@inproceedings {815, title = {AUTOMATIC FEATURE LEARNING OF SAR IMAGES FOR SEA ICE CONCENTRATION ESTIMATION USING FEED-FORWARD NEURAL NETWORKS}, booktitle = {IGARSS}, year = {2014}, address = {Quebec City}, abstract = {

A\ two\ layer\ feed\ forward\ neural\ network\ is\ used\ to\ estimate\ ice\ concentration from SAR images directly in this research. SAR image patches are used as input. The CIS (Canadian Ice Service) ice concentration image analyses are used to train the neural network. The experiment shows that the simple neural network can be used to generate a reasonable ice concentra- tion with no preprocessing to the SAR images.

}, keywords = {neural network, regression, SAR, Sea ice concentration}, author = {L Wang and A K. Scott and D A. Clausi} } @inproceedings {255, title = {A new Bayesian source separation approach to blind decorrelation of SAR data}, booktitle = {IEEE International Geoscience and Remote Sensing Syposium}, year = {2010}, abstract = {

In this paper, a novel approach for performing blind decorrelation of SAR data is proposed. A patch-wise computation of the point-spread function (PSF) is performed directly from the SAR data to account for spatial nonstationarities present in SAR. The problem of estimating the PSF is formulated as an additive source separation problem in the frequency domain, and is subsequently solved using a Bayesian least squares estimation approach based on a Fisher-Tippett log-scatter model. Experimental results using both simulated SAR data and real RADARSAT-2 SAR sea-ice data showed that the proposed decorrelation approach can successfully learn the correct PSF and significantly reduce the correlation in SAR data.

}, keywords = {Bayesian least squares, decorrelation, SAR}, author = {A Wong and P Fieguth} } @inproceedings {593, title = {Automatic registration of SAR and visible band remote sensing images}, booktitle = {Joint IEEE International Geoscience and Remote Sensing Symposium and 24th Canadian Remote Sensing Symposium}, year = {2002}, abstract = {

Image registration is one of the basic image processing operations in remote sensing. With an increasing number of images collected every day from different sensors, automated registration of multi-sensor/multi-spectral images has become an important issue. A wide range of registration techniques exists for different types of applications and data sources, however no algorithm is known that can accurately register multi-source images consistently. This research addresses this problem by investigating the development of a fully automatic registration system for synthetic aperture radar (SAR) and optical remote sensing images. The development of this new automatic image registration method is based on the extraction and matching of common features that are visible in both images. The algorithm involves the following five steps: noise removal, edge extraction, edge linking pattern extraction and pattern matching. The application of the developed automatic image registration model to SAR and optical image pairs showed that accurate ground control points (GCPs) could be identified automatically.

}, keywords = {algorithm, edge extraction, edge linking pattern extraction, fully automatic registration, geophysical measurement technique, geophysical signal processing, geophysical techniques, image registration, land surface, multi-source image, noise removal, optical imaging, pattern matching, radar imaging, radar remote sensing, Remote Sensing, remote sensing by radar, remote sensing image, SAR, sensor fusion, synthetic aperture radar, terrain mapping, visible band}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026106}, author = {M Ali and D A. Clausi} } @article {586, title = {Hierarchical network flow phase unwrapping}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {40}, year = {2002}, pages = {1695 - 1708}, abstract = {

The well-studied interferometric synthetic aperture radar (InSAR) problem for digital elevation map generation involves the derivation of topography from the radar phase. The topography is a function of the full phase, whereas the measured phase is known modulo 2 pi;, necessitating the process of recovering full phase values via phase unwrapping. This mathematical process becomes difficult through the presence of noise and phase discontinuities. This paper is motivated by research which models phase unwrapping as a network-flow minimization problem. A major limitation is that often a substantial computational effort is required to find solutions. Commonly, these phase images are huge ( Gt;10 million pixels), and obviously the sheer size of the problem itself makes phase unwrapping challenging. This paper addresses the development of a computationally efficient hierarchical algorithm, based on a \"divide-and-conquer\" approach. We have shown that the phase-unwrapping problem can first be partitioned into independent phase-unwrapping subproblems, which can further be recombined to produce the unwrapped phase. Interestingly, the recombination step itself can be interpreted as an unwrapping problem, for which a modified network-flow solution applies! In short, this paper develops a parallelization of the network-flow algorithm, allowing images of virtually unlimited size to be unwrapped and leading to dramatic decreases in the algorithm execution time.

