Abstract
Despite the significant progress that was achieved throughout the recent years, to this day, automatic change detection and classification from synthetic aperture radar (SAR) images remains a difficult task. This is, in large part, due to (a) the high level of speckle noise that is inherent to SAR data; (b) the complex scattering response of SAR even for rather homogeneous targets; (c) the low temporal sampling that is often achieved with SAR systems, since sequential images do not always have the same radar geometry (incident angle, orbit path, etc.); and (d) the typically limited performance of SAR in delineating the exact boundary of changed regions. With this paper we present a promising change detection method that utilizes SAR images and provides solutions for these previously mentioned difficulties. We will show that the presented approach enables automatic and high-performance change detection across a wide range of spatial scales (resolution levels). The developed method follows a three-step approach of (i) initial pre-processing; (ii) data enhancement/filtering; and (iii) wavelet-based, multi-scale change detection. The stand-alone property of our approach is the high flexibility in applying the change detection approach to a wide range of change detection problems. The performance of the developed approach is demonstrated using synthetic data as well as a real-data application to wildfire progression near Fairbanks, Alaska.
Highlights
Introduction and BackgroundMulti-temporal images acquired by optical [1] or radar remote sensing sensors [2] are routinely applied to the detection of changes on the Earth’s surface
As we are interested in developing a flexible change detection method that can be applied to a wide range of change situations, we are interested in preserving the original image resolution when filtering the data
An is obtained after erosion has been initially applied to the original image range of data from different change detection projects, we found that most consistent results were paper, we used a fixed square shape
Summary
Multi-temporal images acquired by optical [1] or radar remote sensing sensors [2] are routinely applied to the detection of changes on the Earth’s surface. To fully automate the classification operations required in the multi-scale change detection approach (limitation (C)), we model the probability density function of the change map at each resolution level as a sum of two or more Gaussian distributions (similar to [11]). To accurately delineate the boundary of the changed region (limitation (D)), we utilize measurement level fusion techniques These techniques used the posterior probability of each class at each multi-scale image to compose a final change detection map. We tested five different techniques including (i) product rule fusion; (ii) sum rule fusion; (iii) max rule fusion; (iv) min rule fusion; and (v) majority voting rule fusion These individual methods are combined in a multi-step change detection approach consisting of a pre-processing step, a data enhancement and filtering step, and the application of the multi-scale change detection algorithm.
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