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MSegnet, a practical network for building detection from high spatial resolution images / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 12 (December 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 12 (December 2021) . - pp 901 - 906 Titre : MSegnet, a practical network for building detection from high spatial resolution images Type de document : Article/Communication Auteurs : Bo Yu, Auteur ; Fang Chen, Auteur ; Ying Dong, Auteur Année de publication : 2021 Article en page(s) : pp 901 - 906 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du bâti [Termes IGN] image à haute résolution [Termes IGN] matrice [Termes IGN] segmentation multi-échelle [Termes IGN] segmentation sémantique Résumé : (Auteur) Building detection in big earth data by remote sensing is crucial for urban development. However, improving its accuracy remains challenging due to complicated background objects and different viewing angles from various remotely sensed images. The hereto proposed methods predominantly focus on multi-scale feature learning, which omits features in multiple aspect ratios. Moreover, postprocessing is required to refine the segmentation performance. We propose modified semantic segmentation (MSegnet), a single-shot semantic segmentation model based on a matrix of convolution layers to extract features in multiple scales and aspect ratios. MSegnet consists of two modules: backbone feature learning and matrix convolution to conduct vertical and horizontal learning. The matrix convolution comprises a set of convolution operations with different aspect ratios. MSegnet is applied to a public building data set that is widely used for evaluation and shown to achieve satisfactory accuracy, compared with the published single-shot methods. Numéro de notice : A2021-898 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00016R2 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.14358/PERS.21-00016R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99296 [article]

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A simple but effective landslide detection method based on image saliency / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 5 (May 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 5 (May 2017) . - pp 351 - 363 Titre : A simple but effective landslide detection method based on image saliency Type de document : Article/Communication Auteurs : Bo Yu, Auteur ; Fang Chen, Auteur ; Muhammad Shakir, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 351 - 363 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] détection de changement [Termes IGN] effondrement de terrain [Termes IGN] extraction du relief [Termes IGN] relief [Termes IGN] risque naturel Résumé : (auteur) Effective large-scale landslide mapping is becoming significantly important for analyzing natural hazards and providing landslide locations rapidly for emergency response. Change detection and machine learning methods are commonly used for landslide detection. Change detection mostly relies on several experienced parameters that users have to tune for different images, which limits the practical application. The training machine learning model consumes much time, and it is limited to specific imaging conditions. In this paper, a simple method for landslide detection using a fixed parameter by calculating image saliency is proposed. Landslide is detected as a saliency object within the background of vegetation and bare rocks. It is fast and robust for the experimental images, and outperforms the state-of-the-art, semi-automatic method in terms of accuracy and computing time. Given the high efficiency and robustness of the proposed method, it is applicable to practical cases for hazard estimation. Numéro de notice : A2017-190 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.5.351 En ligne : https://doi.org/10.14358/PERS.83.5.351 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84800 [article]