scholarly journals Spatio-Temporal Change Detection of Ningbo Coastline Using Landsat Time-Series Images during 1976–2015

2017 ◽  
Vol 6 (3) ◽  
pp. 68 ◽  
Author(s):  
Xia Wang ◽  
Yaolin Liu ◽  
Feng Ling ◽  
Yanfang Liu ◽  
Feiguo Fang
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Temporal Change ◽  
Spatio Temporal ◽  
Time Series Images

Spatio-temporal detection of converting from cropland to forest/grassland on the Loess Plateau using all available Landsat time-series images

2021 ◽  
Author(s):  
Zhihui Wang ◽  
Peiqing Xiao
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Loess Plateau ◽  
Water Conservation ◽  
Series Data ◽  
Vegetation Types ◽  
Continuous Change ◽  
The Loess Plateau ◽  
Ndvi Time Series ◽  
Spatio Temporal

<p><strong>Conversion of cropland to forest/grassland has become a key ecological restoration measure on the Loess Plateau since 1999. Accurate mapping of the spatio-temporal dynamic information of conversion from cropland into forest/grassland is necessary for studying the effects of vegetation change on hydro-ecological process and soil and water conservation on the Loess Plateau, China. Currently, the accuracy of change detection of farmland and forest/grassland at 30-m scale in this area is seriously affected by insufficient temporal information from observations and irregular fluctuations in vegetation greenness caused by precipitation and human activities. In this study, an innovative method for continuous change detection of cropland and forest/grassland using all available Landsat time-series data. The period with vegetation coverage is firstly identified using normalized difference vegetation index (NDVI) time series. The intra-annual NDVI time series is then developed at a 1-day resolution based on linear interpolation and S-G filtering using all available NDVI data during the period when vegetation types are stable. Vegetation type change is initially detected by comparing the NDVI of intra-annual composites and the newly observed NDVI. Finally, the time of change and classification for vegetation types are determined using decision tree rules developed using a combination of inter-annual and intra-annual NDVI temporal metrics. Validation results showed that the change detection was accurate, with an overall accuracy of 88.9% ± 1.0%, and a kappa coefficient of 0.86, and the time of change was successfully retrieved, with 85.2% of the change pixels attributed to within a 2-year deviation.</strong></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.

scholarly journals Long-Term Stationary Object Detection Based on Spatio-Temporal Change Detection

2015 ◽  
Vol 22 (12) ◽  
pp. 2368-2372 ◽  
Author(s):  
Diego Ortego ◽  
Juan C. SanMiguel ◽  
Jose M. Martinez
Keyword(s):  
Change Detection ◽  
Object Detection ◽  
Temporal Change ◽  
Stationary Object ◽  
Spatio Temporal
Sign in / Sign up

Export Citation Format

Share Document