An approach to change detection in time series of SAR images based on multitemporal similarity measures

2008 ◽  
Author(s):  
F. Bovolo ◽  
L. Bruzzone
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Similarity Measures ◽  
Sar Images

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 Change Detection in Time Series of Polarimetric SAR Images

2021 ◽  
pp. 35-71
Author(s):  
Knut Conradsen ◽  
Henning Skriver ◽  
Morton J. Canty ◽  
Allan A. Nielsen
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Polarimetric Sar ◽  
Sar Images

scholarly journals New Robust Statistics for Change Detection in Time Series of Multivariate SAR Images

2019 ◽  
Vol 67 (2) ◽  
pp. 520-534 ◽  
Author(s):  
Ammar Mian ◽  
Guillaume Ginolhac ◽  
Jean-Philippe Ovarlez ◽  
Abdourrahmane Mahamane Atto
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Robust Statistics ◽  
Sar Images

scholarly journals Unsupervised Multitemporal Building Change Detection Framework Based on Cosegmentation Using Time-Series SAR

2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Kaiyu Zhang ◽  
Xikai Fu ◽  
Xiaolei Lv ◽  
Jili Yuan
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Accuracy Assessment ◽  
Detection Methods ◽  
Sar Images ◽  
Assessment Index ◽  
Moment Maps ◽  
Marketing Planning ◽  
Frequency Map ◽  
Time Series Images

Building change detection using remote sensing images is essential for various applications such as urban management and marketing planning. However, most change detection approaches can only detect the intensity or type of change. The aim of this study is to dig for more change information from time-series synthetic aperture radar (SAR) images, such as the change frequency and the change moments. This paper proposes a novel multitemporal building change detection framework that can generate change frequency map (CFM) and change moment maps (CMMs) from multitemporal SAR images. We first give definitions of CFM and CMMs. Then we generate change feature using four proposed generators. After that, a new cosegmentation method combining raw images and change feature is proposed to divide time-series images into changed and unchanged areas separately. Secondly, the proposed cosegmentation and the morphological building index (MBI) are combined to extract changed building objects. Then, the logical conjunction between the cosegmentation results and the binarized MBI is performed to recognize every moment of change. In the post-processing step, we use fragment removal to increase accuracy. Finally, we propose a novel accuracy assessment index for CFM. We call this index average change difference (ACD). Compared to the traditional multitemporal change detection methods, our method outperforms other approaches in terms of both qualitative results and quantitative indices of ACD using two TerraSAR-X datasets. The experiments show that the proposed method is effective in generating CFM and CMMs.

A comparison of time series similarity measures for classification and change detection of ecosystem dynamics

2011 ◽  
Vol 115 (12) ◽  
pp. 3129-3152 ◽  
Author(s):  
S. Lhermitte ◽  
J. Verbesselt ◽  
W.W. Verstraeten ◽  
P. Coppin
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Similarity Measures ◽  
Ecosystem Dynamics

scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

River ice monitoring and change detection with multi-spectral and SAR images: application over yellow river

2021 ◽  
Author(s):  
Xiuwei Zhang ◽  
Yuanzeng Yue ◽  
Lin Han ◽  
Fei Li ◽  
Xiuzhong Yuan ◽  
...  
Keyword(s):  
Change Detection ◽  
Yellow River ◽  
River Ice ◽  
Sar Images
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