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.

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

Generating an exclusion map for SAR-based flood extent maps using Sentinel-1 time series analysis

2020 ◽  
Author(s):  
Jie Zhao ◽  
Marco Chini ◽  
Ramona Pelich ◽  
Patrick Matgen ◽  
Renaud Hostache ◽  
...  
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Urban Areas ◽  
Ground Truth ◽  
Detection Methods ◽  
Tree Cover ◽  
Long Time ◽  
The Right ◽  
Flood Maps ◽  
Over Time

<p>Change detection has been widely used in many flood-mapping algorithms using pairs of Synthetic Aperture Radar (SAR) intensity images. The rationale is that when the right conditions are met, the appearance of floodwater results in a significant decrease of backscatter.  However, limitations still exist in areas where the SAR backscatter is not sufficiently impacted by surface changes due to floodwater. For example, in shadow areas, the backscatter is stable over time because the SAR signal does not reach the ground due to prominent topography or obstacles on the ground (e.g., buildings). Densely vegetated forest is another insensitive region due to low capability of SAR C-band wavelengths to penetrate its canopy. Moreover, although in principle SAR can sense water over different land cover classes such as arid regions, streets and buildings, the backscatter changes over time could not be detected because in such areas the scattering variation caused by the presence of water might be negligible with respect to the normal “unflooded” state. The identification of the abovementioned areas where SAR does not allow detecting water based on change detection methods, hereafter called exclusion map, is crucial for providing reliable SAR-based flood maps.</p><p>In this study, insensitive areas are identified using long time-series of Sentinel-1 data and the final exclusion map is classified in four distinctive classes: shadow, layover, urban areas and dense forest. In the proposed method the identification of insensitive areas is based on the use of pixel-based time series backscatter statistics (minimum, maximum, median and standard deviation) coupled with a local spatial autocorrelation analysis (i.e. Moran’s I, Getis-Ord Gi and Geary’s C). In order to evaluate the extracted exclusion map, which is quite unique, we employ a comprehensive ground truth dataset that is obtained by combining different products: 1) a shadow/layover map generated using a 25m-resolution DEM and the geometric acquisition parameters of the SAR data; 2) 20m resolution imperviousness map provided by Copernicus, as well as a high-resolution global urban footprint (GUF) data provided by DLR; 3) a 20m tree cover density (TCD) map provided by Copernicus. In the end, the exclusion map is used to mask out unclassified areas in the flood maps derived by an automatic change detection method, which is expected to enhance flood maps by removing areas where the presence or absence of floodwater cannot be evidenced. In addition, we argue that our insensitive area map provides valuable information for improving the calibration, validation and regular updating of hydraulic models using SAR derived flood extent maps.</p>

Markovian Change Detection of Urban Areas Using Very High Resolution Complex SAR Images

2014 ◽  
Vol 11 (5) ◽  
pp. 995-999 ◽  
Author(s):  
Fabio Baselice ◽  
Giampaolo Ferraioli ◽  
Vito Pascazio
Keyword(s):  
High Resolution ◽  
Change Detection ◽  
Urban Areas ◽  
Sar Images ◽  
Very High

scholarly journals Change Detection Based on the Coefficient of Variation in SAR Time-Series of Urban Areas

2020 ◽  
Vol 12 (13) ◽  
pp. 2089 ◽  
Author(s):  
Elise Colin Koeniguer ◽  
Jean-Marie Nicolas
Keyword(s):  
Time Series ◽  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Coefficient Of Variation ◽  
Urban Areas ◽  
Real Data ◽  
Probability Of Detection ◽  
Detection Methods ◽  
Coefficients Of Variations

This paper discusses change detection in SAR time-series. First, several statistical properties of the coefficient of variation highlight its pertinence for change detection. Subsequently, several criteria are proposed. The coefficient of variation is suggested to detect any kind of change. Furthermore, several criteria that are based on ratios of coefficients of variations are proposed to detect long events, such as construction test sites, or point-event, such as vehicles. These detection methods are first evaluated on theoretical statistical simulations to determine the scenarios where they can deliver the best results. The simulations demonstrate the greater sensitivity of the coefficient of variation to speckle mixtures, as in the case of agricultural plots. Conversely, they also demonstrate the greater specificity of the other criteria for the cases addressed: very short event or longer-term changes. Subsequently, detection performance is assessed on real data for different types of scenes and sensors (Sentinel-1, UAVSAR). In particular, a quantitative evaluation is performed with a comparison of our solutions with baseline methods. The proposed criteria achieve the best performance, with reduced computational complexity. On Sentinel-1 images containing mainly construction test sites, our best criterion reaches a probability of change detection of 90% for a false alarm rate that is equal to 5%. On UAVSAR images containing boats, the criteria proposed for short events achieve a probability of detection equal to 90% of all pixels belonging to the boats, for a false alarm rate that is equal to 2%.

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
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