scholarly journals Despeckling PolSAR Images with a Structure Tensor Filter

2020 ◽  
Author(s):  
D Santana-Cedres ◽  
L Gomez ◽  
L Alvarez ◽  
Alejandro Frery
Keyword(s):  
Monte Carlo ◽  
Anisotropic Diffusion ◽  
State Of The Art ◽  
Monte Carlo Analysis ◽  
Structure Tensor ◽  
Synthetic Aperture ◽  
Experimental Setup ◽  
Polarimetric Synthetic Aperture Radar ◽  
Wishart Matrices ◽  
Dominant Direction

© 2004-2012 IEEE. In this letter, we propose a new despeckling filter for fully polarimetric synthetic aperture radar (PolSAR) images defined by 3× 3 complex Wishart distributions. We first generalize the well-known structure tensor to deal with PolSAR data which allows to efficiently measure the dominant direction and contrast of edges. The generalization includes stochastic distances defined in the space of the Wishart matrices. Then, we embed the formulation into an anisotropic diffusion-like schema to build a filter able to reduce speckle and preserve edges. We evaluate its performance through an innovative experimental setup that also includes Monte Carlo analysis. We compare the results with a state-of-the-art polarimetric filter.

scholarly journals Despeckling PolSAR Images with a Structure Tensor Filter

2020 ◽  
Author(s):  
D Santana-Cedres ◽  
L Gomez ◽  
L Alvarez ◽  
Alejandro Frery
Keyword(s):  
Monte Carlo ◽  
Anisotropic Diffusion ◽  
State Of The Art ◽  
Monte Carlo Analysis ◽  
Structure Tensor ◽  
Synthetic Aperture ◽  
Experimental Setup ◽  
Polarimetric Synthetic Aperture Radar ◽  
Wishart Matrices ◽  
Dominant Direction

© 2004-2012 IEEE. In this letter, we propose a new despeckling filter for fully polarimetric synthetic aperture radar (PolSAR) images defined by 3× 3 complex Wishart distributions. We first generalize the well-known structure tensor to deal with PolSAR data which allows to efficiently measure the dominant direction and contrast of edges. The generalization includes stochastic distances defined in the space of the Wishart matrices. Then, we embed the formulation into an anisotropic diffusion-like schema to build a filter able to reduce speckle and preserve edges. We evaluate its performance through an innovative experimental setup that also includes Monte Carlo analysis. We compare the results with a state-of-the-art polarimetric filter.

scholarly journals Self-Paced Convolutional Neural Network for PolSAR Images Classification

2019 ◽  
Vol 11 (4) ◽  
pp. 424 ◽  
Author(s):  
Changzhe Jiao ◽  
Xinlin Wang ◽  
Shuiping Gou ◽  
Wenshuai Chen ◽  
Debo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Synthetic Aperture ◽  
Training Process ◽  
Polarimetric Synthetic Aperture Radar ◽  
Learning Mechanism ◽  
3 Dimensional ◽  
Spatial Features ◽  
Rgb Values

Fully polarimetric synthetic aperture radar (PolSAR) can transmit and receive electromagnetic energy on four polarization channels (HH, HV, VH, VV). The data acquired from four channels have both similarities and complementarities. Utilizing the information between the four channels can considerably improve the performance of PolSAR image classification. Convolutional neural network can be used to extract the channel-spatial features of PolSAR images. Self-paced learning has been demonstrated to be instrumental in enhancing the learning robustness of convolutional neural network. In this paper, a novel classification method for PolSAR images using self-paced convolutional neural network (SPCNN) is proposed. In our method, each pixel is denoted by a 3-dimensional tensor block formed by its scattering intensity values on four channels, Pauli’s RGB values and its neighborhood information. Then, we train SPCNN to extract the channel-spatial features and obtain the classification results. Inspired by self-paced learning, SPCNN learns the easier samples first and gradually involves more difficult samples into the training process. This learning mechanism can make network converge to better values. The proposed method achieved state-of-the-art performances on four real PolSAR dataset.

