Remote Sensing
Remote sensing is a technology for gathering data
and information about the physical world by detecting and
measuring signals emanating from specific source targets on
the Earth's land surface, oceans, and atmosphere. Since the
final product of remote sensing is usually an image
displaying some physical properties of the observed area,
image processing techniques play a very important role in
the production, organization and interpretation of data
acquired by remote sensing technologies.
The experience of the IAPP group in image processing for
remote sensing is mainly related to image analysis,
enhancement and restoration techniques for data coming from
synthetic aperture radar (SAR) and optical/infrared
hyperspectral sensors.
- SAR Image Processing
SAR remote sensing offers a number of advantages
over optical remote sensing, mainly the all-day, all weather
acquisition capability. However, the main drawback of SAR
data is the presence of speckle, a signal dependent granular
noise, inherent of all active coherent systems, which
visually degrades the appearance of images and may severely
diminishes the performances of automated scene analysis and
information extraction techniques.
The availability of efficient tools for speckle reduction,
or de-speckling, may become of crucial importance in
applications requiring multiple SAR observations. Since the
pioneering Lee's filter in 1980, an extensive literature has
flourished around the problem of de-speckling. The last ten
years have witnessed the ever increasing presence of
de-speckling methods based on multi-resolution analysis. The
superior performances of such methods over conventional
spatial filters has been recognised only very recently for
de-speckling, unlike for standard image de-noising, which
has started benefiting from multi-resolution approaches
several years before.
- Hyperspectral Image Processing
Hyperspectral sensors collect image data simultaneously in
dozens or hundreds of narrow, adjacent spectral bands. These
measurements make it possible to derive a continuous spectrum
for each image cell, which can be compared with field or
laboratory reflectance spectra in order to recognize and map a
large variety of surface materials/materials. Due to their potentials,
hyperspectral images can find many applications in resource
management, agriculture, mineral exploration, and
environmental monitoring.
Recently, the ability to simultaneously capture a
high-resolution panchromatic (PAN) image and a hyperspectral
image, typically with much lower resolution, has been taken
into consideration. All this makes the development and
evaluation of algorithms that allow to achieve better spatial
resolution in hyperspectral images through image fusion
techniques very interesting.
For the proper functioning of these techniques it is extremely
important to characterize the sensor noise model, which in
modern sensors often involves the prevalence of photonic
noise, which is signal dependent and intrinsic of the
optoelectronic acquisition process, compared to electronic
noise, which is independent of the signal and is likely to be
reduced with the progress of technology. In addition,
techniques of image enhancement and restoration based on these
noise models can be effectively used to improve the quality of
data.
