Object-based method for optical and SAR images change detection

Abstract

This study introduces an automatic method for change detection of multi-sensor remote-sensing images (e.g. optical and synthetic aperture radar (SAR) images). As object-based image analysis can effectively reduce the spurious changes and the sensitivity to registration, first, multi-date segmentation is employed to generate homogeneous image objects in spectral, spatial, and temporal, in order to weak the intensity variation effects of multi-sensor images. Then, modified fuzzy c-means (FCM) algorithms are employed to preliminarily classify optical and SAR images, and a criterion is defined using membership values of parcels to select the sample parcels for each class and image. Finally, a change detection principle, which takes statistical properties as the feature space, is introduced to detect changes between multi-sensor images. The experiment results verify that the proposed method is able to cope with optical and SAR images change detection.

Keywords

• remote sensing
• synthetic aperture radar
• geophysical image processing
• image segmentation
• feature extraction
• radar imaging
• object-based image analysis
• synthetic aperture radar
• optical aperture radar
• multisensor remote-sensing images
• sar images change detection
• optical images change detection
• change detection principle
• optical sar images
• multisensor images
• homogeneous image objects
• spurious changes