High-resolution satellite imagery (HRSI) offers great possibilities for urban mapping. Unfortunately, shadows cast by buildings in high-density urban environments obscure much of the information in the image leading to potentially corrupted classification results or blunders in interpretation. Although significant research has been carried out on the subject of shadowing in remote sensing, very few studies have focused on the particular problems associated with high-resolution satellite imaging of urban areas. This paper reviews past and current research and proposes a solution to the problem of automatic detection and removal of shadow features. Tests show that although detection and removal of shadow features can lead to improved image quality, results can be image-dependent.

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An understanding of the evolution of cracks in concrete structures due to long term natural deformation is important to civil engineers, but quantitative measurements can be difficult to make. However, digital imaging offers a potential solution. This short paper illustrates the operational application of automated image processing techniques for accurate, multi-temporal crack measurements. The first part of this paper provides an overview of automatic feature extraction, essential for automatic crack detection. The latter part describes the methods developed for detecting and measuring cracks. Due to the long term nature of the application, operational results have yet to be finalised, although sample results are presented.K: automation, crack monitoring, feature extraction I F  has been the cornerstone of many applications in photogrammetry and remote sensing for several years (Fö rstner, 1993). The inevitable trend towards automation in all things digital has meant that research into automatic feature extraction has received considerable attention for some time now. Although significant progress has been made in many areas of automatic feature extraction (for example, Firestone et al., 1996; Sowmya and Trinder, 2000), transfer of those algorithms from the research community to the commercial domain has been slow.In the photogrammetry and remote sensing research communities, automatic feature extraction is being used for a considerable number of different applications.

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It is an accepted fact that combining data from different sensors can yield more information than the individual sensors will give when used on their own. This is especially true for synthetic aperture radar (SAR) data when combined with optical data. However, if performed manually, the registration of these two data sets can be time consuming and inaccurate. Due to the properties of SAR images, it is generally very difficult to select ground control points to use in the registration process. This paper covers the work being undertaken to develop an automatic system for registering SAR data to optical data using feature matching. In order to complete the registration, corresponding features have to be extracted from both images and matched with each other to generate a number of 'match points' .The parameters of the transformation can then be calculated using these match points. The successfully merged image will then be used to develop accurate change detection algorithms. Perhaps one of the most difficult aspects of this procedure is the extraction of features from SAR images, which are heavily degraded by speckle. Here we present the results of testing various speckle reduction filters and segmentation procedures in order to examine their ability to aid feature extraction. We have also developed a number of new algorithms which reduce speckle and segment SAR images.