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Riparian ecosystems mapping at fine scale: a density approach based on multi-temporal UAV photogrammetric point clouds / Elena Belcore in Remote sensing in ecology and conservation, vol 8 n° 5 (October 2022)
[article]
Titre : Riparian ecosystems mapping at fine scale: a density approach based on multi-temporal UAV photogrammetric point clouds Type de document : Article/Communication Auteurs : Elena Belcore, Auteur ; Melissa Latella, Auteur Année de publication : 2022 Article en page(s) : pp 644 - 655 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] carte de la végétation
[Termes IGN] densité de la végétation
[Termes IGN] détection d'objet
[Termes IGN] forêt ripicole
[Termes IGN] houppier
[Termes IGN] image captée par drone
[Termes IGN] Italie
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] orthophotoplan numérique
[Termes IGN] semis de points
[Termes IGN] structure-from-motionRésumé : (auteur) In recent years, numerous directives worldwide have addressed the conservation and restoration of riparian corridors, activities that rely on continuous vegetation mapping to understand its volumetric features and health status. Mapping riparian corridors requires not only fine-scale resolution but also the coverage of relatively large areas. The use of Unmanned Aerial Vehicles (UAV) allows for meeting both conditions, although the cost-effectiveness of their use is highly influenced by the type of sensor mounted on them. Few works have so far investigated the use of photogrammetric sensors for individual tree crown detection, despite being cheaper than the most common Light Detection and Ranging (LiDAR) ones. This work aims to improve the individual crown detection from UAV-photogrammetric datasets in a two fold way. Firstly, the effectiveness of a new approach that has already achieved interesting results in LiDAR applications was tested for photogrammetric point clouds. The test was carried out by comparing the accuracy achieved by the new approach, which is based on the point density features of the analysed dataset, with those related to the more common local maxima and textural methods. The results indicated the potentiality of the density-based method, which achieved accuracy values (0.76F-score) consistent with the traditional methods (0.49–0.80F-score range) but was less affected by under- and over-fitting. Secondly, the potential improvement of working on intra-annual multi-temporal datasets was assessed by applying the density-based approach to seven different scenarios, three of which were constituted by single-epoch datasets and the remaining given by the joining of the others. The F-score increased from 0.67 to 0.76 when passing from single- to multi-epoch datasets, aligning with the accuracy achieved by the new method when applied to LiDAR data. The results demonstrate the potential of multi-temporal acquisitions when performing individual crown detection from photogrammetric data. Numéro de notice : A2022-879 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1002/rse2.267 Date de publication en ligne : 22/03/2022 En ligne : https://doi.org/10.1002/rse2.267 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102193
in Remote sensing in ecology and conservation > vol 8 n° 5 (October 2022) . - pp 644 - 655[article]Mapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery / Shengyuan Zou in International journal of applied Earth observation and geoinformation, vol 113 (September 2022)
[article]
Titre : Mapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery Type de document : Article/Communication Auteurs : Shengyuan Zou, Auteur ; Le Wang, Auteur Année de publication : 2022 Article en page(s) : n° 103018 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte thématique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection d'objet
[Termes IGN] image à très haute résolution
[Termes IGN] image Streetview
[Termes IGN] logement
[Termes IGN] New York (Etats-Unis ; état)
[Termes IGN] théorie de Dempster-Shafer
[Termes IGN] zone urbaineRésumé : (auteur) Abandoned houses (AH) present an utmost challenge confronting the urban environment in contemporary U.S. shrinking cities. Data accessibility is a major hurdle that prevents the acquisition of large-scale AH information at the individual property level. To this end, the latest revolution of open-access remote sensing platforms has witnessed a plethora of multi-source, multi-perspective fine-spatial-resolution data for urban environments, among which very-high-resolution (VHR) top-down view remote sensing images and horizontal-perspective Google Street View (GSV) images are prominent exemplifiers. In this study, we aim to map individual-level abandoned houses across cities by developing a method that can effectively leverage VHR remote sensing and GSV images. The proposed method is composed of four steps. First, we explored the feasibility of the three most relevant and complementary remote sensing data for individual-level AH detection, i.e., daytime VHR images, nighttime light VHR images, and GSV images. Second, we extracted discriminative features that are indicative