. OSIrIS: a physically based simulation tool to improve training in thermal infrared remote sensing over urban areas at high spatial resolution. Remote Sensing of Environment, Elsevier, 2006, 104, pp.238-246. <10.1016/j.rse.2006 a physically based simulation tool to improve training in thermal infrared remote sensing over urban areas at high spatial resolution. Remote Sensing of Environment, 104, 238-246, 2006Environment, 104, 238-246, , doi:10.1016Environment, 104, 238-246, /j.rse.2006
AbstractThis paper describes an infrared image simulator for remote sensing applications, called OSIrIS Examples are given that illustrate specific aspects of infrared images.
This article deals with the processing of already classified satellite images according to land use in order t o remove ambiguities, i.e. mistakes in labels.Those images have already been classified with the maximum likehood method but some classes are not correctly determined. For the elimination of ambiguities in this kind of class, we applied our method of determination of land use mixture in pixels. We first briefly review our method of determination of land use mixture. Then we explain how we deal with ambiguities in labels of the maximum likehood classification. We finish with three examples of satellite images that have not correctly been classified. The first one is the vineyard case. Another example for naked soil and urban zone. The last one is a forestry survey application, the determination of the planted pines density.
A knowledge-based system for the computation of land cover mixing and the classification of multi-spectral satellite imageryAbstract. This paper describes the use of a knowledge-based system to manage a library of image processing programs which performs the computation of land cover mixing in pixels of a multi-spectral satellite image. This system has been developed to help naturalists, such as geologists, pedologists, or foresters who are not specialists in computer vision. It automatically processes the data coming from satellites. The role of such a system is to convert the requirements of a user, expressed in terms of user requests, into the correct image processingcommands. Consequently, the users can concentrate their attention on the interpretation of the results and not on the data management. Finally, some examples are shown.
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