Projekte

We introduce an approach for the detection of approximately rectangular structures in gray scale images. Our research is motivated by the Silvretta Historica project that aims at automated detection of remains of livestock enclosures in remotely sensed images of alpine regions. The approach allows detection of enclosures with linear sides of various sizes and proportions. It is robust to incomplete or fragmented rectangles and tolerates deviations from a perfect rectangular shape. Morphological operators are used to extract linear features. They are grouped into parameterized linear segments by means of a local Hough transform. To identify appropriate configurations of linear segments we define convexity and angle constraints. Configurations meeting these constraints are rated by a proposed rectangularity measure that discards overly fragmented configurations and configurations with more than one side completely missing. The search for appropriate configurations is efficiently performed on a graph. Its nodes represent linear segments and edges encode the above constraints. We tested our approach on a set of aerial and GeoEye-1 satellite images of 0.5m resolution that contain ruined livestock enclosures of approximately rectangular shape. The approach showed encouraging results in finding configurations of linear segments originating from the objects of our interest.

In this paper we report on a new archaeological project in the central Alps, titled Silvretta Historica, in which satellite-assisted archaeological surveying is intended to play a key role. Following an overview of the Silvretta study area on the Swiss-Austrian border and its archaeology, we present the first results of recent archaeological fieldwork focussing on the remains of prehistoric alpine pasture economy or Alpwirtschaft. We then continue to discuss why spectral and geometric analysis of high-resolution satellite images are considered a promising method to assist archaeological survey in a high alpine environment.

Since 2007, the Silvretta Archaeological Project in the high Alps on the Swiss-Austrian border has been investigating the prehistoric origins of alpine pasture economy. In an area of about 540 km2 more than 20 well-preserved archaeological sites associated with alpine pastoralism have been recorded, the earliest of them dating to the Iron Age (Reitmaier (ed.), 2012; Walser and Lambers, 2012). All of the ruined huts, cellars and livestock enclosures at these sites are visible on the surface and show a limited range of shapes and proportions. According to their function, all of them are located in open grassland. Based on this sample, we are currently developing methods to detect archaeological objects of the kind described above in high-resolution satellite images of our study area (Lambers and Zingman, in press). These methods are intended to assist archaeological survey in vast and/or difficult to access areas by screening large amounts of remotely sensed images in order to detect possible archaeological sites prior to fieldwork (Cowley, 2012). Our general approach aims at assessing the probability of the presence of objects of our interest based on geometric cues that can be automatically detected in the satellite and aerial images that we use. We here describe our general methodology and the first integral step constituting a new approach to texture segmentation.

We report on recent research undertaken in the framework of the Silvretta Archaeological Project, in which we are developing methods to detect certain types of archaeological ruins in remotely sensed images in order to assist archaeological survey. Our approach aims at assessing the probability of the presence of objects of our interest based on geometric cues that can be automatically detected in the satellite and aerial images that we use. We describe our methodology and the first integral step, constituting a new approach to texture segmentation that we developed to reduce the rate of false detections.

Recently, we introduced a morphological texture contrast (MTC) operator that allows detection of textural and non-texture regions in images. In this paper we provide comparison of the MTC with other available techniques. We show that, in contrast to other approaches, the MTC discriminates between texture details and isolated features, and does not extend borders of texture regions. Using the ideas underlying the MTC operator, we develop a complementary operator called morphological feature contrast (MFC) that allows extraction of isolated Features while not being confused by texture details. We illustrate an application of the MFC operator for extraction of isolated objects such as individual trees or buildings that should be distinguished from forests or urban centers. We furthermore provide an example of how this operator can be used for detection of isolated linear structures. We also derive an extended version of the MFC that works with vector-valued images.

We develop a transformation based on morphological filters that measures the contrast of image texture. This transformation is proportional to texture contrast, but insensitive to its specific type. Though the transformation provides a high response in textured areas, it suppresses individual high contrast features that stand apart from textured areas. It can serve as an effective texture descriptor for unsupervised or supervised segmentation of textured regions, provides high accuracy of localization and does not involve heavy computations. The method is robust to variations of illumination and works on different types of images without needing to be tuned. The only parameter is a scale related parameter. We illustrate the use of the proposed method on satellite and aerial images.