scholarly journals A Novel Deep Fully Convolutional Network for PolSAR Image Classification

2018 ◽  
Vol 10 (12) ◽  
pp. 1984 ◽  
Author(s):  
Yangyang Li ◽  
Yanqiao Chen ◽  
Guangyuan Liu ◽  
Licheng Jiao
Keyword(s):  
Image Classification ◽  
Sparse Coding ◽  
State Of The Art ◽  
Sliding Window ◽  
Synthetic Aperture ◽  
Prediction Problem ◽  
Convolutional Network ◽  
Polarimetric Synthetic Aperture Radar ◽  
Fully Convolutional Network ◽  
Image Integrity

Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more popular in recent years. As we all know, PolSAR image classification is actually a dense prediction problem. Fortunately, the recently proposed fully convolutional network (FCN) model can be used to solve the dense prediction problem, which means that FCN has great potential in PolSAR image classification. However, there are some problems to be solved in PolSAR image classification by FCN. Therefore, we propose sliding window fully convolutional network and sparse coding (SFCN-SC) for PolSAR image classification. The merit of our method is twofold: (1) Compared with convolutional neural network (CNN), SFCN-SC can avoid repeated calculation and memory occupation; (2) Sparse coding is used to reduce the computation burden and memory occupation, and meanwhile the image integrity can be maintained in the maximum extent. We use three PolSAR images to test the performance of SFCN-SC. Compared with several state-of-the-art methods, SFCN-SC achieves promising results in PolSAR image classification.

scholarly journals Complex-Valued Convolutional Autoencoder and Spatial Pixel-Squares Refinement for Polarimetric SAR Image Classification

2019 ◽  
Vol 11 (5) ◽  
pp. 522 ◽  
Author(s):  
Ronghua Shang ◽  
Guangguang Wang ◽  
Michael A. Okoth ◽  
Licheng Jiao
Keyword(s):  
Image Classification ◽  
State Of The Art ◽  
Majority Voting ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Polarimetric Synthetic Aperture Radar ◽  
Convolutional Autoencoder ◽  
Comparable Accuracy ◽  
Complex Valued ◽  
Preliminary Classification

Recently, deep learning models, such as autoencoder, deep belief network and convolutional autoencoder (CAE), have been widely applied on polarimetric synthetic aperture radar (PolSAR) image classification task. These algorithms, however, only consider the amplitude information of the pixels in PolSAR images failing to obtain adequate discriminative features. In this work, a complex-valued convolutional autoencoder network (CV-CAE) is proposed. CV-CAE extends the encoding and decoding of CAE to complex domain so that the phase information can be adopted. Benefiting from the advantages of the CAE, CV-CAE extract features from a tiny number of training datasets. To further boost the performance, we propose a novel post processing method called spatial pixel-squares refinement (SPF) for preliminary classification map. Specifically, the majority voting and difference-value methods are utilized to determine whether the pixel-squares (PixS) needs to be refined or not. Based on the blocky structure of land cover of PolSAR images, SPF refines the PixS simultaneously. Therefore, it is more productive than current methods worked on pixel level. The proposed algorithm is measured on three typical PolSAR datasets, and better or comparable accuracy is obtained compared with other state-of-the-art methods.

Generation of Correlated Logistic-Normal Random Variates for Medical Decision Trees

1998 ◽  
Vol 37 (03) ◽  
pp. 235-238 ◽  
Author(s):  
M. El-Taha ◽  
D. E. Clark
Keyword(s):  
Monte Carlo ◽  
Decision Trees ◽  
Computer Algebra ◽  
Taylor Series ◽  
Computer Algebra System ◽  
Random Variable ◽  
Monte Carlo Analysis ◽  
Normal Random Variable ◽  
Medical Decision ◽  
Theoretical Results

AbstractA Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.

Monte Carlo Analysis of Nonequilibrium Ionized Flows.

1996 ◽  
Author(s):  
Iain D. Boyd ◽  
Xiaoming Liu ◽  
Jitendra Balakrishnan
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Analysis
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