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Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning / Maryam Pourshamsi in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 172 (February 2021) . - pp 79 - 94 Titre : Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning Type de document : Article/Communication Auteurs : Maryam Pourshamsi, Auteur ; Junshi Xia, Auteur ; Naoto Yokoya, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 79 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bande L [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] Gabon [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] Rotation Forest classification [Termes descripteurs IGN] semis de points Résumé : (auteur) Forest height is an important forest biophysical parameter which is used to derive important information about forest ecosystems, such as forest above ground biomass. In this paper, the potential of combining Polarimetric Synthetic Aperture Radar (PolSAR) variables with LiDAR measurements for forest height estimation is investigated. This will be conducted using different machine learning algorithms including Random Forest (RFs), Rotation Forest (RoFs), Canonical Correlation Forest (CCFs) and Support Vector Machine (SVMs). Various PolSAR parameters are required as input variables to ensure a successful height retrieval across different forest heights ranges. The algorithms are trained with 5000 LiDAR samples (less than 1% of the full scene) and different polarimetric variables. To examine the dependency of the algorithm on input training samples, three different subsets are identified which each includes different features: subset 1 is quiet diverse and includes non-vegetated region, short/sparse vegetation (0–20 m), vegetation with mid-range height (20–40 m) to tall/dense ones (40–60 m); subset 2 covers mostly the dense vegetated area with height ranges 40–60 m; and subset 3 mostly covers the non-vegetated to short/sparse vegetation (0–20 m) .The trained algorithms were used to estimate the height for the areas outside the identified subset. The results were validated with independent samples of LiDAR-derived height showing high accuracy (with the average R2 = 0.70 and RMSE = 10 m between all the algorithms and different training samples). The results confirm that it is possible to estimate forest canopy height using PolSAR parameters together with a small coverage of LiDAR height as training data. Numéro de notice : A2021-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.008 date de publication en ligne : 19/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.008 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96846 [article]

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Integrating multilayer perceptron neural nets with hybrid ensemble classifiers for deforestation probability assessment in Eastern India / Sunil Saha in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 12 n° 1 (2021) . - pp 29 - 62 Titre : Integrating multilayer perceptron neural nets with hybrid ensemble classifiers for deforestation probability assessment in Eastern India Type de document : Article/Communication Auteurs : Sunil Saha, Auteur ; Gopal Chandra, Auteur ; Biswajeet Pradhan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 29 - 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bagging [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] classification hybride [Termes descripteurs IGN] classification par Perceptron multicouche [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] Inde [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] Rotation Forest classification [Termes descripteurs IGN] système d'information géographique Résumé : (auteur) The rapid expansion of human settlement, agricultural land and roads because of population growth in several regions of the world has contributed to the depletion of forest land. In this study, novel ensemble intelligent approaches using bagging, dagging and rotation forest (RTF) as meta classifiers of multilayer perceptron (MLP) were used to predict spatial deforestation probability (DP) in Gumani Basin, India. The success rate and correctness of prediction of the ensemble models were compared with MLP. A total of 1000 deforested pixels and 14 deforestation determining factors (DDFs) were used. The ensemble models were trained using 70% of the deforested pixels and validated with the remaining 30%. DDFs were chosen by applying the information gain ratio and Relief-F test methods. Distance to settlement, population growth and distance to roads were the most important factors. The results of DP modelling demonstrated that nearly 16.82%–12.64% of the basin had very high DP. All four models created DP maps with reasonable prediction accuracy and goodness of fit, but the best map was produced by MLP-bagging. The accuracy of the MLP neural net model was increased 2-3% after ensemble with the hybrid meta classifiers (RTF, bagging and dagging). The proposed method could be used for deforestation prediction in other areas having similar geo-environmental conditions. Furthermore, the findings might be used as a basis for future research and could help planners in forest management. Numéro de notice : A2021-106 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475705.2020.1860139 date de publication en ligne : 22/12/2020 En ligne : https://doi.org/10.1080/19475705.2020.1860139 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96903 [article]

Comparison of tree-based classification algorithms in mapping burned forest areas / Dilek Kucuk Matci in Geodetski vestnik, vol 64 n° 3 (September - November 2020)  

Public [article]  inGeodetski vestnik > vol 64 n° 3 (September - November 2020) . - 13 p. Titre : Comparison of tree-based classification algorithms in mapping burned forest areas Type de document : Article/Communication Auteurs : Dilek Kucuk Matci, Auteur ; Resul Comert, Auteur ; Ugur Avdan, Auteur Année de publication : 2020 Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] bassin méditerranéen [Termes descripteurs IGN] carte forestière [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par arbre de décision [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] incendie de forêt [Termes descripteurs IGN] matrice de confusion [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] Rotation Forest classification [Termes descripteurs IGN] Turquie [Termes descripteurs IGN] zone sinistrée Résumé : (auteur) In this study, we compared the performance of tree-based classification algorithms – Random Forest (RF), Rotation Forest (RotF), J48, The Alternating Decision Tree (ADTree), Forest by Penalising Attributes (Forest PA), Logical Analysis of Data Algorithm (LADTree) and Functional Trees (FT) – for mapping burned forest areas within the Mediterranean region in Turkey. Object-based image analysis (OBIA) was performed to pan-sharpened the Landsat 8 images. Four different burned areas, namely Kumluca, Adrasan, Anamur, and Alanya, were used as study areas. Kumluca, Anamur, and Alanya regions were used as training areas, and Adrasan region was used as the test area. Obtained results were evaluated with confusion matrix and statistically significant analysis. According to the results, FT and RotF produced more accurate results than other algorithms. Also, the results obtained with these algorithms are statistically significant. Numéro de notice : A2020-626 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2020.03.348-360 date de publication en ligne : 23/08/2020 En ligne : https://doi.org/10.15292/geodetski-vestnik.2020.03.348-360 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96087 [article]

