scholarly journals Analysis of Stochastic Distances and Wishart Mixture Models Applied on PolSAR Images

2019 ◽  
Vol 11 (24) ◽  
pp. 2994 ◽  
Author(s):  
Naiallen Carolyne Rodrigues Lima Carvalho ◽  
Leonardo Sant’Anna Bins ◽  
Sidnei João Siqueira Sant’Anna
Keyword(s):  
Em Algorithm ◽  
Unsupervised Classification ◽  
Wishart Distribution ◽  
Classification Performance ◽  
Amazon Forest ◽  
Bhattacharyya Distance ◽  
Chi Square ◽  
Em Algorithms ◽  
Polarimetric Synthetic Aperture Radar ◽  
The Em Algorithm

This paper address unsupervised classification strategies applied to Polarimetric Synthetic Aperture Radar (PolSAR) images. We analyze the performance of complex Wishart distribution, which is a widely used model for multi-look PolSAR images, and the robustness of five stochastic distances (Bhattacharyya, Kullback-Leibler, Rényi, Hellinger and Chi-square) between Wishart distributions. Two unsupervised classification strategies were chosen: the Stochastic Clustering (SC) algorithm, which is based on the K-means algorithm but uses stochastic distance as the similarity metric, and the Expectation-Maximization (EM) algorithm for Wishart Mixture Model. With the aim of assessing the performance of all algorithms presented here, we performed a Monte Carlo simulation over a set of simulated PolSAR images. A second experiment was conducted using the study area of Tapajós National Forest and the surrounding area, in Brazilian Amazon Forest. The PolSAR images were obtained by the satellite PALSAR. The results, in both experiments, suggest that the EM algorithm and the SC with Hellinger and the SC with Bhattacharyya distance provide a better classification performance. We also analyze the initialization problem for SC and EM algorithms, and we demonstrate how the initial centroid choice influences the final classification result.

scholarly journals Hybrid DE-EM Algorithm for Gaussian Mixture Model-Based Wireless Channel Multipath Clustering

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Yupeng Li ◽  
Jianhua Zhang ◽  
Ruisi He ◽  
Lei Tian ◽  
Hewen Wei
Keyword(s):  
Em Algorithm ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Clustering Algorithm ◽  
Gaussian Mixture ◽  
Wireless Channel ◽  
Model Parameters ◽  
Em Algorithms ◽  
The Em Algorithm ◽  
Searching Ability

In this paper, the Gaussian mixture model (GMM) is introduced to the channel multipath clustering. In the GMM field, the expectation-maximization (EM) algorithm is usually utilized to estimate the model parameters. However, the EM widely converges into local optimization. To address this issue, a hybrid differential evolution (DE) and EM (DE-EM) algorithms are proposed in this paper. To be specific, the DE is employed to initialize the GMM parameters. Then, the parameters are estimated with the EM algorithm. Thanks to the global searching ability of DE, the proposed hybrid DE-EM algorithm is more likely to obtain the global optimization. Simulations demonstrate that our proposed DE-EM clustering algorithm can significantly improve the clustering performance.

scholarly journals Unsupervised Classification of Polarimetric SAR Image Based on Geodesic Distance and Non-Gaussian Distribution Feature

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1317
Author(s):  
Junrong Qu ◽  
Xiaolan Qiu ◽  
Chibiao Ding ◽  
Bin Lei
Keyword(s):  
Geodesic Distance ◽  
Image Interpretation ◽  
Unsupervised Classification ◽  
Wishart Distribution ◽  
Surface Scattering ◽  
Polarimetric Synthetic Aperture Radar ◽  
Initial Segmentation ◽  
L Band ◽  
Non Gaussian

Polarimetric synthetic aperture radar (PolSAR) image classification plays a significant role in PolSAR image interpretation. This letter presents a novel unsupervised classification method for PolSAR images based on the geodesic distance and K-Wishart distribution. The geodesic distance is obtained between the Kennaugh matrices of the observed target and canonical targets, and it is further utilized to define scattering similarity. According to the maximum scattering similarity, initial segmentation is produced, and the image is divided into three main categories: surface scattering, double-bounce scattering, and random volume scattering. Then, using the shape parameter α of K-distribution, each scattering category is further divided into three sub-categories with different degrees of heterogeneity. Finally, the K-Wishart maximum likelihood classifier is applied iteratively to update the results and improve the classification accuracy. Experiments are carried out on three real PolSAR images, including L-band AIRSAR, L-band ESAR, and C-band GaoFen-3 datasets, containing different resolutions and various terrain types. Compared with four other classic and recently developed methods, the final classification results demonstrate the effectiveness and superiority of the proposed method.

scholarly journals Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

2019 ◽  
Vol 11 (16) ◽  
pp. 1933 ◽  
Author(s):  
Yangyang Li ◽  
Ruoting Xing ◽  
Licheng Jiao ◽  
Yanqiao Chen ◽  
Yingte Chai ◽  
...  
Keyword(s):  
Image Classification ◽  
State Of The Art ◽  
Sample Selection ◽  
Spatial Relations ◽  
Speckle Noise ◽  
Classification Performance ◽  
Selection Strategy ◽  
Training Set ◽  
Polarimetric Synthetic Aperture Radar ◽  
Unlabeled Sample

Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

EM Algorithm Based on Fuzzification and its Application

2012 ◽  
Vol 532-533 ◽  
pp. 1445-1449
Author(s):  
Ting Ting Tong ◽  
Zhen Hua Wu
Keyword(s):  
Em Algorithm ◽  
Mixed Model ◽  
Image Data ◽  
Training Sample ◽  
Model Parameters ◽  
Parameter Learning ◽  
Weighted Degree ◽  
The Em Algorithm ◽  
The Impact

EM algorithm is a common method to solve mixed model parameters in statistical classification of remote sensing image. The EM algorithm based on fuzzification is presented in this paper to use a fuzzy set to represent each training sample. Via the weighted degree of membership, different samples will be of different effect during iteration to decrease the impact of noise on parameter learning and to increase the convergence rate of algorithm. The function and accuracy of classification of image data can be completed preferably.

scholarly journals PENDUGAAN DATA HILANG DENGAN METODE YATES DAN ALGORITMA EM PADA RANCANGAN LATTICE SEIMBANG

2015 ◽  
Vol 4 (2) ◽  
pp. 74
Author(s):  
MADE SUSILAWATI ◽  
KARTIKA SARI
Keyword(s):  
Missing Data ◽  
Em Algorithm ◽  
Basic Concept ◽  
Incomplete Data ◽  
Likelihood Function ◽  
Animal Husbandry ◽  
Lattice Design ◽  
The Em Algorithm ◽  
Missing Data Estimation ◽  
Data Estimation

Missing data often occur in agriculture and animal husbandry experiment. The missing data in experimental design makes the information that we get less complete. In this research, the missing data was estimated with Yates method and Expectation Maximization (EM) algorithm. The basic concept of the Yates method is to minimize sum square error (JKG), meanwhile the basic concept of the EM algorithm is to maximize the likelihood function. This research applied Balanced Lattice Design with 9 treatments, 4 replications and 3 group of each repetition. Missing data estimation results showed that the Yates method was better used for two of missing data in the position on a treatment, a column and random, meanwhile the EM algorithm was better used to estimate one of missing data and two of missing data in the position of a group and a replication. The comparison of the result JKG of ANOVA showed that JKG of incomplete data larger than JKG of incomplete data that has been added with estimator of data. This suggest  thatwe need to estimate the missing data.

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