In burst mode SAR imaging, echo intensity depends on the target's azimuth position in the antenna pattern. As a result, an amplitude modulation known as scalloping may appear, particularly in ScanSAR images of ocean areas. A denoising method, recently developed for multibeam bathymetry, can be used to reduce residual scalloping in ScanSAR images. The algorithm is analogous to a band-stop filter in the frequency domain. Here, the transform is the composition of an edge detection operator and a discrete Radon transform (DRT). The edge operator accentuates fine-scale intensity changes; the DRT focuses linear features, as each DRT component is the sum of pixel intensities along a linear graph. A descalloping filter is implemented in the DRT domain by suppressing the range direction. The restored image is obtained by applying the inverse composite transform. First, a rapidly converging iterative pseudo-inverse DRT is computed. The edge operator is a spatial filter based on a discrete approximation of the Laplace operator, but modified to make the operator invertible. The method was tested on ocean scene ScanSAR images from the Envisat Advanced Synthetic Aperture Radar. The scalloping effect was significantly reduced, with no apparent distortion or smoothing of physical features.
This paper discusses robust classification of hyperspectral images. Both methods for dimensionality reduction
and robust estimation of classifier parameters in full dimension are presented. A new approach to dimensionality
reduction that uses piecewise constant function approximation of the spectral curve is compared to conventional
dimensionality reduction methods like principal components, feature selection, and decision boundary
feature extraction. Computing robust estimates of the decision boundary in full dimension is an alternative to
dimensionality reduction. Two recently proposed techniques for covariance estimation based on the eigenvector
decomposition and the Cholesky decomposition are compared to Support Vector Machine classifiers, simple
regularized estimates, and regular quadratic classifiers. The experimental results on four different hyperspectral
data sets demonstrate the importance of using simple, sparse models. The sparse model using Cholesky decomposition
in full dimension performed slightly better than dimensionality reduction. However, if speed is an issue,
the piecewise constant function approximation method for dimensionality reduction could be used.
In this paper, we present results from a study on classifiers for automatic oil slick classification in ENVISAT ASAR images. First, based on our basic statistical classifier, we improve the classification performance by introducing regularization of the covariance matrixes. The new improved classifier reduces the false alarm rate from 19.6% to 13.1%. Second, we compare the statistical classifier with SVM, finding that the statistical classifier outranks SVM for this particular application. Experiments are done on a set of 103 SAR images.
In this paper, a general model for multisource classification of remotely sensed data based on Markov random fields (MRF) is proposed. A specific model for fusion of optical images, synthetic aperture radar (SAR) images, and GIS (geographic information systems) ground cover data is presented in detail and tested. The MRF model exploits spatial class dependency context between neighboring pixels in an image, and temporal class dependency context between the different images. The performance of the specific model is investigated by fusing Landsat TM images, multitemporal ERS-1 SAR images, and GIS ground-cover maps for land- use classification. The MRF model performs significantly better than a simpler reference fusion model it is compared to.
Conference Committee Involvement (6)
Image and Signal Processing for Remote Sensing
19 September 2011 | Prague, Czech Republic
Image and Signal Processing for Remote Sensing
20 September 2010 | Toulouse, France
Image and Signal Processing for Remote Sensing
31 August 2009 | Berlin, Germany
Image and Signal Processing for Remote Sensing
15 September 2008 | Cardiff, Wales, United Kingdom
Image and Signal Processing for Remote Sensing
18 September 2007 | Florence, Italy
Image and Signal Processing for Remote Sensing XII
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