter was delineated by using seed generation with Quantum Geographic Information System (QGIS) and Seeded Region Growing (SRG) segmentation methods in the System for Automated Geoscientific Analysis (SAGA). The study validates the results between the actual tree height and tree height estimated from UAV. The results showed that the orthophoto was successfully generated with a Ground Sampling Distance (GSD) of 2.95 cm and 129 tree crowns were successfully analyzed. The accuracy of the tree height as compared to the actual measurement was 57.7 cm. In conclusion, UAV images are capable of determining the tree height after going through the correct procedure to help foresters in their daily task. Numéro de notice : A2020-562 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1805366 date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.1080/10095020.2020.1805366 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95878 [article]

Mapping quality prediction for RTK/PPK-equipped micro-drones operating in complex natural environment / Emmanuel Cledat in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 24 - 38 Titre : Mapping quality prediction for RTK/PPK-equipped micro-drones operating in complex natural environment Type de document : Article/Communication Auteurs : Emmanuel Cledat, Auteur ; Laurent Valentin Jospin, Auteur ; D. A. Cucci, Auteur ; Jan Skaloud, Auteur Année de publication : 2020 Article en page(s) : pp 24 - 38 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] aérotriangulation [Termes descripteurs IGN] compensation par faisceaux [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] plan de vol [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] positionnement cinématique en temps réel [Termes descripteurs IGN] qualité cartographique [Termes descripteurs IGN] temps de vol [Termes descripteurs IGN] test statistique [Termes descripteurs IGN] texture du sol Résumé : (auteur) Drone mapping with GNSS-assisted photogrammetry is a highly efficient method for surveying small- or medium-sized areas. However, the mapping quality is not intuitively predictable, particularly in complex environments (with steep and cluttered terrain), in which the quality of the real-time kinematic (RTK) or post-processed kinematic (PPK) positioning varies. We present a method to predict the mapping quality from the information that is available prior to the flight, such as the flight plan, expected flight time, approximate digital terrain model, prevailing surface texture, and embedded sensor characteristics. After detailing the important considerations, we also present the concept of global precision within the context of minimal and efficient ground control point placement in a complex terrain. Finally, we validate the proposed methodology by means of rigorous statistical testing against numerous experiments conducted under different mapping conditions. Numéro de notice : A2020-545 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.015 date de publication en ligne : 12/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95771 [article]

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Monitoring narrow mangrove stands in Baja California Sur, Mexico using linear spectral unmixing / Jonathan B. Thayn in Marine geodesy, Vol 43 n° 5 (September 2020)  

Public [article]  inMarine geodesy > Vol 43 n° 5 (September 2020) . - pp 493 - 508 Titre : Monitoring narrow mangrove stands in Baja California Sur, Mexico using linear spectral unmixing Type de document : Article/Communication Auteurs : Jonathan B. Thayn, Auteur Année de publication : 2020 Article en page(s) : pp 493 - 508 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse linéaire des mélanges spectraux [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] Mexique [Termes descripteurs IGN] réflectance spectrale Résumé : (auteur) Small stands of mangrove trees are difficult to detect and monitor using satellite remote sensing because the width of the narrow strips of vegetation are typically much smaller than the spatial resolution of the imagery. Every mangrove pixel also contains water and bare soil reflectance. Linear spectral unmixing, which estimates the fractional presence of specific land cover types per pixel, was performed on Landsat 8 imagery to detect mangroves on the eastern shoreline of the Bay of La Paz on the Baja California Peninsula of Mexico. Low-altitude aerial imagery collected from a DJI Mavic Pro drone was used as ground-reference data in the accuracy assessment. Continuous fractional presence of mangroves was detected with 80% accuracy and 85% of mangrove area was found. Future work will use linear spectral unmixing to systematically monitor mangrove extent and health in the region relative to expected growth in tourism development. Numéro de notice : A2020-483 Thématique : IMAGERIE Nature : Article DOI : 10.1080/01490419.2020.1751753 date de publication en ligne : 30/04/2020 En ligne : https://doi.org/10.1080/01490419.2020.1751753 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95636 [article]

Vehicle detection of multi-source remote sensing data using active fine-tuning network / Xin Wu in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

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CNN semantic segmentation to retrieve past land cover out of historical orthoimages and DSM: first experiments / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

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Guided feature matching for multi-epoch historical image blocks pose estimation / Lulin Zhang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)    

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Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping / Alvin B. Baloloy in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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Leveraging photogrammetric mesh models for aerial-ground feature point matching toward integrated 3D reconstruction / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery / Kasper Johansen in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)  

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Accuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing / Desta Ekaso in Geo-spatial Information Science, vol 23 n° 2 (June 2020)  

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Aqueous alteration mapping in Rishabdev ultramafic complex using imaging spectroscopy / Hrishikesh Kumar in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)  

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Digital terrain, surface, and canopy height models from InSAR backscatter-height histograms / Gustavo H.X. Shiroma in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

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A hybrid deep learning–based model for automatic car extraction from high-resolution airborne imagery / Mehdi Khoshboresh Masouleh in Applied geomatics, vol 12 n° 2 (June 2020)  

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