Change detection in urban areas by investigating image data of remote sensing satellites is an important topic. Of special interest is, for example, the detection of changes in terms of monitoring and disaster management, where accurate information about dimension and category of changes are frequently requested. Hence, in this paper, a workflow for object-oriented multispectral classification is presented to differentiate between traffic infrastructure, water, vegetation and non-vegetation areas. Changes are detected by analyzing multi-temporal classification results. For this, multitemporal QuickBird images covering the city Karlsruhe and LiDAR data are investigated to detect urban change areas.
Nowadays, climatic and socio-economic conditions require a change in thinking in the field of state forest management.
A high demand for up to date and precise forest information is given - especially in regard to increasing forest damages
by natural hazards. The increasing availability of high-resolution and shortly-revisiting satellite systems (e.g., TerraSAR-X,
Cosmo-SkyMed, RapidEye) allows to support such monitoring tasks. A TerraSAR-X image pair was analyzed
focusing on the image analysis of forest areas. There, the advantage of the higher geometric resolution and the
independance to sun-illumination of the SAR imagery compared to electro-optical image data was taken. The study in
this paper deals with the extraction of tree and forest heights as structural parameters.
The new generation of space borne SAR sensors provides geometric resolution of one meter, airborne systems even
higher. In this high resolution data many features of urban objects become visible, which were beyond the scope of radar
remote sensing only a few years ago. Focusing on elevated objects (e.g., urban area), layover, and occlusion issues
inevitably arise because of the side-looking SAR sensor principle. In order to support interpretation, SAR data are often
analyzed using additional information provided by maps or other remote sensing imagery. The focus of this paper is on
building extraction in urban scenes by means of combined InSAR data and optical aerial imagery.
The main advantages of SAR (Synthetic Aperture Radar) are the availability of data under nearly all weather conditions
and its independence from natural illumination. Data can be gathered on demand and exploited to extract the needed
information. However, due to the side looking imaging geometry, SAR images are difficult to interpret and there is a
need for support of human interpreters by image analysis algorithms. In this paper a method is described to improve and
to simplify the interpretation of high resolution repeat pass SAR images. Modern spaceborne SAR sensors provide imagery
with high spatial resolution and the same imaging geometry in an equidistant time interval. These repeat pass orbits
are e. g. used for interferometric evaluation. The information contained in a repeat pass image pair is visualized by
the introduced method so that some basic features can be directly extracted from a color representation of three deduced
features. The CoV (Coefficient of Variation), the amplitude and the coherence are calculated and jointly evaluated. The
combined evaluation of these features can be used to identify regions dominated by volume scatterers (e. g. leafed vegetation),
rough surfaces (e. g. grass, gravel) and smooth surfaces (e. g. streets, parking lots). Additionally the coherence
between the two images includes information about changes between the acquisitions. The potential of the CovAmCoh-
Analysis is demonstrated and discussed by the evaluation of a TerraSAR-X image pair of the Frankfurt airport. The method
shows a simple way to improve the intuitive interpretation by the human interpreter and it is used to improve the
classification of some basic urban features.
SAR is a remote sensing technique capable to deliver actual data at any time and under bad weather conditions. Before
launch of TerraSAR-X, RADARSAT-2, or COSMO-SkyMed, the rather coarse resolution of operational SAR satellite
systems allowed an analysis of spaceborne SAR data in case of disaster management only for medium scale products.
The new generation of spaceborne SAR satellites permits a more detailed analysis at the object level even for urban
areas, which was before restricted to airborne SAR sensors. Change detection in SAR images is an important field of
research. In general, the appearance of objects in SAR images strongly depends on the viewing angle and look direction.
This makes a comparison of images on a pixel level difficult. The changeover from pixel- to object level leads to the
possibility, to look for object-features that are more stable concerning different imaging constellations. Bridges are keyelements
of man made infrastructure. In this paper the appearance of bridges in SAR data is analyzed and features are
derived that are exploitable for change detection. Here the focus is on analysis at the object level to derive features that
are either stable concerning the imaging constellations or that can be predicted based on a given imaging constellation.
Thereby, the usage of different sensors will be possible to achieve the goal of real time information. The investigations
are supported by simulations, which allow the creation of SAR images for different imaging constellations, bridge
materials, and even for situations with destroyed bridges.
State-of-the-art SAR sensors suggest utilizing InSAR-Data for the analysis of dense urban areas. The appearance of
buildings in SAR or InSAR data is characterized by the effects of the inherent oblique scene illumination, such as
layover, occlusion by radar shadow and multipath signal propagation. Therefore, especially in dense built-up areas
reconstruction quality can be improved by a combined analysis of multi-aspect data.
The presented approach focuses on reconstruction of buildings in residential districts supported by knowledge based
analysis considering the mentioned SAR-specific effects. The algorithm of building extraction starts with the
segmentation of primitives, such as lines and edges, followed by the assembly of building hypotheses based on typical
building features like linearity and right-angularity. The subsequent post-processing of building hypotheses contains the
analysis of InSAR phases to improve footprint or to detect roof-type of buildings. The results are presented by using
optical data and a high resolution LIDAR surface model as ground truth data.
Operational SAR satellite systems such as ENVISAT-ASAR and RADARSAT-1 deliver image data of a rather coarse
resolution, which allows the recognition or feature extraction only for large man-made objects. State of the art airborne
SAR sensors on the other hand provide spatial resolution in the order well below a half meter. In such data many features
of urban objects can be identified and used for recognition. Core elements of man-made infrastructure are bridges. In
case of bridges over water, the oblique side looking imaging geometry of SAR sensors may lead to special signature in a
SAR image depending on the aspect. In this paper, the appearance of bridges over water in SAR data is discussed.
Geometric constraints concerning the changing of this signature are investigated using simulation techniques based on an
adapted ray tracing. Furthermore, an approach is presented to detect bridges over water and to derive object features
from spaceborne and airborne SAR images in the context of disaster management. RADARSAT-1 data with a spatial
resolution of about 9 m as well as high-resolution airborne SAR data of geometric sampling distance better than 40 cm
are investigated.
The improved ground resolution of state-of-the-art synthetic aperture radar (SAR) sensors suggests utilizing this technique for analysis of urban areas. However, building reconstruction from SAR or InSAR data suffers from consequences of the inherent oblique scene illumination, such as foreshortening, layover, occlusion by radar shadow and multipath signal propagation. Especially in built-up areas, building reconstruction is often hardly possible based on single SAR or InSAR data sets alone. An approach is presented to improve the reconstruction quality combining multiaspect InSAR data.
Building object primitives are extracted independently for two directions from the magnitude and phase information of the interferometric data. After projection of these initial primitive objects from slant range into the world coordinate system they are fused. This set of primitive objects is used to generate building hypotheses. SAR illumination effects are discussed using real and simulated data. The simulation results have been compared with real imagery. Deviations between simulations and real data were the base for further investigations. The approach is demonstrated for two InSAR data sets of a building group in an urban environment, which have been taken from orthogonal viewing directions with spatial resolution of about 30 cm.
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