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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]

An effective morphological index in automatic recognition of built-up area suitable for high spatial resolution images as ALOS and SPOT data / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 6 (June 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 6 (June 2014) . - pp 529 - 536 Titre : An effective morphological index in automatic recognition of built-up area suitable for high spatial resolution images as ALOS and SPOT data Type de document : Article/Communication Auteurs : Bo Yu, Auteur ; Li Wang, Auteur ; Zheng Niu, Auteur ; Muhammad Shakir, Auteur Année de publication : 2014 Article en page(s) : pp 529 - 536 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] détection du bâti [Termes IGN] image ALOS [Termes IGN] image SPOT [Termes IGN] indice de détection [Termes IGN] morphologie mathématique [Termes IGN] Normalized Difference Vegetation Index Résumé : (Auteur) Building detection from remote sensed images is the main technique to monitor economic or environmental development of an area. Advanced Land Observing Satellite (alos) and SPOT data are reliable sources due to the limitation of weather, position, time, and other practical reasons. However, to the best of our knowledge, algorithms proposed in the identification of buildings mostly aim only at images with very high spatial resolution or high spectral resolution. There are few algorithms for detecting buildings from ALOS and SPOT data. A built-up detection index (BDI) is proposed in this paper to automatically identify buildings from images with 10 meters resolution. It synthesizes morphological theory and normalized differential vegetation index (NDVl) to enhance buildings by suppressing vegetation. Four images of ALOS and SPOT are used to verify the efficiency, stability and accuracy of BDI. Experiments show that BDI is suitable to detect buildings from 10 meters resolution with reliable accuracy. Numéro de notice : A2014-292 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.6.529-536 En ligne : https://doi.org/10.14358/PERS.80.6.529-536 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33195 [article]