|
Urban areas are rapidly changing all over the world and therefore provoke the necessity to update urban maps frequently.
Remote sensing has been used for many years to monitor these changes. The urban scene is characterized by a very high
complexity, containing objects formed from different types of man-made materials as well as natural vegetation.
Hyperspectral sensors provide the capability to map the surface materials present in the scene using their spectra and
therefore to identify the main object classes in the scene in a relatively easy manner. However ambiguities persist where
different types of objects are constructed of the same material. This is for instance the case for roads and roof covers.
Although higher-level image processing (e.g. spatial reasoning) might be able to relief some of these constraints, this
task is far from straight forward. In the current paper the authors fused information gathered using a hyperspectral sensor
with that of high-resolution polarimetric SAR data. SAR data give information about the type of scattering backscatter
from an object in the scene, its geometry and its dielectric properties. Therefore, the information obtained using the SAR
processing is complementary to that obtained using hyperspectral data. This research was applied on a dataset consisting
of hyperspectral data from the HyMAP sensor (126 channels in VIS-SWIR) and E-SAR data which consists of fullpolarimetric
L-band and dual-polarisation (HH and VV) X-band data. Two supervised classifications are used; 'Logistic
Regression' (LR) which applied to the SAR and the PolSAR data and a 'Matched Filter' which is applied to the
hyperspectral data. The results of the classification are fused in order to improve the mapping of the main classes in the
scene and were compared to a ground truth map that was constructed by combining a digital topographic map and a
vectorized cadastral map of the research area. An adequate change detection of man-made objects in urban scenes was
obtained by the fusion of features derived from SAR, PolSAR and hyperspectral data.
|
