A novel approach for object-based change image generation using multitemporal high-resolution SAR images

Object-based change detection offers a unique approach for high-resolution images to capture meaningful detailed change information while suppressing noise in change detection results. In this approach, mean intensities of objects are commonly used as a feature and images comparison is carried out based on simple mathematical operations such as ratioing. The strong intensity variations within an object - a consequence of high spatial resolution - combined with synthetic aperture radar (SAR) image speckle degrade the accuracy of object mean intensity estimate, and consequently, affect the quality of the estimated object-based change image. A change quantification approach that takes into account the characteristics of high-resolution SAR images, that is, SAR speckle and the strong intensity variation, is proposed. By descending to the pixel level, a new representation of change data (i.e. the change signal) is proposed. With this representation, change quantification boils down to measuring the roughness of the change signal. Two techniques to assess the intensity of change at the object-level, based on Fourier and wavelet transforms (WT) of the change signal, are proposed. Their main advantages lie in their ability to capture the dominant change behaviour of the object, while being insusceptible to irrelevant disturbances. The proposed approach is evaluated using two multitemporal data sets of TerraSAR-X images acquired over Beijing and Shanghai. The qualitative and quantitative analyses of the results demonstrate the superior discrimination power of the proposed change variables compared with the object-based modified ratio (MR) and the absolute log ratio (LR) images.