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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]

Urban land use/land cover discrimination using image-based reflectance calibration methods for hyperspectral data / Shailesh S. Deshpande 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 365 - 376 Titre : Urban land use/land cover discrimination using image-based reflectance calibration methods for hyperspectral data Type de document : Article/Communication Auteurs : Shailesh S. Deshpande, Auteur ; Arun B. Inamdar, Auteur ; Harrick M. Vin, Auteur Année de publication : 2017 Article en page(s) : pp 365 - 376 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse discriminante [Termes IGN] étalonnage de capteur (imagerie) [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] Inde [Termes IGN] occupation du sol [Termes IGN] réflectance végétale [Termes IGN] surface imperméable [Termes IGN] surveillance de l'urbanisation [Termes IGN] utilisation du sol [Termes IGN] zone urbaine Résumé : (auteur) Irrespective of substantial research in land use/land cover (LULC) monitoring of urban area, hyperspectral data is not yet exploited effectively because of lack of local spectral resources and a practical reflectance calibration method. The objective of this research is to develop an effective methodology for urban LULC classification using image-based reflectance calibration methods: especially Vegetation-Impervious-Soil classes (VIS), using hyperspectral data. We used EO-1 Hyperion image of Pune City, India and assessed the suitability of different land covers as reflectance calibration surfaces. Furthermore, we performed LULC classification using different reflectance calibration methods such as Internal Area Relative Reflectance, Flat Field Relative Reflectance, and 6S for comparative analysis. Urban VIS signatures extracted from Hyperion image show distinct spectral curves at broader level. Flat Field Relative Reflectance method provides above 90 percent average overall accuracy. An advanced physics-based method such as 6S does not provide any added advantage over image-based calibration methods. Numéro de notice : A2017-191 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.5.365 En ligne : https://doi.org/10.14358/PERS.83.5.365 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84801 [article]