Synthetic aperture radar (SAR) images are currently widely used in target recognition
tasks. In this work, we propose an automatic approach for radar shadow detection and extraction
from SAR images utilizing geometric projections along with the digital elevation model (DEM),
which corresponds to the given georeferenced SAR image. First, the DEM is rotated into the
radar geometry, so that each row would match that of a radar line of sight. Next, we extract the
shadow regions by processing row by row until the image is covered fully. We test the proposed
shadow detection approach on different DEMs and simulated one-dimensional signals and
two-dimensional hills and valleys modeled by various variance-based Gaussian functions.
Experimental results indicate that the proposed algorithm produces good results in detecting
shadows in SAR images with high resolution.
@article{PrasathJARS14,
author = "V. B. S. Prasath and O. Haddad",
title = "Radar shadow detection in synthetic aperture radar images using digital elevation model and projections",
year = 2014,
journal = "Journal of Applied Remote Sensing",
volume = 8,
number = 1,
pages = "083628",
keywords = "shadow detection, synthetic aperture radar, digital elevation model",
doi = "10.1117/1.JRS.8.083628",
url = "http://remotesensing.spiedigitallibrary.org/article.aspx?articleid=1874347"
}
V. B. S. Prasath and O. Haddad. Radar shadow detection in synthetic aperture radar images using digital elevation model and projections. Journal of Applied Remote Sensing, volume 8, issue 1, pages 083628, 2014.
We show that an adaptive robust filtering based image segmentation model can be used for microvasculature network detection and quantitative characterization in epifluorescence-based high resolution images. Inhomogeneous fluorescence contrast due to the variable binding properties of the lectin marker and leakage hampers accurate vessel segmentation. We use a robust adaptive filtering approach to remove noise and reduce inhomogeneities without destroying small scale vascular structures. An adaptive variance based thresholding method combined with morphological filtering yields an effective detection and segmentation of the vascular network suitable for medial axis estimation. Quantitative parameters of the microvascular network geometry, including curvature, tortuosity, branch segments and branch angles are computed using post segmentation-based medial axis tracing. Experiments using epifluorescence-based high resolution images of porcine and murine microvasculature demonstrates the effectiveness of the proposed approach for quantifying morphological properties of vascular networks.
@inproceedings{Surya:EMBC-2013-vessel,
author = "V. B. S. Prasath and O. Haddad and F. Bunyak and O. Glinskii and V. Glinsky and V. Huxley and K. Palaniappan",
title = "Robust filtering based segmentation and analysis of dura mater vessel laminae using epiflourescence microscopy",
year = 2013,
booktitle = "35th IEEE Engineering in Medicine and Biology Society Conf. (EMBC)",
pages = "6055-6058",
keywords = "segmentation, image enhancement, vasculature, biomedical"
}
V. B. S. Prasath, O. Haddad, F. Bunyak, O. Glinskii, V. Glinsky, V. Huxley, and K. Palaniappan. Robust filtering based segmentation and analysis of dura mater vessel laminae using epiflourescence microscopy. 35th IEEE Engineering in Medicine and Biology Society Conf. (EMBC), pages 6055-6058, 2013.