}, keywords = {computationally efficient hierarchical algorithm, digital elevation map generation, divide-and-conquer approach, full phase, geophysical signal processing, hierarchical network flow phase unwrapping, independent phase-unwrapping subproblems, InSAR problem, interferometric synthetic aperture radar problem, measured phase, modified network-flow solution, network-flow algorithm, network-flow minimization problem, parallelization, phase images, radar imaging, radiowave interferometry, recombination step, remote sensing by radar, SAR, synthetic aperture radar, topography, topography (Earth)}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2002.800279}, author = {G F. Carballo and P Fieguth} } @inproceedings {601, title = {Shape Preserving Edge Enhancement in Remote Sensing Imagery}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2002}, abstract = {

A novel approach to geometric shape preservation of remote sensing SAR sea ice images is presented in this paper. This approach will complement the existing edge detection schemes. The final edge is evolved by iterations. During each iteration all existing edges are checked for missing link and missing links are found by checking the orientation of the existing edges and by searching for best gradient path. When open edges are reduced considerably, opening of the features that are critical for shape preservation are found by tracing the edges and closing the boundary.

}, keywords = {best gradient path, edge detection, geometric shape preservation, geophysical signal processing, image enhancement, iteration, iterative method, measurement technique, missing link, ocean, oceanographic techniques, orientation, radar imaging, radar remote sensing, remote sensing by radar, SAR, sea ice, shape preserving edge enhancement, spaceborne radar, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026145}, author = {M S. Chowdhury and D A. Clausi} } @article {575, title = {The effect of speckle filtering on scale-dependent texture estimation of a forested scene}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {38}, year = {2000}, pages = {1160 - 1170}, abstract = {

Spatial fluctuations in microwave backscatter may be an important piece of information in discriminating tree stands. However, the presence of speckle in synthetic aperture radar (SAR) image data is a barrier to the exploitation of image texture. The authors explored a new methodology that combines a recent adaptive speckle reduction algorithm by Lopes et al. (1990) with a generic texture estimation scheme. They investigated the claim that this filter was capable of preserving backscatter texture. To understand if speckle reduction was destroying backscatter texture, they compared the strength of the relationship between forest inventory parameters and image texture as a function of spatial scale for both filtered and unfiltered images. They used Radarsat Fine mode image data: single look resolution is approximately 8.5 m, and pixel spacing is 3 m. Their study area was northern Vancouver Island, B.C., on the west coast of Canada. For the unfiltered data, they found that the ability of image texture to predict the forest parameters decreased as the texture scale increased from 3 to 13 m, suggesting greater information content in the small scale texture. For the filtered data, this relationship was much weaker at small scales and was not a function of distance. Their results suggest that the speckle filter was not retaining small scale texture, which is consistent with the theoretical hypotheses underlying its multiplicative noise model. They also show that there is significant information in small state SAR image texture that may be used as an adjunct to other spatial information for discriminating tree stands in the temperate rain forest

}, keywords = {adaptive signal processing, adaptive speckle reduction algorithm, backscatter texture, forest, forested scene, forestry, generic texture estimation scheme, geophysical measurement technique, geophysical signal processing, geophysical techniques, image texture, land surface, microwave backscatter, radar imaging, radar remote sensing, remote sensing by radar, SAR, scale-dependent texture estimation, spatial fluctuations, speckle, speckle filtering, synthetic aperture radar, tree stand, vegetation mapping}, issn = {0196-2892}, author = {M J. Collins and J Wiebe and D A. Clausi} } @inproceedings {513, title = {Probabilistic cost functions for network flow phase unwrapping}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {1999}, abstract = {

Phase unwrapping is a key step in deriving topographic information from interferometric SAR. A recent method is based on minimizing the total weighted length of branch cuts by computing the minimum cost flow on a network. The authors provide a theoretical foundation to derive the network weights based on a probabilistic approach

}, keywords = {branch cuts, geophysical measurement technique, geophysical techniques, InSAR, interferometric SAR, land surface topography, minimizing, minimum cost flow, network flow phase unwrapping, network weights, probabilistic approach, probabilistic cost function, radar imaging, radar remote sensing, radar theory, remote sensing by radar, SAR, synthetic aperture radar, terrain mapping, theoretical foundation, topographic mapping, topography (Earth), total weighted length}, doi = {http://dx.doi.org/10.1109/IGARSS.1999.772009}, author = {G F Carballo and P Fieguth} }