Abstract
In the framework of synthetic aperture radar (SAR) systems, current satellite missions make it possible to acquire images at very high and multiple spatial resolutions with short revisit times. This scenario conveys a remarkable potential in applications to, for instance, environmental monitoring and natural disaster recovery. In this context, data fusion and change detection methodologies play major roles. This paper proposes an unsupervised change detection algorithm for the challenging case of multimodal SAR data collected by sensors operating at multiple spatial resolutions. The method is based on Markovian probabilistic graphical models, graph cuts, linear mixtures, generalized Gaussian distributions, Gram–Charlier approximations, maximum likelihood and minimum mean squared error estimation. It benefits from the SAR images acquired at multiple spatial resolutions and with possibly different modalities on the considered acquisition times to generate an output change map at the finest observed resolution. This is accomplished by modeling the statistics of the data at the various spatial scales through appropriate generalized Gaussian distributions and by iteratively estimating a set of virtual images that are defined on the pixel grid at the finest resolution and would be collected if all the sensors could work at that resolution. A Markov random field framework is adopted to address the detection problem by defining an appropriate multimodal energy function that is minimized using graph cuts.
Highlights
Multitemporal image analysis represents a powerful source of information in Earth observation, especially in applications to environmental monitoring and disaster management
Each stripmap image was composed of 1460 × 1140 pixels captured at a resolution of 5 m, while the polarimetric channels were composed of 365 × 285 pixels at a resolution of 20 m
A preliminary visual inspection of the multitemporal images pointed out that the finer resolution stripmap data were poorly informative for the discrimination of one of the two types of change, while both changes were apparent in the polarimetric image pair, yet with obviously coarser spatial detail
Summary
Multitemporal image analysis represents a powerful source of information in Earth observation, especially in applications to environmental monitoring and disaster management. SAR sensors allow one or a few data features (e.g., one amplitude or intensity with a single-polarization SAR or up to four polarimetric channels with a polarimetric SAR-PolSAR-instrument) to be acquired, and these measurements are affected by speckle, which acts as a multiplicative noise-like phenomenon [9] These issues often reduce the capability to discriminate changed and unchanged areas as compared to the case of multispectral and hyperspectral imagery [3,10]. While ratioing and log-ratioing are typical choices for change detection with SAR data, other feature extraction strategies are generally used for change detection with multi- and hyper-spectral imagery, because of the different data and noise statistics These strategies include image differencing [5], change vector analysis [2], the Reed–Xiaoli (RX) detector and its extensions [6,7] and spectrally-segmented linear predictors [8], among others
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