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Automatic determination of the optimum generic sensor model based on genetic algorithm concepts / F. Samadzadegan in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 3 (March 2005)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 71 n° 3 (March 2005) . - pp 277 - 288 Titre : Automatic determination of the optimum generic sensor model based on genetic algorithm concepts Type de document : Article/Communication Auteurs : F. Samadzadegan, Auteur ; A. Azizi, Auteur ; A. Abootalebi, Auteur Année de publication : 2005 Article en page(s) : pp 277 - 288 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] algorithme génétique [Termes IGN] capteur optique [Termes IGN] espace image [Termes IGN] espace objet [Termes IGN] image optique [Termes IGN] modèle géométrique de prise de vue [Termes IGN] modèle mathématique Résumé : (Auteur) Generic sensor models (GSMs) are comprehensive mathematical models by which different geometric structures of satellite images could be modeled in order to establish the connection between image and object spaces. Nevertheless, as they are mathematical models, rather than physical models, it is difficult to determine which term and order of GSMs can provide the best result. Therefore, conventional solutions need an expert operator to try different terms and orders for the best solution of GSMs or to find the best trade-off, which is a complex and time consuming process. Moreover, conventional solutions for automatic determination of the optimum GSM parameters are not practically efficient and instead of going towards the global optimum, frequently get trapped in some local optima. In this paper, we propose a novel methodology which automatically determines the optimum GSM's terms and orders based on genetic algorithm concepts. Extensive evaluations carried out on a wide range of different optical satellite images demonstrate the high potentials of the proposed strategy. Numéro de notice : A2005-104 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.71.3.277 En ligne : https://doi.org/10.14358/PERS.71.3.277 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27242 [article]

Comparison of three algorithms for filtering airborne lidar data / K. Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 3 (March 2005)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 71 n° 3 (March 2005) . - pp 313 - 324 Titre : Comparison of three algorithms for filtering airborne lidar data Type de document : Article/Communication Auteurs : K. Zhang, Auteur ; D. Whitman, Auteur Année de publication : 2005 Article en page(s) : pp 313 - 324 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme de filtrage [Termes IGN] analyse comparative [Termes IGN] données lidar [Termes IGN] filtrage numérique d'image [Termes IGN] pente Résumé : (Auteur) This paper compares three methods for removing non-ground measurements from airborne laser scanning data. These methods, including the elevation threshold with expanding window (ETEW), maximum local slope (MLS), and progressive morphological (PM) filters, analyze data points based on variations of local slope, and elevation. Low and high-relief data sets with various densities of trees, houses, and sand dunes were selected to test the filtering methods. The results show that all three methods can effectively remove most non-ground points in both low-relief urban and high-relief forested areas. The PM filter generated the best result in coastal barrier island areas, whereas the other algorithms tended to remove the tops of steep sand dunes. Each method experienced various omission or commission errors, depending on the filtering parameters. Topographic slope is the most sensitive parameter for the three filtering methods. Numéro de notice : A2005-105 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.71.3.313 En ligne : https://doi.org/10.14358/PERS.71.3.313 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27243 [article]

Semi-automatic registration of multi-source satellite imagery with varying geometric resolutions / A. Habib in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 3 (March 2005)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 71 n° 3 (March 2005) . - pp 325 - 332 Titre : Semi-automatic registration of multi-source satellite imagery with varying geometric resolutions Type de document : Article/Communication Auteurs : A. Habib, Auteur ; R. Al-Ruzouq, Auteur Année de publication : 2005 Article en page(s) : pp 325 - 332 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] données multisources [Termes IGN] image multicapteur [Termes IGN] limite de résolution géométrique [Termes IGN] primitive géométrique [Termes IGN] résolution multiple [Termes IGN] superposition d'images Résumé : (Auteur) Image registration is concerned with the problem of how to combine data and/or information from multiple sensors in order to achieve improved accuracies and better inference about the environment than could be attained through the use of a single sensor. Registration of imagery and information from multiple sources is essential for a variety of applications in remote sensing, medical diagnosis, computer vision, and pattern recognition. In general, an image registration methodology must deal with four issues. First, a decision has to be made regarding the choice of primitives for the registration procedure. The second issue is concerned with establishing the registration transformation function that mathematically relates geometric attributes of corresponding primitives. Then, a similarity measure should be devised to ensure the correspondence of conjugate primitives. Finally, a matching strategy has to be designed and implemented as a controlling framework that utilizes the primitives, the similarity measure, and the transformation function to solve the registration problem. This paper outlines a comprehensive investigation and implementation of the involved issues in a semi-automatic registration procedure capable of handling multi-source satellite imagery with varying geometric resolutions. Numéro de notice : A2005-106 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.71.3.325 En ligne : https://doi.org/10.14358/PERS.71.3.325 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27244 [article]

Nested hyper-rectangle learning model for remote sensing: land-cover classification / L. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 3 (March 2005)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 71 n° 3 (March 2005) . - pp 333 - 340 Titre : Nested hyper-rectangle learning model for remote sensing: land-cover classification Type de document : Article/Communication Auteurs : L. Chen, Auteur Année de publication : 2005 Article en page(s) : pp 333 - 340 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage dirigé [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal [Termes IGN] image SPOT-HRV [Termes IGN] occupation du sol [Termes IGN] Taïwan Résumé : (Auteur) This study presents an exemplar-based nested hyper-rectangle learning model (NHLM) which is an efficient and accurate supervised classification model. The proposed model is based on the concept of seeding training data in the Euclidean m-space (where m denotes the number of features) as hyper-rectangles. To express the exceptions, these hyper-rectangles may be nested inside one another to an arbitrary depth. The fast and one-shot learning procedures can adjust weights dynamically when new examples are added. Furthermore, the "second chance" heuristic is introduced in NHLM to avoid creating more memory objects than necessary. NHLM is applied to solving the land cover classification problem in Taiwan using remote sensed imagery. The study investigated five land cover classes and clouds. These six classes were chosen from field investigation of the study area according to previous study. Therefore, this paper aims to produce a land cover classification based on SPOT HRV spectral data. Compared with a standard back-propagation neural network (BPN), the experimental results indicate that NHLM provides a powerful tool for categorizing remote sensing data. Numéro de notice : A2005-107 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.71.3.333 En ligne : https://doi.org/10.14358/PERS.71.3.333 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27245 [article]