Change analysis of dual polarimetric Sentinel-1 SAR image time series using stationary wavelet transform and change detection matrix

2015 ◽  
Author(s):  
Thu Trang Le ◽  
Abdourrahmane M. Atto ◽  
Emmanuel Trouve
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Change Detection ◽  
Sar Image ◽  
Stationary Wavelet Transform ◽  
Change Analysis

Change detection matrix for multitemporal filtering and change analysis of SAR and PolSAR image time series

2015 ◽  
Vol 107 ◽  
pp. 64-76 ◽  
Author(s):  
Thu Trang Lê ◽  
Abdourrahmane M. Atto ◽  
Emmanuel Trouvé ◽  
Akhmad Solikhin ◽  
Virginie Pinel
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Change Analysis

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 Change Detection from Remotely Sensed Images Based on Stationary Wavelet Transform

2017 ◽  
Vol 7 (6) ◽  
pp. 3395
Author(s):  
Abhishek Sharma ◽  
Tarun Gulati
Keyword(s):  
Wavelet Transform ◽  
Change Detection ◽  
High Frequency ◽  
Low Frequency ◽  
Speckle Noise ◽  
False Alarms ◽  
Stationary Wavelet Transform ◽  
Fusion Rules ◽  
Number Of False Alarms ◽  
Log Ratio

The major issue of concern in change detection process is the accuracy of the algorithm to recover changed and unchanged pixels. The fusion rules presented in the existing methods could not integrate the features accurately which results in more number of false alarms and speckle noise in the output image. This paper proposes an algorithm which fuses two multi-temporal images through proposed set of fusion rules in stationary wavelet transform. In the first step, the source images obtained from log ratio and mean ratio operators are decomposed into three high frequency sub-bands and one low frequency sub-band by stationary wavelet transform. Then, proposed fusion rules for low and high frequency sub-bands are applied on the coefficient maps to get the fused wavelet coefficients map. The fused image is recovered by applying the inverse stationary wavelet transform (ISWT) on the fused coefficient map. Finally, the changed and unchanged areas are classified using Fuzzy c means clustering. The performance of the algorithm is calculated in terms of percentage correct classification (PCC), overall error (OE) and Kappa coefficient (K<sub>c</sub>). The qualitative and quantitative results prove that the proposed method offers least error, highest accuracy and Kappa value as compare to its preexistences.

scholarly journals Change Analysis in Urban Areas Based on Statistical Features and Temporal Clustering Using TerraSAR-X Time-Series Images

2019 ◽  
Vol 11 (8) ◽  
pp. 926 ◽  
Author(s):  
Jili Yuan ◽  
Xiaolei Lv ◽  
Fangjia Dou ◽  
Jingchuan Yao
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Urban Areas ◽  
High Efficiency ◽  
Temporal Change ◽  
Cluster Number ◽  
Change Analysis ◽  
Sar Images ◽  
Temporal Dimension ◽  
Temporal Grouping

The existing unsupervised multitemporal change detection approaches for synthetic aperture radar (SAR) images based on the pixel level usually suffer from the serious influence of speckle noise, and the classification accuracy of temporal change patterns is liable to be affected by the generation method of similarity matrices and the pre-specified cluster number. To address these issues, a novel time-series change detection method with high efficiency is proposed in this paper. Firstly, spatial feature extraction using local statistical information on patches is conducted to reduce the noise and for subsequent temporal grouping. Secondly, a density-based clustering method is adopted to categorize the pixel series in the temporal dimension, in view of its efficiency and robustness. Change detection and classification results are then obtained by a fast differential strategy in the final step. The experimental results and analysis of synthetic and realistic time-series SAR images acquired by TerraSAR-X in urban areas demonstrate the effectiveness of the proposed method, which outperforms other approaches in terms of both qualitative results and quantitative indices of macro F1-scores and micro F1-scores. Furthermore, we make the case that more temporal change information for buildings can be obtained, which includes when the first and last detected change occurred and the frequency of changes.

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.

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