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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.

Interferometric Synthetic Aperture Radar (InSAR) and Differential Interferometric Synthetic Aperture Radar (DInSAR) have shown numerous applications for subsidence monitoring. In the past 10 years, the Persistent Scatterer InSAR (PSI) and Small BAseline Subset (SBAS) approaches were developed to overcome the problem of decorrelation and atmospheric effects, which are common in interferograms. However, DInSAR or PSI applications in rural areas, especially in mountainous regions, can be extremely challenging. In this study we have employed a combined technique, i.e., SBAS-DInSAR, to a mountainous area that is severely affected by mining activities. In addition, L-band (ALOS) and C-band (ENVISAT) data sets, 21 TerraSAR-X images provided by German Aerospace Center (DLR) with a high resolution have been used. In order to evaluate the ability of TerraSAR-X for mining monitoring, we present a case study of TerraSAR-X SAR images for Subsidence Hazard Boundary (SHB) extraction. The resulting data analysis gives an initial evaluation of InSAR applications within a mountainous region where fast movements and big phase OPEN ACCESS Remote Sens. 2014, 6 1477 gradients are common. Moreover, the experiment of four-dimension (4-D) Tomography SAR (TomoSAR) for structure monitoring inside the mining area indicates a potential near all-wave monitoring, which is an extension of conventional InSAR.