High-resolution satellite imagery (HRSI)  Shadows have played an important role in remote sensing for almost as long as the science has been in existence. From the earliest days of aerial photography, the effects of shadowing have been utilised to highlight ground features in applications such as archaeology and aerial reconnaissance. However, more often than not, shadows are considered a nuisance obscuring important object space detail. Unlike airborne imaging where shadows can be minimised by flying at certain times during the day, low Earth orbit satellitebased sensors are limited to acquiring images at fixed times of the day. If the solar elevation is low at the time, then the presence of shadows will be unavoidable. The principal problem caused by shadows is either a reduction or total loss of information in an image. Reduction of information could potentially lead to the corruption of biophysical parameters derived from pixels values, such as vegetation indices (Leblon et al., 1996) . Total loss of information means that areas of the image cannot be interpreted, and value-added products, such as digital terrain models, cannot be created. The problem of shadowing is particularly significant in high-resolution (approximately 1 m) satellite imaging (HRSI). The equatorial crossing time of 1030 of the Ikonos satellite was chosen to ensure stable imaging conditions (the atmosphere is generally clearer in the morning than later in the day). This, however, means that the solar elevation will never be high, irrespective of latitude and season. The effects of shadowing can be reduced slightly by increasing the look angle of the sensor (basically looking at a region on the ground where the local solar time is slightly later), Figure 1 are casting shadows that are obscuring many other surface features. Even the smaller buildings are casting shadows that are obscuring details on the surrounding streets. It is somewhat ironic that the features that highresolution satellite sensors have been designed to image are those features which are most affected by shadowing. In comparison, large-scale features, which are more appropriately imaged by lower resolution sensors such as regional vegetation patterns, consist of fewer dramatic changes in elevation, and hence, are less likely to be affected by their own shadows. The objective of this paper is to present a methodology for detecting and removing shadows from HRSI of urban areas. However, before this is tackled, the physical principles behind shadowing are investigated, and a literature review of previous shadow analysis research is presented. Methodologies and results are then presented for the two themes of this paper: shadow detection and shadow removal. When considering the shadow cast by a typical high-rise building, there are actually two shadows present: the cast shadow (the one cast on the ground) and the self shadow (the part of the object that is not illuminated, i.e., the façade of the building). It is likely that the cast shadow and the self shadow have different brightness values. The brightness of all the shadows in an image depends on the reflectivity of the object upon which they are cast as well as the illumination from secondary light sources. Self shadows usually have a higher brightness than cast shadows since they receive more secondary lighting from surrounding illuminated objects. Although the work presented in this paper does not discriminate between cast shadows and self shadows, almost all of the shadows in the images used in this study are cast shadows due to the sensor look angle at the time of acquisition. With high-resolution satellite remote sensing (i.e., images with a ground resolution of 1 m or better) it cannot be assumed that the light source is a point source at infinity F e b r u a r y 2 0 0 5 169
In 2005, P.M. Dare presented shadow analysis in high-resolution satellite imagery of urban areas REF .
Shadow Analysis in High-Resolution Satellite Imagery of Urban Areas
Photogrammetric Engineering and Remote Sensing
Geography