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A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 97 - 106 Titre : A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery Type de document : Article/Communication Auteurs : Lucas Prado Osco, Auteur ; Mauro Dos Santos de Arruda, Auteur ; José Marcato Junior, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 97 - 106 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] agrume [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] carte de confiance [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] inventaire de la végétation [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] verger Résumé : (Auteur) Visual inspection has been a common practice to determine the number of plants in orchards, which is a labor-intensive and time-consuming task. Deep learning algorithms have demonstrated great potential for counting plants on unmanned aerial vehicle (UAV)-borne sensor imagery. This paper presents a convolutional neural network (CNN) approach to address the challenge of estimating the number of citrus trees in highly dense orchards from UAV multispectral images. The method estimates a dense map with the confidence that a plant occurs in each pixel. A flight was conducted over an orchard of Valencia-orange trees planted in linear fashion, using a multispectral camera with four bands in green, red, red-edge and near-infrared. The approach was assessed considering the individual bands and their combinations. A total of 37,353 trees were adopted in point feature to evaluate the method. A variation of σ (0.5; 1.0 and 1.5) was used to generate different ground truth confidence maps. Different stages (T) were also used to refine the confidence map predicted. To evaluate the robustness of our method, we compared it with two state-of-the-art object detection CNN methods (Faster R-CNN and RetinaNet). The results show better performance with the combination of green, red and near-infrared bands, achieving a Mean Absolute Error (MAE), Mean Square Error (MSE), R2 and Normalized Root-Mean-Squared Error (NRMSE) of 2.28, 9.82, 0.96 and 0.05, respectively. This band combination, when adopting σ = 1 and a stage (T = 8), resulted in an R2, MAE, Precision, Recall and F1 of 0.97, 2.05, 0.95, 0.96 and 0.95, respectively. Our method outperforms significantly object detection methods for counting and geolocation. It was concluded that our CNN approach developed to estimate the number and geolocation of citrus trees in high-density orchards is satisfactory and is an effective strategy to replace the traditional visual inspection method to determine the number of plants in orchards trees. Numéro de notice : A2020-045 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.010 date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94525 [article]

Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya / Vijendra Kumar Pandey in Geocarto international, vol 35 n° 2 ([01/02/2020])  

Public [article]  inGeocarto international > vol 35 n° 2 [01/02/2020] . - pp 168 - 187 Titre : Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya Type de document : Article/Communication Auteurs : Vijendra Kumar Pandey, Auteur ; Hamid Reza Pourghasemi, Auteur ; Milap Chand Sharma, Auteur Année de publication : 2020 Article en page(s) : pp 168 - 187 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] autoroute [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] entropie maximale [Termes descripteurs IGN] Himalaya [Termes descripteurs IGN] image IRS-LISS [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] Linear Imaging Self-Scanning System [Termes descripteurs IGN] modèle statistique [Termes descripteurs IGN] mousson [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] séparateur à vaste marge [Termes descripteurs IGN] test statistique Résumé : (Auteur) The main objective of this study to produce landslide susceptibility zones using maximum entropy (MaxEnt) and support vector machine (SVM) data-driven models along the Tipari to Ghuttu highway corridors in the Garhwal Himalaya. A landslide inventory has been prepared through field surveys and LISS-IV and Landsat 8 satellite images. The datasets of 85 landslides were categorised into training and test sets. In this study 11 landslide conditioning variables were used that are; altitude, slope angle, aspect, plan curvature, topographic wetness index, normalised difference vegetation index (NDVI), land use, soil texture, distance to rivers, distance to faults, and distance to the road. The result produced using MaxEnt and SVM model were subsequently validated using receiver operating characteristics curve (ROC) with test sets of landslide dataset. Both the models have good prediction capabilities. MaxEnt has ROC value of 0.78 while SVM has the highest prediction rate of 0.85. Numéro de notice : A2020-036 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1510038 date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1510038 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94519 [article]

Plant survival monitoring with UAVs and multispectral data in difficult access afforested areas / Maria Luz Gil-Docampo in Geocarto international, vol 35 n° 2 ([01/02/2020])  

