scholarly journals K-Matrix: A Novel Change-Pattern Mining Method for SAR Image Time Series

2019 ◽  
Vol 11 (18) ◽  
pp. 2161 ◽  
Author(s):  
Dong Peng ◽  
Ting Pan ◽  
Wen Yang ◽  
Heng-Chao Li
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Clustering Algorithm ◽  
Pattern Mining ◽  
Distance Matrix ◽  
Sar Image ◽  
Change Pattern ◽  
The Matrix ◽  
Distance Matrices ◽  
K Matrix

In this paper, we present a novel method for change-pattern mining in Synthetic Aperture Radar (SAR) image time series based on a distance matrix clustering algorithm, called K-Matrix. As it is different from the state-of-the-art methods, which analyze the SAR image time series based on the change detection matrix (CDM), here, we directly use the distance matrix to determine changed pixels and extract change patterns. The proposed scheme involves two steps: change detection in SAR image time series and change-pattern discovery. First, these distance matrices are constructed for each spatial position over the time series by a dissimilarity measurement. The changed pixels are detected by using a thresholding algorithm on the energy feature map of all distance matrices. Then, according to the change detection results in SAR image time series, the changed areas for pattern mining are determined. Finally, the proposed K-Matrix algorithm which clusters distance matrices by the matrix cross-correlation similarity is used to group all changed pixels into different change patterns. Experimental results on two datasets of TerraSAR-X image time series illustrate the effectiveness of the proposed method.

scholarly journals Robust Low-Rank Change Detection for Multivariate SAR Image Time Series

2020 ◽  
Vol 13 ◽  
pp. 3545-3556
Author(s):  
Ammar Mian ◽  
Antoine Collas ◽  
Arnaud Breloy ◽  
Guillaume Ginolhac ◽  
Jean-Philippe Ovarlez
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Low Rank ◽  
Sar Image

scholarly journals SAR Image Change Detection Based on Mathematical Morphology and the K-Means Clustering Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 43970-43978 ◽  
Author(s):  
Luyang Liu ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola K. Kasabov
Keyword(s):  
Change Detection ◽  
Mathematical Morphology ◽  
Clustering Algorithm ◽  
Sar Image ◽  
Image Change Detection

scholarly journals PRINCIPAL COMPONENT ANALYSIS AND ITS GENERALIZATIONS FOR ANY TYPE SEQUENCE (PCA-SEQ)

2019 ◽  
Author(s):  
V.M. Efimov ◽  
K.V. Efimov ◽  
V.Y. Kovaleva
Keyword(s):  
Time Series ◽  
Principal Components ◽  
Hamming Distance ◽  
Principal Component ◽  
Numerical Data ◽  
Distance Matrix ◽  
Phase Portraits ◽  
One Dimensional ◽  
Molecular Sequence ◽  
The Matrix

In the 40s of the last century, Karhunen and Loève proposed a method for processing of one-dimensional numeric time series by converting it into a multidimensional by shifts. In fact, a one-dimensional number series was decomposed into several orthogonal time series. This method has many times been independently developed and applied in practice under various names (EOF, SSA, Caterpillar, etc.). Nowadays, the name SSA (the Singular Spectral Analysis) is most often used. It turned out that it is universal, applicable to any time series without requiring stationary assumptions, automatically decomposes time series into a trend, cyclic components and noise. By the beginning of the 1980s Takens showed that for a dynamical system such a method makes it possible to obtain an attractor from observing only one of these variables, thereby bringing the method to a powerful theoretical basis. In the same years, the practical benefits of phase portraits became clear. In particular, it was used in the analysis and forecast of the animal abundance dynamics.In this paper we propose to extend SSA to one-dimensional sequence of any type elements, including numbers, symbols, figures, etc., and, as a special case, to molecular sequence. Technically, the problem is solved almost the same algorithm as the SSA. The sequence is cut by a sliding window into fragments of a given length. Between all fragments, the matrix of Euclidean distances is calculated. This is always possible. For example, the square root from the Hamming distance between fragments is the Euclidean distance. For the resulting matrix, the principal components are calculated by the principal-coordinate method (PCo). Instead of a distance matrix one can use a matrix of any similarity/dissimilarity indexes and apply methods of multidimensional scaling (MDS). The result will always be PCs in some Euclidean space.We called this method PCA-Seq. It is certainly an exploratory method, as its particular case SSA. For any sequence, including molecular, PCA-Seq without any additional assumptions allows to get its principal components in a numerical form and visualize them in the form of phase portraits. Long-term experience of SSA application for numerical data gives all reasons to believe that PCA-Seq will be not less useful in the analysis of non-numerical data, especially in hypothesizing.PCA-Seq is implemented in the freely distributed Jacobi 4 package (http://mrherrn.github.io/JACOBI4/).

SAR image change detection method based on intuitionistic fuzzy C-means clustering algorithm

2020 ◽  
Vol 38 (4) ◽  
pp. 3595-3604
Author(s):  
Deshuai Yin ◽  
Rui Hou ◽  
Junchao Du ◽  
Liang Chang ◽  
Hongxuan Yue ◽  
...  
Keyword(s):  
Change Detection ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Sar Image ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Fuzzy C Means Clustering ◽  
Image Change Detection

Detecting Stationary Intervals for Random Waves Using Time Series Clustering

2014 ◽  
Author(s):  
Carolina Euán ◽  
Joaquín Ortega ◽  
Pedro C. Alvarez-Esteban
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Transition Period ◽  
Distance Matrix ◽  
Real Data ◽  
Wave Climate ◽  
Time Interval ◽  
Random Waves ◽  
Sea Waves ◽  
Wave Measurements

The problem of detecting changes in the state of the sea is very important for the analysis and determination of wave climate in a given location. Wave measurements are frequently statistically analyzed as a time series, and segmentation algorithms developed in this context are used to determine change-points. However, most methods found in the literature consider the case of instantaneous changes in the time series, which is not usually the case for sea waves, where changes take a certain time interval to occur. We propose a new segmentation method that allows for the presence of transition intervals between successive stationary periods, and is based on the analysis of distances of normalized spectra to detect clusters in the time series. The series is divided into 30-minutes intervals and the spectral density is estimated for each one. The normalized spectra are compared using the Total Variation distance and a hierarchical clustering method is applied to the distance matrix. The information obtained from the clustering algorithm is used to classify the intervals as belonging to a stationary or a transition period We present simulation studies to validate the method and examples of applications to real data.

An Overview of Covariance‐based Change Detection Methodologies in Multivariate SAR Image Time Series

2021 ◽  
pp. 73-108
Author(s):  
Ammar Mian ◽  
Guillaume Ginolhac ◽  
Jean‐Philippe Ovarlez ◽  
Arnaud Breloy ◽  
Frédéric Pascal
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Sar Image
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