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An assessment of algorithmic parameters affecting image classification accuracy by random forests / Dee Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 6 (June 2016) . - pp 407 - 417 Titre : An assessment of algorithmic parameters affecting image classification accuracy by random forests Type de document : Article/Communication Auteurs : Dee Shi, Auteur ; Xiaojun Yang, Auteur Année de publication : 2016 Article en page(s) : pp 407 - 417 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] classification [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] impact sur les données [Termes IGN] occupation du sol [Termes IGN] précision de la classification Résumé : (Auteur) Random forests as a promising ensemble learning algorithm have been increasingly used for remote sensor image classification, and are found to perform identical or better than some popular classifiers. With only two algorithmic parameters, they are relatively easier to implement. Existing literature suggests that the performance of random forests is insensitive to changing algorithmic parameters. However, this was largely based on the classifier's accuracy that does not necessarily represent the resulting thematic map accuracy. The current study extends beyond the classifier's accuracy assessment and investigate how the algorithmic parameters could affect the resulting thematic map accuracy by random forests. A set of random forest models with different parameter settings was carefully constructed and then used to classify a satellite image into multiple land cover categories. Both the classifier's accuracy and the map accuracy were assessed. The results reveal that these parameters can affect the map accuracy up to 9 ∼16 percent for some classes, although their impact on the classifier's accuracy was quite limited. A careful parameterization prioritizing thematic map accuracy can help improve the performance of random forests in image classification, especially for spectrally complex land cover classes. These findings can help establish practical guidance on the use of random forests in the remote sensing community. Numéro de notice : A2016-440 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.6.407 En ligne : http://dx.doi.org/10.14358/PERS.82.6.407 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81345 [article]

Correction of atmospheric refraction geolocation error for high resolution optical satellite pushbroom images / Ming Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 6 (June 2016) . - pp 427 - 435 Titre : Correction of atmospheric refraction geolocation error for high resolution optical satellite pushbroom images Type de document : Article/Communication Auteurs : Ming Yan, Auteur ; Chengyi Wang, Auteur ; Jianglin Ma, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 427 - 435 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] correction atmosphérique [Termes IGN] erreur de positionnement [Termes IGN] géoréférencement direct [Termes IGN] image à haute résolution [Termes IGN] image DMC-3 [Termes IGN] image optique [Termes IGN] image satellite [Termes IGN] réfraction atmosphérique Résumé : (Auteur) When an optical remote sensing satellite is imaging the Earth in-orbit, the propagation direction of the Line of Sight (LOS) will be changed because of atmospheric refraction. This will result in a geolocation deviation on the collinear rigorous geometric model for direct georeferencing, pushbroom images. To estimate and correct the atmospheric refraction geolocation error, the LOS vector tracking algorithm is introduced and a weighted mean algorithm is used to simplify the ISO standard atmospheric model into a troposphere and stratosphere, i.e., two layers spherical atmosphere. The simulation result shows the atmospheric refraction will introduce about 2 m and 7.5 m geometric displacement when the spacecraft is off-pointed view at 30 and 45 degree angle, respectively. For a state-of-the-art high resolution satellite, the atmospheric refraction displacement shall be corrected. The method has been practiced in the DMC3/TripleSat Constellation to remove the atmospheric refraction geolocation error without ground control points. Numéro de notice : A2016-441 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.6.427 En ligne : http://dx.doi.org/10.14358/PERS.82.6.427 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81346 [article]

Predicting palustrine wetland probability using random forest machine learning and digital elevation data-derived terrain variables / Aaron E. Maxwell in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 6 (June 2016) . - pp 437 - 447 Titre : Predicting palustrine wetland probability using random forest machine learning and digital elevation data-derived terrain variables Type de document : Article/Communication Auteurs : Aaron E. Maxwell, Auteur ; Thimoty A. Warner, Auteur ; Michael P. Strager, Auteur Année de publication : 2016 Article en page(s) : pp 437 - 447 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données topographiques [Termes IGN] Etats-Unis [Termes IGN] inventaire [Termes IGN] marais salant [Termes IGN] modèle numérique de terrain [Termes IGN] prédiction [Termes IGN] surveillance écologique [Termes IGN] Virgine Occidentale Résumé : (Auteur) The probability of palustrine wetland occurrence in the state of West Virginia, USA, was mapped based on topographic variables and using random forests (RF) machine learning. Models were developed for both selected ecological subregions and the entire state. The models were first trained using pixels randomly selected from the United States National Wetland Inventory (NWI) dataset and were tested using a separate random subset from the NWI and a database of wetlands not found in the NWI provided by the West Virginia Division of Natural Resources (WVDNR). The models produced area under the curve (AUC) values in excess of 0.90, and as high as 0.998. Models developed in one ecological subregion of the state produced significantly different AUC values when applied to other subregions, indicating that the topographical models should be extrapolated to new physiographic regions with caution. Several previously unexplored DEM-derived terrain variables were found to be of value, including distance from water bodies, roughness, and dissection. Non-NWI wetlands were mapped with an AUC value of 0.956, indicating that the probability maps may be useful for finding potential palustrine wetlands not found in the NWI . Numéro de notice : A2016-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.6.437 En ligne : http://dx.doi.org/10.14358/PERS.82.6.437 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81348 [article]