Stereo matching is one of the most important and challenging subjects in computer vision, digital photogrammetry, and
image understanding. For the purpose of wide-baseline stereo matching, a novel approach on high-quality affine
invariant feature extraction is proposed. The key contribution of the novel approach is a filtering strategy for affine
invariant features detecting based on information content and spatial dispersion quality constraints. The essential idea is
to remove the features with low information content and bad distribution, just select the high-quality features (high
information content and good distribution). Based on the filtering strategy, an automatic algorithm on high-quality affine
invariant feature extraction is introduced. The experiment using image sequences with different texture conditions proves
that our algorithm can get much higher repeatability than the other algorithms, which is more suitable for subsequent
wide baseline stereo matching.
Interferometric synthetic aperture radar (InSAR) has been demonstrated useful for topographic mapping and surface
deformation measurement. However, the atmospheric disturbance, especially the tropospheric heterogeneity, represents a
major limitation to accuracy. It is usually difficult to accurately model and correct the atmospheric effects. Consequently,
significant errors are often resulted in misinterpretation of InSAR results. The purpose of this paper is to seek to reduce
the atmospheric effects on repeat-pass InSAR using independent datasets, viz. Global Positioning System (GPS). A
between-site and between-epoch double-differencing algorithm for the generation of tropospheric corrections to InSAR
results based on GPS observations is applied. In order to correct the radar results on a pixel-by-pixel basis, the Support
Vector Machine (SVM) with adaptive parameters is introduced to regressively estimate tropospheric corrections over
unknown points using the sparse GPS-derived corrections. The feasibility of applying SVM in troposphetic corrections
estimation is examined by using data from the Southern California Integrated GPS Network (SCIGN). Cross-validation
tests show that SVM method is more suitable than the conventional inverse distance weighted (IDW) method; it accounts
for not only topography-dependent but also topography-independent atmospheric effects, so it seems optimal to estimate
the tropospheric delay corrections of unknown pixels from GPS data.
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