Claudia Zoppetti

Nonparametric Change Detection in Multitemporal SAR Images Based on Mean-Shift Clustering

A nonparametric method for unsupervised change detection in multipass synthetic aperture radar (SAR) imagery is described. The method relies on a novel feature capturing the structural change between two SAR images and is robust to the statistical change that may be originated by speckle and coregistration inaccuracies. The proposed method starts from the scatterplot of the amplitude levels in the two images and applies the mean-shift (MS) algorithm to find the modes of the underlying bivariate distribution. If we assume that the two images have been preliminarily coregistered and calibrated on one another, then all the modes lying outside the main diagonal correspond to the structural changes across the two observations. The value of the probability density function (PDF) in any of the off-diagonal modes found by the MS algorithm is translated into a value of conditional information. This value is assigned to all image pixels generating the corresponding cluster in the scatterplot. Thus, a feature is obtained on a per-pixel basis. Experimental results on simulated changes and true SAR images acquired by the COSMO-SkyMed satellite constellation show that the proposed feature exhibits significantly better discrimination capability than the classical log-ratio (LR). Advantages over a preliminary version of the method without MS regularization and over another nonparametric method based on Kullback-Leibler divergence are also demonstrated. The method is robust when it is applied to SAR images with different acquisition angles, whose effects are deemphasized compared to the actual scene changes.

Robust unsupervised nonparametric change detection of SAR images

This paper presents an unsupervised nonparametric method for change detection in multitemporal synthetic aperture radar (SAR) imagery. The proposed method relies on a novel feature capable of capturing the structural changes between the two images and discarding almost completely the statistical changes due to speckle patterns or co-registration inaccuracies. This feature utilizes the scatterplots of the amplitude levels in the two SAR images and applies a fast version of the mean-shift (MS) algorithm to find the modes of the underlying bivariate distribution. The value of the probability density function (PDF) is translated to a value of conditional information and given to all image pixels originating such modes. Experimental results have been carried out with simulated changes and true SAR images acquired by the COSMO-SkyMed satellite constellation. The proposed feature exhibits significantly better discrimination capability than both the classical log-ratio (LR) and is particularly robust if applied to SAR images having different processing and/or acquisition angles.

Change Detection in Multitemporal Images Through Single- and Multi-scale Approaches

This chapter presents an analysis of the current status and the challenges in change detection techniques for the analysis of multitemporal SAR images. Algorithms and methods based on validated statistical models for SAR data are investigated, which adopt advanced information-theoretic and multi-scale signal-processing methodologies. After a brief review of the recent literature on general change detection methods, the chapter investigates the specific problem of change detection in SAR images. The main properties of the change detection problem in SAR images are explored and discussed. Then, recent change detection techniques for high-resolution (HR) and very high-resolution (VHR) SAR data are presented and critically analyzed from the theoretical viewpoint. Finally, examples of application of these techniques to real problems are presented by using simulated image pairs and Enhanced Spotlight COSMO-SkyMed images.

A segmentation-based approach to SAR change detection and mapping

The potentials of SAR sensors in change detection applications have been recently strengthened by the high spatial resolution and the short revisit time provided by the new generation SAR-based missions, such as COSMO- SkyMed, TerraSAR-X, and RadarSat 3. Classical pixel-based change detection methods exploit first-order statistics variations in multitemporal acquisitions. Higher-order statistics may improve the reliability of the results, while plain object-based change detection are rarely applied to SAR images due to the low signal-to-noise ratio which characterizes 1-look VHR SAR image products. The paper presents a hybrid approach considering both a pixel-based selection of likely-changed pixels and a segmentation-driven step based on the assumption that structural changes correspond to some clusters in a multiscale amplitude/texture representation. Experiments on simulated and true SAR image pairs demonstrate the advantages of the proposed approach.