Public [article]  inGeocarto international > vol 35 n° 2 [01/02/2020] . - pp 128 - 140 Titre : Plant survival monitoring with UAVs and multispectral data in difficult access afforested areas Type de document : Article/Communication Auteurs : Maria Luz Gil-Docampo, Auteur ; Juan Ortiz-Sanz, Auteur ; S. Martínez-Rodríguez, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 128 - 140 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] aire protégée [Termes descripteurs IGN] analyse de survie [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] climat aride [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] mortalité [Termes descripteurs IGN] reboisement [Termes descripteurs IGN] ressources en eau [Termes descripteurs IGN] surveillance de la végétation [Termes descripteurs IGN] télédétection aérienne Résumé : (Auteur) Water supply devices enable afforestation in dry climates and on poor lands with generally high success rates. Previous survival analyses have been based on the direct observation of each individual plant in the field, which entails considerable effort and costs. This study provides a low-cost method to discriminate between live and dead plants in afforestation that can efficiently replace traditional field inspections through the use of unmanned aerial vehicles (UAVs) equipped with RGB and NIR sensors. The method combines the use of a conventional camera with an identical camera modified to record the NIR channel. Survival analysis was performed with digital image processing techniques based on calculated indices associated with plant vigour and PCA-based decorrelation. The method yielded results with high global accuracy rates (∼96.2%) with a minimum percentage of doubtful plants, even in young plantations (seedlings Numéro de notice : A2020-035 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1508312 date de publication en ligne : 02/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1508312 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94517 [article]

Transferring deep learning models for cloud detection between Landsat-8 and Proba-V / Gonzalo Mateo-García in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 1 - 17 Titre : Transferring deep learning models for cloud detection between Landsat-8 and Proba-V Type de document : Article/Communication Auteurs : Gonzalo Mateo-García, Auteur ; Valero Laparra, Auteur ; Dan López-Puigdollers, Auteur ; Luis Gómez-Chova, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 17 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage par transformation [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] conversion de données [Termes descripteurs IGN] détection des nuages [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image PROBA [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] masque [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] seuillage de points Résumé : (Auteur) Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical properties of the acquired signals and propose a simple transfer learning approach using Landsat-8 and Proba-V sensors, whose images have different but similar spatial and spectral characteristics. Three types of experiments are conducted to demonstrate that transfer learning can work in both directions: (a) from Landsat-8 to Proba-V, where we show that models trained only with Landsat-8 data produce cloud masks 5 points more accurate than the current operational Proba-V cloud masking method, (b) from Proba-V to Landsat-8, where models that use only Proba-V data for training have an accuracy similar to the operational FMask in the publicly available Biome dataset (87.79–89.77% vs 88.48%), and (c) jointly from Proba-V and Landsat-8 to Proba-V, where we demonstrate that using jointly both data sources the accuracy increases 1–10 points when few Proba-V labeled images are available. These results highlight that, taking advantage of existing publicly available cloud masking labeled datasets, we can create accurate deep learning based cloud detection models for new satellites, but without the burden of collecting and labeling a large dataset of images. Numéro de notice : A2020-043 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.024 date de publication en ligne : 10/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.024 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94522 [article]

3D iterative spatiotemporal filtering for classification of multitemporal satellite data sets / Hessah Albanwan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 1 (January 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 1 (January 2020) . - pp 23 - 31 Titre : 3D iterative spatiotemporal filtering for classification of multitemporal satellite data sets Type de document : Article/Communication Auteurs : Hessah Albanwan, Auteur ; Rongjun Qin, Auteur ; Xiaohu Lu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 23 - 31 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] changement d'utilisation du sol [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] données multitemporelles [Termes descripteurs IGN] filtrage spatiotemporel [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] itération [Termes descripteurs IGN] orthoimage Résumé : (Auteur) The current practice in land cover/land use change analysis relies heavily on the individually classified maps of the multi-temporal data set. Due to varying acquisition conditions (e.g., illumination, sensors, seasonal differences), the classification maps yielded are often inconsistent through time for robust statistical analysis. 3D geometric features have been shown to be stable for assessing differences across the temporal data set. Therefore, in this article we investigate the use of a multi-temporal orthophoto and digital surface model derived from satellite data for spatiotemporal classification. Our approach consists of two major steps: generating per-class probability distribution maps using the random-forest classifier with limited training samples, and making spatiotemporal inferences using an iterative 3D spatiotemporal filter operating on per-class probability maps. Our experimental results demonstrate that the proposed methods can consistently improve the individual classification results by 2%–6% and thus can be an important postclassification refinement approach. Numéro de notice : A2020-049 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.1.23 date de publication en ligne : 01/01/2020 En ligne : https://doi.org/10.14358/PERS.86.1.23 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94534 [article]

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