In this paper, a technique for the automatic extraction of houses based on data from the airborne sensor HRSC-A is presented. Due to uncertainties within the given data sources (multispectral, 3D, and panchromatic information), a fuzzy approach is applied that is divided into two main processing parts and a third post-processing part. Within the first part, each information source is processed separately to achieve higher level information products for the following fusion process. A multispectral classification with a fuzzy measure extracts the posibility of each pixel to belong to an urban class. Since the necessary type of 3D information is the actual height of objects above the ground level, this information including the possibilities for different height levels are computed from the digital surface model. A watershed segmentation is used to identify homogeneous regions within the panchromatic band. The resulting segments represent the basic units for further analysis steps. Within the second part, height and spectral information of each segment are combined and improved by fuzzy rules. Uncertainties within the height information are reduced by spectral context knowledge while many spectral ambiguities are solved using reliable height information. Finally, possible house segments are extracted based upon the improved class possibilities.
The subject of this paper is the extraction of houses in very high resolution satellite data. For this purpose, existing segmentation techniques are analyzed as a tool for house detection in IKONOS data. Additionally, a new combination of region and edge based segmentation as well as classification techniques is presented that uses an optimum of the inherent information of the given image data to achieve best possible detection results. Besides spectral and gray value features of the multispectral and panchromatic bands, context and shape information is extracted and incorporated within the classification process.
We have reconstructed the color spaces of normal observers and of protanomalous and deuteranomalous observers by measuring the time that each requires to decide whether pairs of colors are the same or different. By means of a multidimensional scaling procedure these response times were ordered into a space such that colors that were more quickly discriminated were farther from one another. When presented with a set of colors that yields an approximately rectangular color space for normal observers, anomalous observers have difficulty in discriminating colors that lie on lines parallel to the red-green cardinal axis. Their color spaces suggest that the gamut of color that anomalous observers experience is far more impoverished than is usually thought to be the case, and that their ability to interpret color-coded displays is correspondingly limited.