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Synthetic Aperture Radar (SAR) satellite sensors recently provide valuable sources of earth observation data for various environmental applications. Beside the specifics properties of these data including multi-polarization and polarimetric image data, the presence of unavoidable speckle seriously degrades the quality of these data. Specifically, in certain applications such as clustering, classification...
The SAR image data must be compressed efficiently so that the requirements for transmission bandwidth and storage space, which are brought by large amount of data on SAR image, can be reduced. The traditional methods of SAR image compression based on wavelet transformation can only decompose low frequency sub-bands, resulting in the loss of important information of high frequency sub-bands. Aiming...
Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. This paper proposes a novel DFB-based algorithm with hidden Markov modeling, which reduces speckle in SAR images while preserving the structural features and textural information of the scene, and introduces evolutionary computation theory...
As speckle noise suppression is important for Synthetic aperture radar (SAR) images processing, this paper presents an approach for SAR image despeckling based on nonsubsampled directionlets. Firstly, images are partitioned into subbands using nonsubsampled directionlets. Then, the coefficients of subbands are modeled with Gaussian scale mixtures (GSM). Besides, for reducing the speckle noise, coefficients...
In this paper, we present a new method of change detection in SAR images based on multiscale product of wavelet transform and PCA algorithm. This method applied multiscale product of wavelet transform, in order to avoid the affect of speckle noise in SAR, enhance the changed information and weaken the effect of noise. The algorithm of PCA is used to merge every scale after the process of multiscale...
A new method about SAR image despeckling is proposed in this paper, this method is achieved by combining wavelet kernel transform (WKT) and Gaussian Scale Mixture model (GSM). WKT is a multiscale transform which is based on machine learning model. By analysis the distribution of the coefficients after WKT, these coefficients are similar to Gaussian distribution, and these noised coefficients are distributed...
Curvelet transform is a new kind of multiscale analysis algorithm which is more suitable for image processing, as compared with Wavelet it can better analysis the line and curve edge characteristics, and it has better approximation precision and sparsity description, also has good directivity. This paper introduces that remote sensing image speckle reduction based on Curvelet transform. Synthetic...
In this paper, a wavelet-based speckle-removing algorithm is represented and tested on synthetic aperture radar (SAR) images. The SAR image is first transformed using a dyadic wavelet transform. The noise in the wavelet-transformed image is modeled as an additive signal-dependent noise with Gaussian distribution. The distribution of a noise-free image in a wavelet domain is modeled as a generalized...
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