scholarly journals Influence of a tone’s tonal function on temporal change detection

2007 ◽  
Vol 69 (8) ◽  
pp. 1450-1459 ◽  
Author(s):  
Géraldine Lebrun-Guillaud ◽  
Barbara Tillmann
Keyword(s):  
Change Detection ◽  
Temporal Change ◽  
Tonal Function

scholarly journals Landsat-8 and Sentinel-2 burned area mapping - A combined sensor multi-temporal change detection approach

2019 ◽  
Vol 231 ◽  
pp. 111254 ◽  
Author(s):  
David P. Roy ◽  
Haiyan Huang ◽  
Luigi Boschetti ◽  
Louis Giglio ◽  
Lin Yan ◽  
...  
Keyword(s):  
Change Detection ◽  
Temporal Change ◽  
Burned Area ◽  
Landsat 8 ◽  
Detection Approach ◽  
Burned Area Mapping ◽  
Multi Temporal ◽  
Combined Sensor ◽  
Sentinel 2

Fully Unsupervised Bi-Temporal Change Detection Framework for VHR SAR

2021 ◽  
Author(s):  
Shaunak De ◽  
Lloyd Hughes ◽  
Davide Castelletti ◽  
Ganesh Yalla
Keyword(s):  
Change Detection ◽  
Temporal Change

Forest change as a proxy for landslide occurrence - a Sentinel 2 based spatio-temporal landslide detection approach for two test sites

2020 ◽  
Author(s):  
Peter Mayrhofer ◽  
Stefan Steger ◽  
Ruth Sonnenschein ◽  
Giovanni Cuozzo ◽  
Clement Atzberger ◽  
...  
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Temporal Change ◽  
Information Source ◽  
Spectral Characteristics ◽  
Landslide Occurrence ◽  
South Tyrol ◽  
Detection Approach ◽  
Multi Temporal ◽  
Sentinel 2

<p>Landslides represent a major threat to humans and result in high costs for the society. Landslide inventory maps depict the areas of past slope instabilities and are a valuable information source for authorities, spatial planners and risk managers. However, existing inventories are rarely complete, especially in sparsely populated and/or areas difficult to access. Previous work based on change detection and using approaches that automatically map distinct landslide events exploiting remote sensing data has shown promising results. The aim of this study was to test the applicability of multi-temporal change indices derived from Sentinel-2 (S2) for landslide detection for two landslide-prone study sites in Italy and China: South Tyrol and Longnan, respectively.</p><p>The methodical approach was built upon a change vector analysis applied to annual cloud-free S2-composites at 10m spatial resolution to extract land-cover disturbances. Landslide areas in the time period 2015-2019 were analyzed on the basis of already known landslide location points, downslope-oriented moving windows and supervised classifications using the Receiver Operating Characteristic (ROC) curve.  Subsequently, time-series analysis was applied to the detected landslide-affected areas and to derive temporal breakpoints (i.e. the timing of the landslide occurrence). Finally, applying a multi-temporal revegetation analysis, we accounted for false positives originating from agricultural activities or artefacts on single images. Our findings highlight that out of the 67 already known landslide locations in South Tyrol, only 9 (13.4%) were detectable by means of S2 data. Major challenges resulted from similar spectral characteristics of landslides and other land cover disturbances (especially tree logging). However, larger landslides were detectable both spatially and temporally by means of the multi-temporal change detection approach. By applying a quantitative accuracy assessment for the independent test site in Longnan, China, we are currently assessing the transferability and suitability of the developed approach for efficient spatial-temporal landslide mapping over large areas.</p>

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.

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