Adaptive Multitemporal SAR Image Filtering Based on the Change Detection Matrix

• Published in: IEEE geoscience and remote sensing letters Vol. 11; no. 10; pp. 1826 - 1830
• Main Authors: Thu Trang Le, Atto, Abdourrahmane M., Trouvé, Emmanuel, Nicolas, Jean-Marie
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-10-2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); IEEE - Institute of Electrical and Electronics Engineers
• Subjects: Change detection; coefficient of variation (CV); Computer Science; Filtering; Image Processing; Indexes; Noise; Remote sensing; Speckle; Synthetic aperture radar; synthetic aperture radar (SAR) image time series; temporal adaptive filtering; Time series analysis; synthetic aperture radar (SAR) image time series; Change detection; coefficient of variation (CV); temporal adaptive filtering; change detection; SAR image time series
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2014.2311663

This letter presents an adaptive filtering approach of synthetic aperture radar (SAR) image times series based on the analysis of the temporal evolution. First, change detection matrices (CDMs) containing information on changed and unchanged pixels are constructed for each spatial position over the time series by implementing coefficient of variation (CV) cross tests. Afterward, the CDM provides for each pixel in each image an adaptive spatiotemporal neighborhood, which is used to derive the filtered value. The proposed approach is illustrated on a time series of 25 ascending TerraSAR-X images acquired from November 6, 2009 to September 25, 2011 over the Chamonix-Mont-Blanc test-site, which includes different kinds of change, such as parking occupation, glacier surface evolution, etc.