Markus Boldt

Change detection in high resolution SAR images: Amplitude based activity map compared with the CoVAmCoh analysis

Change detection based on remote sensing data has become a highly frequented field of research with multiple applications for practical use. In this study, a fully-automatic change detection method based on SAR amplitude images is proposed. This method aims at the detection of small-scaled abrupt changes which are caused for example by different kinds of vehicles or building sites. As dataset, a time series (about half a year) of TerraSAR-X images covering the scene of Greding (Germany) and surroundings was used. From this dataset, an amplitude based activity map was calculated. For evaluation purpose, this method is compared with the CoVAmCoh analysis, which represents a complementary approach for SAR change detection. In a final step, first evaluations concerning the change categorization are considered.

Parameter Determination by RapidEye and TerraSAR-X Data: A Step Toward a Remote Sensing Based Inventory, Monitoring and Fast Reaction System on Forest Enterprise Level

State forest administrations in Central Europe have to adapt to future climatic and socioeconomic conditions. This results in new demands for up-to-date and precise forest information—especially with regard to the increase of forest damages by natural hazards. Remote Sensing techniques are appropriated for delivering information in support of such tasks. We present details of a research project that focuses on the demonstration of the potential of satellite data for forest management planning and disaster management. Integrated in the over-all concept of a decision support system (DSS) for the forest–wood chain (Entscheidungs-Unterstutzungs-System Forst-Holz, EUS-FH), the frame conditions for a ‘Remote Sensing based Inventory and Monitoring System’ for the forest-wood chain are developed. Particular focus is on investigations towards synergistic and complementary use of the two German satellite systems RapidEye and Terra SAR-X. The comparison is done on base of the accuracy of parameter derivation with each of the systems. The results deliver a couple of arguments for combined multispectral and SAR data use for monitoring and fast response situations in case of sudden calamities. But it reveals as well that the references against the results should be compared and, at the end, which represents the data layers to be updated, do not always fit from both, the semantic meaning e.g., the definition of ‘forest’ to cartographic differences, and the representation of object categories. Harmonisation of definitions and categories to be mapped is needed.

Automated detection of storm damage in forest areas by analyzing TerraSAR-X data

Fast mapping of storm-damaged forest areas is in great demand. In general, airborne platforms are called into action to get a quick impression and to record high-resolution data. However, such storm events come often along with bad weather conditions that limit acquisition of optical data as well as flying by airplane. In this case, the new generation of high-resolution spaceborne SAR sensors (e.g., TerraSAR-X) can be used to acquire rapidly image data. The new generation of high-resolution spaceborne sensors increases the expectation of more promising results. In this paper, we focus first on the border line extraction of forest areas to enable a fast estimation of wind-thrown areas, whereby the pre-event forest border is derived from multi-spectral data. Second, clean-up operations are monitored in the affected forest area by applying a change detection operator.

Change detection in time series of high resolution SAR satellite images

In the last few years, change detection based on remote sensing data has become a highly frequented field of research with multiple applications for practical use. To detect changes between temporarily different satellite images is of interest for example in terms of urban monitoring and disaster management. The approach presented in this paper allows the fully automatic detection of small-scaled changes (e.g. vehicles or construction sites) in time series of SAR amplitude image data. To create a robust method, only one single parameter encoding the size of the detected changes has to be set by the operator. Furthermore, first steps concerning the categorization of the detected changes are presented. As dataset, a time series of high resolution SAR images acquired by the German satellite TerraSAR-X was used. The time span of this time series, acquired in ascending and descending orbit, is about half a year.

Object-based urban change detection analyzing high resolution optical satellite images

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.

Comparison of covamcoh and ILU image products for interferometric very high resolution SAR image pairs

Very high resolution InSAR image pairs include a tremendous information content compared to single images. There exist several RGB false color image products to improve the visualization and the interpretation of InSAR image pairs by using deduced image features. In this paper the two different products: the interferometric land use (ILU) image and the Coefficient of Variation-Amplitude-Coherence (CoVAmCoh) image are compared. Especially the potential for the interpretation of high resolution space borne SAR images is discussed and assessed.

Structural analysis of forest areas in high-resolution SAR images

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.

Using TanDEM data for forest height estimation and change detection

Mapping of forest coverage and forest changes became an increasing issue due to deforestation and forest degradation. Moreover, the estimation of related indicators such as carbon reduction, biomass and wood capacity is of large interest for industry and politics. As forest height is an important contributing parameter for these indicators, the region-wide estimation of forest heights is an essential step. This article investigates the accuracy potential of forest height estimation that can be reached by the current configuration of the two SAR satellites TerraSAR-X and TanDEM-X. Depending on the chosen acquisition mode and flight geometry, products of different quality can be achieved. Eight InSAR data sets showing different characteristics in geometric resolution, length of baseline, and mapping time, are processed and analyzed. To enable a thorough evaluation of the estimated heights, first-pulse LIDAR point clouds and aerial ortho-images are used as reference data.

Change Analysis at Stuttgart Airport Using TerraSAR-X Imagery

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.

Generalization of the CoVAmCoh analysis for the interpretation of arbitrary InSAR images

Very high resolution InSAR image pairs have a tremendous content of information compared to a single image. To improve the visualization and the interpretation of InSAR image pairs RGB false color image products are very helpful e.g. the well-known ILU-image (Interferometric Land Use image). In this paper, the CoVAmCoh method which was already introduced in former studies is analyzed with the aim of obtaining a general rule set for visualization independent e.g. of sensor parameters (incidence angle, resolution etc.). CoVAmCoh™ stands for the RGB arrangement of the three layers Coefficient of Variation (CoV), mean intensity (Am2) and the coherence (Coh) of an interferometric SAR image pair. It has the potential for fast extraction of physical scatter characteristics of the scene (e.g. detection of vegetation or areas of changes).