It determines how finely a system can represent or distinguish differences of intensity, and is usually expressed as a number of levels or a number of bits, for example 8 bits or from(0-255) levels that is typical of computer image files. The higher the radiometric resolution, the better subtle differences of intensity of reflectivity can be represented, at least in theory. In practice, the effective radiometric resolution is typically limited by the noise level, rather than by the number of bits of representation.
Published in Chapter:
New Neural Buildings Stereo Matching Method Applied to Very High Resolution Ikonos Images
E. Zigh (National Institute of Telecommunications and Information Technologies and Communications of Oran, Algeria)
Copyright: © 2015
|Pages: 29
DOI: 10.4018/978-1-4666-7258-1.ch010
Abstract
The author introduces a new neural stereo matching method using very high resolution IKONOS images. They do not have the parameters of the images acquisition system or other technological resources like digital elevation model, Lidar, or Laser data. These images contain dense urban scenes including various kinds of roads, cars, vegetation, and builds. The author is interested by buildings; they have different shapes, positions, and intensity levels or colours, so they make a lot of “false matches.” To solve this issue, the authors extracts regions of buildings at first; after that, she proposes a neural stereo matching method. A neural field is chosen due to its good management of imprecision and uncertainty relatives to real problems in general and to this one in particular. To show the effectiveness of a proposed method, the chapter contains at first details about encountered problems, and secondly, it explains the stereo matching process, its different kinds, and a chosen approach; thirdly, it gives obtained results using panchromatic and colour images.