Change detection of satellite time series images based on spatial-temporal-spectral features

QIN Le; HE Peng; MA Yuzhong; LIU Jianqiang; YANG Bin

doi:10.6046/zrzyyg.2021351

2022 Vol. 34, No. 4

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Article navigation > Remote Sensing for Natural Resources > 2022 > 34(4): 105-112

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QIN Le, HE Peng, MA Yuzhong, LIU Jianqiang, YANG Bin. 2022. Change detection of satellite time series images based on spatial-temporal-spectral features. Remote Sensing for Natural Resources, 34(4): 105-112. doi: 10.6046/zrzyyg.2021351

Citation:

QIN Le, HE Peng, MA Yuzhong, LIU Jianqiang, YANG Bin. 2022. Change detection of satellite time series images based on spatial-temporal-spectral features. Remote Sensing for Natural Resources, 34(4): 105-112. doi: 10.6046/zrzyyg.2021351

Change detection of satellite time series images based on spatial-temporal-spectral features

1. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China；
2. School of Mechanical Engineering, University of South China, Hengyang 421000, China；
3. Shandong Provincial Institute of Land Surveying and Mapping, Jinan 250014, China；
4. Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China

Received Date: 21 October 2021

Revised Date: 15 December 2022

Publish Date: 27 December 2022

Abstract

Abstract

Compared with common dual-temporal satellite images, satellite time series images contain richer surface information and can alleviate the impact of foreign objects with the same spectrum and the same object with different spectra. Therefore, they play an important role in change detection. However, the change detection methods for satellite time series images are mostly based on pixels and ignore the spatial relationship between pixels and their surroundings. This causes noise in the change detection result. Accordingly, this study proposed a method of change detection based on spatial-temporal-spectral features(CDSTS) for satellite time series images. First, the temporal, spatial (textural and statistical), and spectral features of each pixel were extracted from Landsat time series images using a gray-level co-occurrence matrix and local statistical calculation methods. Then, anomalies of time series features were automatically screened according to the time series performance regularity of each pixel in different bands. These anomalies were then fused with the detection results of the continuous change detection and classification method (CCDC) to obtain high-precision changed/unchanged training sample points. Finally, the SVM classifier was trained using the training sample points and their corresponding spatial-temporal-spectral features for full graph classification. The results show that the CDSTS algorithm significantly outperforms the commonly used time series change detection algorithms CCDC and COLD (continuous monitoring of land disturbance) in terms of change detection precision, with the overall precision improved by 4.8 to 11.7 percentage points.
- Landsat /
- time series /
- change detection /
- textural feature

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References

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