The coherent nature of the SAR imaging process opens up the opportunity to create interferometric image pairs, which carry a large amount of information about the scene. In this paper, the interferometric coherence is investigated in detail. Coherence is a measure for the temporal stability of the scene with respect to the phase information. Classically, coherence is used for the task of co-registration of the image pair, with the goal of coherence maximization, since such a co-registration will yield the most reliable interferometric phase information. The second important field of application is coherent change detection, i.e. the detection of changes in the scene that most often do not change the backscattering properties of the images and thus are not detectable in the amplitude images. For such an application it is of importance to maximize the contrast between the incoherent changed parts of the scene and the coherent surroundings. With these two applications in mind, in this paper, several published coherence estimation schemes are investigated. The different coherence estimators are applied to an airborne data set, and results regarding coherence maximization and coherence contrast maximization are shown.
With our study presented at the SPIE conference “Earth Resources and Environmental Remote Sensing/GIS Applications” last year, we discussed a concept for change analysis in sequences of SAR sub-aperture images. The main aspect of this concept is to investigate an adaption of our approach for incoherent time series change analysis on such short-term time series data. In a first step, sub-aperture amplitude images of two maritime scenes were calculated leading to time series stacks being considered as input for our incoherent change detection method. As output, so-called ActivityMaps (AMs) were constructed aiming on a recognition of high activity areas. Focusing on short-term time series, such areas are caused by Ground Moving Targets (GMTs) which denote objects that were in motion during the image acquisition. With respect to the maritime scenes considered in this study, GMTs might be ships, cars, trucks, cranes with moving components, etc. It was observed, that GMTs show different signatures in the AMs, depending for example on their size and their velocity. In the paper at hand, we link to this previous study by discussing different features being reliable for a later categorization of the detected change objects. Moreover, it is investigated, whether High Activity Objects (HAOs) are of solely interest, or, if other change objects have to be included. The relevance of the discussed features to produce categories being clearly distinguishable from each other is tested by an unsupervised clustering procedure. As test data, a TerraSAR-X (TSX) Staring Spotlight (ST) Single Look Complex (SLC) image of Rotterdam (NED) was used.
In the last few years, image acquisition modes have been developed for spaceborne SAR systems aiming on an enhancement of geometric resolution. This offers new opportunities for the calculation of so-called sub-aperture images and their analysis. Due to the increase of illumination time, one single image can be subdivided into multiple subapertures, which are temporarily closely arranged to each other with a lower geometric resolution than the original image. Focusing on this increase of illumination time, the Staring Spotlight (ST) mode of the German TerraSAR-X (TSX) and TanDEM-X (TDX) satellite constellation is mentionable. Here, the azimuth bandwidth can be divided into subset images, in which e.g. vehicles or ships are still well observable. Consequently, moving targets can be detected without utilizing SAR raw data. For this, only a Single Look Complex (SLC) image has to be considered. <p> </p>In this study, different stacks consisting of sub-aperture images are calculated, which are used for incoherent change detection. The detection is performed as an adaption of a method for change analysis in SAR time series data developed earlier. This method comprises the detailed description of changes, concerning their categorization and classification. Therefore, suitable features have to be calculated leading to a clear distinction of different change categories. <p> </p>As test data, TSX SLC images are used which were acquired both in ST and in High Resolution Spotlight (HS) mode. An assessment concerning their suitability for the incoherent change detection method applied to the sub-apertures is given. With future studies, it will be tested, whether the temporal aspect can be meaningfully regarded for the change categorization step. For this, the concept of so-called high activity objects, which were the basis of the earlier developed change analysis scheme, might be of relevance for the analysis of changes in sub-aperture sequences.
The current generation of SAR satellites such as TerraSAR-X, TanDEM-X and COSMO-SkyMed provide resolutions below one meter, permitting the detailed analysis of urban areas while covering large zones. Furthermore, as they are deployable independently of daylight and weather, such remote sensing SAR data are particularly popular for purposes such as rapid damage assessment at building level after a natural disaster.
The purpose of our study is the investigation of techniques for the detection of changes based on one pre-event and one post-event SAR amplitude image. We provide a comparison of several methods for detecting changes in urban areas. Especially, changes at building locations are looked for. We analyzed two areas affected differently in detail. First, a suburban area of Paris, France, was considered due to changes caused by an urbanization project. Here, we have two TanDEM-X acquisitions available, before (November 4, 2012) and after (May 10, 2013) the changes.
Second, we investigated changes that happened in Kathmandu, Nepal, after the April 25, 2015 earthquake. For this analysis, we have two TerraSAR-X acquisitions, one before (October 13, 2013) and one immediately after (April 27, 2015) the earthquake. Both areas differ by the building types, the image resolution and the available reference, which makes it an interesting challenge.
In this paper, we compare six different methods for change detection. The investigated methods contain both standard criteria such as Log Ratio, Kullback-Leibler and the Difference of Entropies detector, and methods developed by the authors such as a Log Ratio combined with an Alternating Sequential Filter. All change detection results are presented and discussed by considering the available ground truth.
Change detection based on remote sensing imagery is a topic highly on demand with various fields of application. Probably, disaster management is the best known, where it is crucial to get fast and reliable results to enable a suitable supply of the affected region. Another important issue, for example in city or land-use planning, is the regular monitoring of specific regions of interest. For both scenarios, it would be significant to have information about the type or category of the detected changes. Since High-Resolution (HR) Synthetic Aperture Radar (SAR) is in opposite to optical sensors an active technique, it is well-capable for all change detection topics where a regular monitoring is intended. SAR sensors illuminate the investigated scene by their own microwave radiation and most applied microwave wavelengths make SAR nearly independent from atmospheric effects like dust, fog, and clouds. Moreover, the time of day makes no difference using SAR sensors. Acquired in HR SpotLight mode 300 (HS300) by the German satellite TerraSAR-X (TSX), images have a resolution of better than one meter, which allows to separate small objects placed close together. In this paper, a concept of change analysis focusing on small-sized areas is presented. Those change areas can be caused by man-made objects (e.g. vehicles, small construction sites) or natural events like phenologically based changes of the vegetation. Since the presented change analysis concept deals with the analysis of time series imagery, other seasonal also man-made caused changes (e.g. agriculture) can be detected. Furthermore, the concept comprises the categorization of the detected changes, which separates it from many of the existing change detection approaches. It includes five central components given by the change detection itself, the pre-categorization of change pixels, the feature extraction for change blobs, the analysis of their spatial context, and the final decision making forming a categorization statement. In all steps, Object-Based Image Analysis (OBIA) methods are utilized. As test area, the airport of Stuttgart (GER) and its surroundings containing heterogeneous change categories is considered. At current state, one time series consisting of 11 HS300 amplitude images acquired in ascending (ASC) orbit direction is available. For the evaluation of results, several reference data are useable comprising optical satellite, terrestrial information and GIS vector data.
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.
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.
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.
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
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.