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

Jili Yuan,Fangjia Dou,Jingchuan Yao,Xiaolei Lv

doi:10.3390/rs11080926

Abstract

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.

Highlights

Change detection is a process of automatically analyzing and identifying the variation of Earth’s surface objects based on multitemporal remote sensing images acquired in the same region at different times [1,2,3]
TerraSAR-X is a commercial Earth observation synthetic aperture radar (SAR) satellite working at X-band that was jointly developed by the German government and industrial circles and was launched in 2007
Compared with other multitemporal change detection approaches, the method proposed in this paper can remarkably improve change detection and classification accuracy, mainly owing to two factors: (1) local information is exploited by extracting the statistical features of each patch according to the appropriate distribution of ground objects; (2) temporal grouping using a density-based clustering method called density-based clustering of application with noise (DBSCAN) can improve classification accuracy without a pre-determined cluster number and considerably reduce the runtime

Summary

Introduction

Change detection is a process of automatically analyzing and identifying the variation of Earth’s surface objects based on multitemporal remote sensing images acquired in the same region at different times [1,2,3]. Optical images have been widely used in remote sensing change detection due to the good interpretability and rich variety of band information [9]. In practice they are limited by various weather factors and by nighttime, especially for some urgent tasks, such as real-time damage investigation in disaster areas, which is usually accompanied by bad weather conditions. Change detection studies based on multi-temporal SAR images have recently been paid more attention by researchers [10,11,12,13]

Methods

Results

Discussion

Conclusion