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Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods / Liheng Peng in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n° 3 (15 - 22 janvier 2020) . - pp 813 - 838 Titre : Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods Type de document : Article/Communication Auteurs : Liheng Peng, Auteur ; Kai Liu, Auteur ; Jingjing Cao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 813 - 838 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] boosting adapté [Termes descripteurs IGN] Chine, mer de [Termes descripteurs IGN] classification et arbre de régression [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] île [Termes descripteurs IGN] image Gaofen [Termes descripteurs IGN] image RapidEye [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] Rotation Forest classification Résumé : (auteur) Mangrove forests are important constitutions for sustainable development of coastal ecosystems, and they are often mapped and monitored with remote sensing approaches. Satellite images allow detailed studies of the distribution and composition of mangrove forests, and therefore facilitate the management and conservation of the ecosystems. The combination of multiple types of satellite images with different spatial and spectral resolutions is helpful in mangrove forests extraction and mangrove species discrimination as it reduces sampling workload and increases classification accuracies. In this study, the 1.0-m-resolution Gaofen-2 (GF-2) and the 5.0-m-resolution RapidEye-4 (RE-4) satellite images, acquired in February 2017 and November 2016 respectively, were used with ensemble machine-learning and object-oriented methods for mangroves mapping at both the community and species levels of the Qi’ao Island, Zhuhai, China. First, the mangroves on the island were segmented from the GF-2 image on a large scale, and then they were extracted combining with their digital elevation model (DEM) data. Second, the GF-2 image was further processed on a fine scale, in which object-oriented features from both the GF-2 and RE-4 images were extracted for each mangrove species. Third, it is followed by the mangrove species classification process which involves three ensemble machine-learning methods: the adaptive boosting (AdaBoost), the random forest (RF) and the rotation forest (RoF). These three methods employed a classification and regression tree (CART) as the base classifier. The results show that the overall accuracy (OA) of mangrove area extraction on the Qi’ao Island with the auxiliary data, DEM, achieves 98.76% (Kappa coefficient (κ) = 0.9289). The features extracted by the GF-2 and RE-4 images were shown to be beneficial for mangrove species discrimination. A maximum improvement in the OA of approximately 8% and a κκ of approximately 0.10 were achieved when employing RoF (OA = 92.01%, κ = 0.9016). Ensemble-learning methods can significantly improve the classification accuracy of CART, and the use of a bagging scheme (RF and RoF) is shown as a better way to map mangrove species than adaptive boosting (AdaBoost). In addition, RoF performed well in mangrove species classification but it was not as robust as the RF, whose average OA and κκ were 80.59% and 0.7608, respectively, while the RoF’s were 77.45% and 0.7214, respectively, in the 10-fold cross-validation. Numéro de notice : A2020-212 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01431161.2019.1648907 date de publication en ligne : 30/07/2019 En ligne : https://doi.org/10.1080/01431161.2019.1648907 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94897 [article]

Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery / H. Tombul in Journal of geodetic science, vol 10 n° 1 (January 2020)  

Public [article]  inJournal of geodetic science > vol 10 n° 1 (January 2020) . - pp 14 - 22 Titre : Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery Type de document : Article/Communication Auteurs : H. Tombul, Auteur ; Ismail Colkesen, Auteur ; Taskin Kavzoglu, Auteur Année de publication : 2020 Article en page(s) : pp 14 - 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme d'apprentissage [Termes descripteurs IGN] analyse canonique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] boosting adapté [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] carte thématique [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] Populus (genre) [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] Rotation Forest classification [Termes descripteurs IGN] segmentation multi-échelle [Termes descripteurs IGN] Turquie Résumé : (auteur) The poplar species in the forest ecosystems are one of the most valuable and beneficial species for the society and environment. Conventional methods require high cost, time and labor need, and the results obtained vary and are insu˚cient in terms of achieved accuracy level. Determination of poplar cultivated fields and mapping of their spatial sites play a vital role for decision-makers and planners to enhance the economic and ecological value of poplar trees. The study aims to map Poplar (P. deltoides) cultivated areas in Akyazi district of Sakarya, Turkey province using various combinations of the Sentinel-2A image bands. For this purpose, object-based classification based on multi-resolution segmentation algorithm was utilized to produce image objects and ensemble learning algorithms, namely, Adaboost (AdaB), Random Forest (RF), Rotation Forest (RotFor) and Canonical correlation forest (CCF) were applied to produce thematic maps. In order to analyze the effects of the spectral bands of the Sentinel-2A image on the object-based classification performance, three datasets consisting of different spectral band combinations (i.e. four 10 m bands, six 20 m bands and ten 10m pan-sharpened bands) were used. The results showed that the RotFor and CCF classifiers produced superior classification performances compared to the AdaB and RF classifiers for the band combinations regarded in this study. Moreover, it was found that determination of poplar tree class level accuracy reached to ~94% in terms of F-score. It was also observed that the inclusion of the six spectral bands at 20 m resolution resulted in a noteworthy increase in classification accuracy (up to 6%) compared to single 10m band combination. Numéro de notice : A2020-420 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jogs-2020-0003 date de publication en ligne : 04/05/2020 En ligne : https://doi.org/10.1515/jogs-2020-0003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95477 [article]

Hyperspectral image classification with canonical correlation forests / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)  

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Rotation-and-scale-invariant airplane detection in high-resolution satellite images based on deep-Hough-forests / Yongtao Yu in ISPRS Journal of photogrammetry and remote sensing, vol 112 (February 2016)  

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Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features / Peijun Du in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)  

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Spectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)

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