This project intend to develop advanced signal processing and information retrieval approaches for tem-
poral analysis of SAR and POLSAR data, which can result in robust change detection and trend analysis
algorithms. Within this research area, we will focus on two important aspects; namely multi-class surface
cover classication, and the detection and measurement of temporal change. The application areas will be
related to surveillance of dynamic trends of northern land and ice (also snow covered) sur faces. The research
is divided into three work packages; WP1: Data acquisition and pre-processing. WP2: Statistical modeling.
WP3: Feature extraction and machine learning. In WP1 all relevant ground truth and existing SAR data
will be collected, prepro cessed and organized into time series. Plans for further satellite and ground truth
data acquisition will be made and carried out. In WP2 physical and statistical modeling of SAR/POLSAR
data will be further developed. The idea is to extract texture feat ures, which together with features derrived
from target decompsition methods, will enhance the classi?cation capabilities, and enable improved change
detection and trend analysis. In WP3 analysis strategies and algorithms rooted in information theoretic
and statistical learning theory will be developed and adapted to multidimensional SAR data. The team
comprises national and international experts on processing and analysis of multi-temporal, multidimensional
SAR remote sensing data, experts in machin e learning and pattern recognotion. in addition to experts from
the application areas. The latter scientists have signi?cant experience from ?eld work. The overall project
adminstration will be conducted by the University of Tromsø.