期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B4
页码：397-402
出版社：Copernicus Publications
摘要：Various applications require an optimized (and rapid) display of remote sensing imagery. Multisensor and multiband remote sensing data have to be enhanced for optimal band combination and image contrast. Problems that exist are that contrast stretches are usually optimized for the whole image and might not prove appropriate for selected features. For example, an image that contains land, water and beach classes will be stretched in a way that would produce a compromise for the different classes. Water is usually dark (especially in CIR display), beach will be very bright with little discernible structure (similar for urban classes), and other land classes (e.g. vegetation) will not make use of the full possible range of digital numbers. Also, different features might require different band combinations for optimum display. Selected stretching especially for regions of low contrast is nothing new in the analysis of remotely sensed data. Usually, this is done interactively by the analyst either by selecting a box or digitizing a certain area of interest in the image. This area is then enhanced using standard image processing techniques. The subset is then displayed separately to highlight certain features that would have been impossible to discern in a global enhancement mode. The goal of this study was to develop automated procedures for feature based image enhancement techniques. Feature based enhancement means that different feature classes in the image require different procedures for optimum display. The procedures do not only encompass locally varying enhancement techniques such as histogram equalization or contrast stretch but also the selection of different spectral bands. There are two main sources for this kind of information: (a) storage of a priori knowledge in a GIS, and (b) context based image information that can be extracted through a segmentation process. Both techniques can also be applied for optimum feature class selection. In this paper, we develop a five-step automated procedure for selective image enhancement and compare the results to those achieved by standard methods. The test area is a coastal region in the US which covers land, water and beach areas. Datasets are multispectral IKONOS and Quickbird satellite data. It is shown that the new methods produces superior results
