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The residual misregistration can be considered as a source of noise, referred as “Registration Noise (RN). The most critical components of this noise are related to spatially corresponding pixels that belong to different objects at the two dates (i.e., border region of objects or high frequency area in the images).

We aims at reducing the misalignment effects and thus at increasing the accuracy of multitemporal information extraction methods.

For decades, multispectral sensor images have been a stable and popular data source in Remote Sensing Change Detection (RS CD) application fields. This is mainly due to the availability of long time series databases and in the last decade to the free distribution of this information, at least for medium resolution sensors (e.g., MODIS, Landsat). With the development of sensor technology, the new generation satellite sensors have been capable to acquire images with higher spatial and spectral resolution and with pass of time, to offer a VHR time series database.

The Airborne light detection and ranging (LiDAR) is a popular active remote sensing technique that over-samples the remote study area using a highly directed laser beam, resulting in the generation of a very dense georeferenced elevation points called as the LiDAR point cloud. Unlike other visible remote sensing techniques, the data aquired using high sampling-density small-footprint muti-return LiDAR can record  a huge amount of  structural information about the forest vertical profile.

RaDAR (RAdio Detection And Ranging) is a general active system in the MW region of the spectrum for the detection and measurement of features of the objects. For its characteristics, it can operate even in nighttime and with clouds coverage. Moreover, the information retrieved is different and complementary to that from systems in optical regions of the spectrum (materials can absorb and reflect energy in different proportions as the radiation wavelength varies).