In this paper, we propose a novel change detection method. Multiple classifiers fusion combine results from various
simple changes detection methods to improve change detection accuracy. In detail, we make use of multiple classifiers
fusion based on fuzzy integrals for change detection. If the fuzzy measures are well defined, the accuracy of change
detection can be improved distinctly. In this paper, we determine the fuzzy measures based on the Genetic Algorithm
(GA). Though multiple classifiers fusion has robust performance, the input change detection result is still important. We
review proposed pre-classification change detection method, and propose two contextual Fuzzy-C Means (FCM)
algorithms and the Self Organization Feature Map (SOFM) change detection method. We select multi-spectral TM and
pan SPOT image pairs as test data and apply five different change detection methods. The first experiment shows that
different methods will produce different change detection accuracy, and different methods will complement each other.
In addition, we apply fuzzy integral aided by genetic algorithm for combining different detection methods. The final
experiment shows that our proposed method can improve change detection accuracy and has better performance than
single detection method.
Tree height is an important biophysical parameter. It is a need for forest and biomass map for the estimation of carbon
budget. Polarimetric SAR interferometry (PolInSAR) has been applied successfully to retrieve biophysical parameters
from forest areas. In the real process, the retrieval result is not truthfulness because of non-volumetric scattering
decorrelation scatterer. The scatterer is generated by coherence noise. The scatterer will be misjudged and mistook for a
tree. Before Retrieving of Tree Height, non-volumetric scattering decorrelation scatterer needed to be removed. This
paper will propose to combine the linear and optimization polarization interferometric coherence to remove the nonvolumetric
scattering decorrelation scatterer. The retrieval results have been proved reasonable.
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