of housing abandonment conditions from the three disparate data sources. Third, we applied decision-level fusion with Dempster-Shafer Theory (DST) to better leverage the prior knowledge about data effectiveness. In the last step, a geographical random forests (GRF) model was first implemented to improve the predictions of where houses were occluded on GSV images. We mapped individual AH in two typical U.S. shrinking cities, Buffalo, NY, and Cleveland, OH, which allowed us to further explore the individual-property-level spatial characteristics of AH. Results revealed that the proposed DST fusion and GRF prediction consistently achieved promising performance across the two cities. Given the merits of incorporating open-access and multi-perspective data, our proposed method has the potential to be generalized to understanding regional and national-scale urban environments tackling housing abandonment challenges. Numéro de notice : A2022-788 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103018 Date de publication en ligne : 18/09/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101894
in International journal of applied Earth observation and geoinformation > vol 113 (September 2022) . - n° 103018[article]Structured binary neural networks for image recognition / Bohan Zhuang in International journal of computer vision, vol 130 n° 9 (September 2022)
[article]
Titre : Structured binary neural networks for image recognition Type de document : Article/Communication Auteurs : Bohan Zhuang, Auteur ; Chunhua Shen, Auteur ; Mingkui Tan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2081 - 2102 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] décomposition
[Termes IGN] détection d'objet
[Termes IGN] implémentation (informatique)
[Termes IGN] logique binaire
[Termes IGN] segmentation sémantiqueRésumé : (auteur) In this paper, we propose to train binarized convolutional neural networks (CNNs) that are of significant importance for deploying deep learning to mobile devices with limited power capacity and computing resources. Previous works on quantizing CNNs often seek to approximate the floating-point information of weights and/or activations using a set of discrete values. Such methods, termed value approximation here, typically are built on the same network architecture of the full-precision counterpart. Instead, we take a new “structured approximation” view for network quantization — it is possible and valuable to exploit flexible architecture transformation when learning low-bit networks, which can achieve even better performance than the original networks in some cases. In particular, we propose a “group decomposition” strategy, termed GroupNet, which divides a network into desired groups. Interestingly, with our GroupNet strategy, each full-precision group can be effectively reconstructed by aggregating a set of homogeneous binary branches. We also propose to learn effective connections among groups to improve the representation capability. To improve the model capacity, we propose to dynamically execute sparse binary branches conditioned on input features while preserving the computational cost. More importantly, the proposed GroupNet shows strong flexibility for a few vision tasks. For instance, we extend the GroupNet for accurate semantic segmentation by embedding the rich context into the binary structure. The proposed GroupNet also shows strong performance on object detection. Experiments on image classification, semantic segmentation, and object detection tasks demonstrate the superior performance of the proposed methods over various quantized networks in the literature. Moreover, the speedup and runtime memory cost evaluation comparing with related quantization strategies is analyzed on GPU platforms, which serves as a strong benchmark for further research. Numéro de notice : A2022-637 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-022-01638-0 Date de publication en ligne : 22/06/2022 En ligne : https://doi.org/10.1007/s11263-022-01638-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101443
in International journal of computer vision > vol 130 n° 9 (September 2022) . - pp 2081 - 2102[article]Comparison of PBIA and GEOBIA classification methods in classifying turbidity in reservoirs / Douglas Stefanello Facco in Geocarto international, vol 37 n° 16 ([15/08/2022])
[article]
Titre : Comparison of PBIA and GEOBIA classification methods in classifying turbidity in reservoirs Type de document : Article/Communication Auteurs : Douglas Stefanello Facco, Auteur ; Laurindo Antonio Guasselli, Auteur ; Luis Fernando Chimelo Ruiz, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 4762 - 4783 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse d'image orientée objet
[Termes IGN] bande spectrale
[Termes IGN] Brésil
[Termes IGN] centrale hydroélectrique
[Termes IGN] classification bayesienne
[Termes IGN] classification dirigée
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Landsat-OLI
[Termes IGN] segmentation d'image