Computer-Assisted Evaluation of Retinal Vessel Diameter in Retinitis Pigmentosa

Purpose: The evaluation of retinal vessel attenuation is very subjective and not sufficiently reliable in patients with retinitis pigmentosa (RP). We tested semiautomatic software capable of obtaining real-time measurements of vessel diameter. Methods: Retinal vessel diameter was calculated in 25 RP subjects and in 20 healthy controls. The Mann-Whitney test was used to compare the average values of RP patients with those of controls and subgroups of RP patients with different clinical features. Results: The retinal vessel diameter was significantly smaller in RP patients than in controls (p < 0.001). In particular, vessel diameters were smaller in older subjects, in patients with worse ERG responses, and in patients with more severe visual field loss. Conclusions: Computer-assisted analysis of retinal fundus pictures may be helpful in the diagnosis of RP and in monitoring disease progression.

Detail-preserving change detection from amplitude SAR images

This paper presents a modified version of the information-theoretic feature in [1] for change detection in multitemporal synthetic aperture radar (SAR) imagery. The proposed method is capable of capturing small-area structural changes between the two images, by maintaining at the same time the capability of rejecting statistical changes due to speckle patterns or co-registration inaccuracies. This improvement is obtained by bootstrapping the original procedure by means of an adaptive preliminary selection of potentially changed pixels driven by the logarithm of the pixel ratio. Experimental results have been carried out on true SAR images acquired by the COSMO-SkyMed constellation.

Optimizing SAR change detection based on log-ratio features

The paper investigates how to optimize the performances of unsupervised log-ratio based change detection algorithms for two-date 1-look amplitude SAR images. The usual approach of pre-processing the SAR images at different dates with state-of-the-art despeckling filters is critically discussed. Those adaptive filters are very efficient, also for the challenging case of 1-look images, for speckle reduction of single-date image data and then for providing reliable classification, detection, or parameter estimation results. However, they are not able to ease the discrimination of statistical from structural changes in 1-look SAR images for which reliable point-target detection is nearly impractical. A simple, yet very effective, multiscale method for change detection and automatic change mapping is proposed and tested on simulated 1-look SAR images. The adopted pre-processing is based on guided image filtering with different window sizes. It improves the detection of changed regions without introducing any geometrical constraint and significantly reduces the false alarm rate. Experimental tests on simulated SAR images and Spotlight COSMO-SkyMed images demonstrate the advantages of the proposed algorithm.

Range Image Processing for Real Time Hospital-Room Monitoring

In this paper we describe a robust and movable real-time system, based on range data and 2D image processing, to monitor hospital-rooms and to provide useful information that can be used to give early warnings in case of dangerous situations. The system auto-configures itself in real-time, no initial supervised setup is necessary, so is easy to displace it from room to room, according to the effective hospital needs. Night-and-day operations are granted even in presence of severe occlusions, by exploiting the 3D data given by a Kinect\(^\copyright \) sensor. High performance is obtained by a hierarchical approach that first detects the rough geometry of the scene. Thereafter, the system detects the other entities, like beds and people. The current implementation has been preliminarily tested at “Le Scotte” polyclinic hospital in Siena, and allows a 24 h coverage of up to three beds by a single Kinect\(^\copyright \) in a typical room.

Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures

In the framework of the monitoring of structures and infrastructures from environmental disasters, the COSMO-SkyMed constellation has a huge potential, thanks to up to metric spatial resolution, short revisit time, and the day/night all-weather acquisition capability ensured by SAR. This paper focuses on the scientific results of the project “Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures,” funded by the Italian Space Agency. Several change-detection, data-fusion, and feature-extraction techniques, which were developed and experimentally validated in the project for COSMO-SkyMed imagery and for their integration with other data sources (including very high resolution optical data), are described and examples of processing results are discussed.

Efficient nonparametric extraction of urban footprint from VHR SAR images

Very-High-Resolution Synthetic Aperture Radar (VHR-SAR) images are of particular interest to characterize and monitor urban areas at a global scale. While automatic urban footprint extraction from SAR images can be theoretically performed at very high spatial and temporal resolutions, in practice it requires a huge amount of processing time and memory resources for a global coverage. The paper presents a fast procedure for 1mresolution Spotlight mode scene analysis which shows limited memory requirements and robustness to one-look speckle disturbance. The proposed method adopts a multiscale approach, with a radiometrically scalable scheme. Experimental results with objective assessment on Spotlight Cosmo-SkyMED images are presented and validated on reference urban classes provided by the Copernicus Urban Atlas 2012 of the European Space Agency.