The application of synthetic aperture radar (SAR) in urban areas is far from resolved with an increased spatial resolution, so building extraction from SAR images remains a difficult task. According to SAR imaging principles, the outline of a building is usually incomplete in a single-aspect SAR image, and microwave interactions between adjacent targets further complicate this phenomenon in urban areas. Thus, in this study, dual-aspect high-resolution SAR images obtained, respectively from ascending and descending orbits are introduced to extract the building footprints in urban areas, and a method for building footprint extraction based on the fusion of dual-aspect SAR images is proposed. First these dual-aspect SAR images are co-registered, and then the preliminary positions of each potential building are determined using Markov random field models and Hough transform. Next the test images are partitioned into several subimages that contain only one building target. Then the edge of a building is extracted within the subimage of each aspect using a region-growing method and gradient algorithm, and then detection results obtained from each aspect are fused to produce the ultimate outline of the buildings based on Dempster-Shafer evidence theory. Experiments using TerraSAR-X images demonstrate that this method can extract the complete footprint of buildings in urban areas and can also improve the accuracy when estimating the dimensions of buildings.
In the southwest of China, it is anticipated that synthetic aperture radar (SAR) will become an important tool for forest inventory because of its all-weather capabilities. The Zhazuo area in Guizhou Province of southwest China, with a typical Karst landform, was selected as the test site. Six RADARSAT-2 polarimetric images were acquired in order to analyze polarimetric backscattering behavior and temporal variation of forest and deforested area. Polarimetric decomposition was conducted, and Pauli and Freeman-Durden decomposition were demonstrated to be more suitable for identifying forest and deforestation respectively. Finally, a scheme for multitemporal polarimetric SAR data fusion was proposed, which could greatly improve image quality and make forest identification more efficient. Support vector machine classification showed that the overall accuracy for forest identification was 87.63%, and the accuracy could be enhanced to 91.49% after gamma filtering.
In this paper, a simulation method is introduced to generate synthetic aperture radar (SAR) image based on ray tracing
algorithm. 3D models of buildings, which are triangulated and described with vectors, are introduced into the simulator
and then the simulated images can be generated under different viewing configuration. The simulation consists of three
steps -modeling of the scene, tracing and generation of high resolution SAR images. 3D models of man-made objects are
illuminated by a virtual antenna whose signal is simplified by rays sent to the objects and back to the sensor. Then the
intensity map of rays is gridded into the SAR images. In the end, two buildings, one with a plane roof and the other with
a gable roof, are imported into the simulator under different viewing configuration. The effects of layover, shadow and
double bounce are simulated correctly in geometrically. So the simulator can be used for some interpret complex SAR
images which are composed of buildings.
Synthetic aperture radar (SAR) provides a powerful tool for forestry inventory because of its all-weather and all-day
capabilities. In this paper forest mapping method using bi-aspect polarimetric SAR data acquired from ascending and
descending path has been studied. Zhazuo forest farm in Guizhou province was selected as test site and an 8-temporal
field experiment was designed to obtain bio-physical parameters and spatial structure parameters of the 12 sample plots.
Then the Michigan Microwave Canopy Scattering model (MIMICS) was employed to analyze the seasonal variation of
these 4 types of managed forests. Using polarimetric Radarsat 2 data, scattering mechanisms of each forest type were
determined and polarimetric variables were extracted and analyzed for forest discrimination. Considering the inherent
geometric distortion of SAR imaging in hilly areas, a geometric correction strategy using bi-aspect SAR images and high
resolution DEM was proposed. Then support vector machines method was adopted for classification of the whole test
area. Experiments show that the bi-aspect geometric strategy is useful for hilly areas especially for shadow elimination in
SAR image, and polarimetric SAR data is helpful to forest mapping.
Synthetic Aperture Radar remote sensing has been effectively used in water compliance and enforcement, especially in
ship detection, but it is still very difficult to classify or identify vessels in inland water only using existing SAR image.
Nevertheless some experience knowledge can help, for example waterway channel is of great significance for water
traffic management and illegal activity monitoring. It can be used for judging a vessel complying with traffic rules or
not, and also can be used to indicate illicit fishing vessels which are usually far away from navigable waterway channel.
For illicit vessel identification speed and efficiency are very important, so it will be significant if we can extract
waterway channel directly from SAR images and use it to identify illicit vessels. The paper first introduces the modified
two-parameter CFAR algorithm used to detect ship targets in inland waters, and then uses principal curves and neural
networks to extract waterway channel. Through comparing the detection results and the extracted waterway channel
those vessels not complying with water traffic rules or potential illicit fishing vessels can be easily identified.
Terrestrial net primary production (NPP), as an important component of carbon cycle on land, not only indicates directly the production level of vegetation community on land, but also shows the status of terrestrial ecosystem. What's more, NPP is also a determinant of carbon sinks on land and a key regulator of ecological processes, including interactions among tropic levels. In the study, three existing models are combined with each other to assess net primary production in Haihe Basin, China. The photosynthetically active radiation (PAR) model of Monteith is used for the calculation of absorbed photosynthetically active radiation (APAR), the light utilization efficiency model of Potter et al. is used for determining the light utilization efficiency, and the surface energy balance system (SEBS) of Su is used into Potter's model to describe water stress in land wetness conditions. To assess NPP, We use NOAA-AVHRR data from November 2003 to September 2004 and the corresponding daily data of temperature and hours of sunshine obtained from meteorological stations in Haihe Basin, China. After atmospheric, geometrical and radiant corrections, every ten days NOAA data are processed to become an image of NDVI by means of the maximal value composition method (MVC) in order to eliminate some noises. Using these data, we compute NPP in spring season and spring season of 2004 in Haihe Basin, China. The result shows, in Haihe Basin, NPP for spring season is averaged to 336.10gC•m<sup>-2</sup>, and 709.16 gC•m<sup>-2</sup> for autumn season. In spatial distribution, NPP is greater in both ends than in middle for spring season, and decrease increasingly from north to south for autumn season. Future work should rely on the integration of high and low resolution images to assess net primary production, which will probably have more accurately estimation.