[Termes IGN] turbidité des eauxRésumé : (auteur) Our goal is to compare the performance of Classification and Regression Tree, Naive Bayes and Random Forest algorithms, from supervised image classification, and approaches on Pixel-Based Image analysis (PBIA) and Geographic Object-Based Image Analysis (GEOBIA), to classify turbidity in reservoirs. Tod do so, we use Landsat 8 image and bands and spectral indices, as predictive parameters, as well as the classification algorithms based on PBIA and GEOBIA. The Brazilian Itaipu reservoir was adopted, as a case study. Our results show that the RF classifier obtained the highest accuracy in both classification approaches, followed by CART and NB. The KA and OA indices of the GEOBIA classifications were superior to the PBIA classifications in both algorithms. This study contributes with an approach to quickly and accurately delineating turbidity spectral limits in reservoirs. Numéro de notice : A2022-668 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1899302 Date de publication en ligne : 22/06/2021 En ligne : https://doi.org/10.1080/10106049.2021.1899302 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101519
in Geocarto international > vol 37 n° 16 [15/08/2022] . - pp 4762 - 4783[article]Effective CBIR based on hybrid image features and multilevel approach / D. Latha in Multimedia tools and applications, vol 81 n° 20 (August 2022)
[article]
Titre : Effective CBIR based on hybrid image features and multilevel approach Type de document : Article/Communication Auteurs : D. Latha, Auteur ; A. Geetha, Auteur Année de publication : 2022 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] base de données d'images
[Termes IGN] écart type
[Termes IGN] espace colorimétrique
[Termes IGN] image en couleur
[Termes IGN] image RVB
[Termes IGN] matrice de co-occurrence
[Termes IGN] motif binaire local
[Termes IGN] niveau de gris (image)
[Termes IGN] observation multiniveaux
[Termes IGN] recherche d'image basée sur le contenu
[Termes IGN] saturation de la couleur
[Termes IGN] texture d'image
[Termes IGN] transformation intensité-teinte-saturationRésumé : (auteur) Content based image retrieval (CBIR) process can retrieve images by matching its feature set values. The proposed novel CBIR methodology called Effective CBIR based on hybrid image features and multilevel approach (CBIR_LTP_GLCM) integrates the hybrid features such as color features and texture features, along with multilevel approach. The color features such as mean and standard deviation are adopted in the proposed method to represent the global color properties of an image. This method manipulates the color input-image by processing the Hue, Saturation and Value channels of the HSV color space. This novel work is enriched with the image feature derived from Local Ternary Pattern (LTP) in addition with GLCM. So, the proposed method CBIR_LTP_GLCM is potentially charged with meaningful modifications travelling with color image manipulation and extended image retrieval accuracy with the aid of multilevel approach. The proposed methodology is experimentally compared with the existing recent CBIR versions by using the standard database such as Corel-1 k, and a user contributed database named DB_VEG. Numéro de notice : A2022-291 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11042-022-12588-7 Date de publication en ligne : 30/03/2022 En ligne : https://doi.org/10.1007/s11042-022-12588-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100337
in Multimedia tools and applications > vol 81 n° 20 (August 2022) . - pp[article]Tracking annual dynamics of mangrove forests in mangrove National Nature Reserves of China based on time series Sentinel-2 imagery during 2016–2020 / Rong Zhang in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)PermalinkExploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation / Huan Ning in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)PermalinkInvestigating the ability to identify new constructions in urban areas using images from unmanned aerial vehicles, Google Earth, and Sentinel-2 / Fahime Arabi Aliabad in Remote sensing, vol 14 n° 13 (July-1 2022)PermalinkStreet-view imagery guided street furniture inventory from mobile laser scanning point clouds / Yuzhou Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)PermalinkEstimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using and object-based technique / Syaza Rozali in Geocarto international, vol 37 n° 11 ([15/06/2022])PermalinkAdversarial defenses for object detectors based on Gabor convolutional layers / Abdollah Amirkhani in The Visual Computer, vol 38 n° 6 (June 2022)PermalinkDetecting interchanges in road networks using a graph convolutional network approach / Min Yang in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)PermalinkInvariant structure representation for remote sensing object detection based on graph modeling / Zicong Zhu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)PermalinkLarge-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images / Lingdong Mao in Landscape and Urban Planning, vol 222 (June 2022)PermalinkLine-based deep learning method for tree branch detection from digital images / Rodrigo L. S. Silva in International journal of applied Earth observation and geoinformation, vol 110 (June 2022)Permalink