Seven countries within the European Defence Agency (EDA) framework are joining effort in a four year project (2009-2013) on Detection in Urban scenario using Combined Airborne imaging Sensors (DUCAS). Data has been collected in a joint field trial including instrumentation for 3D mapping, hyperspectral and high resolution imagery together with in situ instrumentation for target, background and atmospheric characterization. Extensive analysis with respect to detection and classification has been performed. Progress in performance has been shown using combinations of hyperspectral and high spatial resolution sensors.
KEYWORDS: Interference (communication), Signal to noise ratio, Detection and tracking algorithms, RGB color model, Optical engineering, Sensors, Hyperspectral imaging, Cameras, Data modeling, Signal detection
A novel technique for anomalous change detection (ACD) in hyperspectral images is presented. The technique embeds a strategy robust to residual misregistration errors that typically affect data collected by airborne platforms. Furthermore, the proposed technique mitigates the negative effects due to random noise, by means of a band selection technique aimed at discarding spectral channels whose useful signal content is low compared to the noise contribution. Band selection is performed on a per-pixel basis by exploiting the estimates of the noise variance accounting also for the presence of the signal-dependent noise component. Real data collected by a new generation airborne hyperspectral camera on a complex urban scenario are considered to test the proposed method. Performance evaluation shows the effectiveness of the proposed approach with respect to a previously proposed ACD algorithm based on the same similarity measure.
In this work we propose two pixel-wise change detection techniques for unsupervised network infrastructure monitoring in SAR imagery applications. The first algorithm is inspired by a well known algorithm, named RX, proposed to deal with anomaly detection in optical images. The second algorithm is a statistical based procedure, which exploits a nonparametric approach for estimating the probability density function of the image pair. In order to test and validate the proposed methods, we analyze a spot light amplitude COSMO-SkyMed image pair at one-meter spatial resolution acquired on a complex urban scenario. Experimental results obtained on the available dataset are presented and discussed.
A novel technique for anomalous change detection in hyperspectral images is presented. It adaptively measures the spectral distance between corresponding pixels in multi-temporal images by exploiting the local estimates of the signal to noise ratio for each spectral component of the pixel under test. Different metrics have been compared, based on multidimensional angular distance. Results obtained by applying the new algorithm to real data are presented and discussed. Performance evaluation highlighted the effectiveness of the proposed approach with respect to traditional methods, resulting in a consistent improvement of both the probability of detection of changes and the capability of suppressing the background.
In this work, we focus on Anomalous Change Detection (ACD), whose goal is the detection of small changes occurred between two hyperspectral images (HSI) of the same scene. When data are collected by airborne platforms, perfect registration between images is very difficult to achieve, and therefore a residual mis-registration (RMR) error should be taken into account in developing ACD techniques. Recently, the Local Co-Registration Adjustment (LCRA) approach has been proposed to deal with the performance reduction due to the RMR, providing excellent performance in ACD tasks. In this paper, we propose a method to estimate the first and second order statistics of the RMR. The RMR is modeled as a unimodal bivariate random variable whose mean value and covariance matrix have to be estimated from the data. In order to estimate the RMR statistics, a feature description of each image is provided in terms of interest points extending the Scale Invariant Feature Transform (SIFT) algorithm to hyperspectral images, and false matches between descriptors belonging to different features are filtered by means of a highly robust estimator of multivariate location, based on the Minimum Covariance Determinant (MCD) algorithm. In order to assess the performance of the method, an experimental analysis has been carried out on a real hyperspectral dataset with high spatial resolution. The results highlighted the effectiveness of the proposed approach, providing reliable and very accurate estimation of the RMR statistics.
We propose a novel method to estimate the first- and second-order statistics of the residual misregistration noise (RMR), which severely affects the performance of anomalous change detection techniques. Depending on the specific distribution of the RMR, the estimates allow for precisely defining the size of the uncertainty window, which is crucial when dealing with misregistration noise, as in the local coregistration adjustment approach. The technique is based on a sequential strategy that exploits the well-known scale-invariant feature transform (SIFT) algorithm cascaded with the minimum covariance determinant algorithm. The SIFT procedure was originally developed to work on gray-level images. The proposed method adapts the SIFT procedure to hyperspectral images so as to exploit the complementary information content of the numerous spectral channels, further improving the robustness of the outliers filtering by means of a highly robust estimator of multivariate location. The approach has been tested on different real hyperspectral datasets with very high spatial resolution. The analysis highlighted the effectiveness of the proposed strategy, providing reliable and very accurate estimation of the RMR statistics.
The EDA project "Detection in Urban scenario using Combined Airborne imaging Sensors" (DUCAS) is in progress.
The aim of the project is to investigate the potential benefit of combined high spatial and spectral resolution airborne
imagery for several defense applications in the urban area. The project is taking advantage of the combined resources
from 7 contributing nations within the EDA framework. An extensive field trial has been carried out in the city of
Zeebrugge at the Belgian coast in June 2011. The Belgian armed forces contributed with platforms, weapons, personnel
(soldiers) and logistics for the trial. Ground truth measurements with respect to geometrical characteristics, optical
material properties and weather conditions were obtained in addition to hyperspectral, multispectral and high resolution
High spectral/spatial resolution sensor data are used for detection, classification, identification and tracking.