1,721,030 research outputs found
Robust band-dependent spatial-detail approaches for panchromatic sharpening
No full textPansharpening refers to the fusion of a multispectral (MS) image with a finer spectral resolution but coarser spatial resolution than a panchromatic (PAN) image. The classical pansharpening problem can be dealt with component substitution or multiresolution analysis techniques. One of the most notable approaches in the former class is the band-dependent spatial-detail (BDSD) method. It has been shown state-of-the-art performance, in particular, when the fusion of four band data sets is addressed. However, new sensors, such as the WorldView-2/-3 ones, usually acquire MS images with more than four spectral bands to be fused with the PAN image. The BDSD method has shown limitations in performance in these cases. Thus, in this paper, several BDSD-based approaches are provided to solve this issue getting a robustness of the BDSD with respect to the spectral bands to be fused. The experimental results conducted both at reduced and at full resolutions on four real data sets acquired by the IKONOS, the QuickBird, the WorldView-2, and the WorldView-3 sensors demonstrate the validity of the proposed approaches against the benchmark
Pansharpening
No full textWith the increasing of satellite missions, data fusion is growing attention in recent years. Pansharpening, which stands for panchromatic (PAN) sharpening, is a particular data fusion problem based on the enhancement of the spatial resolution of a multispectral image, thanks to the use of PAN data. This chapter is focused on pansharpening and, in particular, basic notions about (1) the main pansharpening approaches and their classification in three classes (i.e., multiresolution analysis, component substitution, and new generation methods), (2) the assessment procedures for pansharpened products, and (3) the extension of pansharpening to deal with the fusion of hyperspectral data (i.e., hyperspectral pansharpening)
Joint probabilistic data association tracker for extended target tracking applied to X-band marine radar data
No full textX-band marine radar systems are flexible and low-cost tools for monitoring multiple targets in a surveillance area. Although they may suffer from several sources of interference, e.g., sea clutter, they can provide high-resolution measurements in both space and time. Such features offer the opportunity to get accurate information not only about the target kinematics, i.e., positions and velocities, as other conventional radars, but also about the targets' extents. This research area is named extended target tracking (ETT). In this paper, we propose a signal processing chain composed by a detector and a joint probabilistic data association (JPDA) tracker to handle the problem of multiple ETT and to jointly estimate both the targets' kinematics and their sizes, i.e., length and width. The performance assessment is conducted on real data acquired by an X-band marine radar located in the Gulf of La Spezia, Italy. The experimental results demonstrate the ability of the processing chain to reach high performance with a limited computational burden
Knowledge-based ship tracking applied to HF surface wave radar data
No full textIn recent years, low-power high-frequency surface-wave radars have received significant attention thanks to their over-the-horizon coverage capability and the continuous-time operation mode. These radars have become effective long-range early-warning tools for maritime situational awareness applications. In this paper a knowledge-based multi-target tracking algorithm is described. The advantages in using a prior information on ship traffic are assessed exploiting real data acquired by two high-frequency surface-wave radars. The outcomes confirm the ability of the proposed approach to better follow targets with a time-on-target increment up to 30% with respect to existing methods. A reduction of the track fragmentation up to 20% is also observed
Variable structure interacting multiple model algorithm for ship tracking using HF surface wave radar data
No full textThese last decades spawned a great interest towards low-power High-Frequency (HF) Surface-Wave (SW) radars for ocean remote sensing. By virtue of their over-the-horizon coverage capability and continuous-time mode of operation, these sensors are also effective long-range early-warning tools in maritime situational awareness applications. In this paper we show how it is possible to take advantage of a priori information on traffic by the means of a knowledge-based multi-target tracking algorithm, demonstrating that the tracking stage can be enhanced by combining on-line data from the HFSW radar with ship traffic information. A significant improvement of the proposed procedure, in terms of system performance, is demonstrated in comparison with the state-of-the-art approach recently presented in the literature. The main benefit of our approach is the ability to better follow targets without increasing the false alarm rate. The ability to follow targets can be over 30% better than existing methods. The proposed approach also exhibits a reduction of the track fragmentation. Average gains between the 13% and the 20% are observed
Pansharpening: Context-Based Generalized Laplacian Pyramids by Robust Regression
No full textPansharpening refers to the combination of panchromatic (PAN) and multispectral (MS) images, designed to obtain a fused product retaining the fine spatial resolution of the former and the high spectral content of the latter. One of the most popular and successful approaches to pansharpening is the method known as context-based generalized Laplacian pyramid, which requires as a key ingredient for the estimation of the so-called injection coefficients. In this article, we propose the adoption of robust techniques for the estimation of the injection coefficients and detection strategies to select the clusters for which robust regression is needed, providing a suitable balancing between fusion performance and computational burden. Experimental results conducted on five real data sets acquired by the sensors QuickBird, WorldView-3, and WorldView-4, show the superiority of the proposed method with respect to current state-of-The-Art pansharpening techniques
Knowledge-Based Multitarget Ship Tracking for HF Surface Wave Radar Systems
No full textThese last decades spawned a great interest toward low-power high-frequency (HF) surface-wave (SW) radars for ocean remote sensing. By virtue of their over-the-horizon coverage capability and continuous-time mode of operation, these sensors are also effective long-range early warning tools in maritime situational awareness applications providing an additional source of information for target detection and tracking. Unfortunately, they also exhibit many shortcomings that need to be taken into account, and proper algorithms need to be exploited to overcome their limitations. In this paper, we develop a knowledge-based (KB) multitarget tracking methodology that takes advantage of a priori information on the ship traffic. This a priori information is given by the ship sea lanes and by their related motion models, which together constitute the basic building blocks of a variable structure interactive multiple model procedure. False alarms and missed detections are dealt with using a joint probabilistic data association rule and nonlinearities are handled by means of the unscented Kalman filter. The KB-tracking procedure is validated using real data acquired during an HF-radar experiment in the Ligurian Sea (Mediterranean Sea). Two HFSW radar systems were operated to develop and test target detection and tracking algorithms. The overall performance is defined in terms of time-on-target, false-alarm rate (FAR), track fragmentation (TF), and accuracy. A full statistical characterization is provided using one month of data. A significant improvement of the KB-tracking procedure, in terms of system performance, is demonstrated in comparison with a standard joint probabilistic data association tracker recently proposed in the literature to track HFSW radar data. The main improvement of our approach is the better capability of following targets without increasing the FAR. This increment is much more evident in the region of low FAR, where it can be over the 30% for both the HFSW radar systems. The KB-tracking exhibits on average a reduction of the TF of about the 20% and the 13% of the utilized HFSW-radar systems
Extended target tracking using joint probabilistic data association filter on X-band radar data
No full textX-band Marine radar systems are low-cost tools for monitoring multiple targets in a surveillance area. Although they may suffer from several sources of interference, high resolution measurements in both space and time can be provided. Such features offer the opportunity to get accurate information not only about the targets' kinematics, as other conventional sensors, but also about the targets' extent. In this paper, a signal processing chain composed by a detector and a joint probabilistic data association tracker is proposed to address the problem of tracking using X-band Marine radar data. Estimations of both the targets' kinematics, i.e. positions and velocities, and length and width, are provided. The performance assessment, conducted on real data acquired by an X-band Marine radar located in the Gulf of La Spezia, Italy, demonstrates the ability of the processing chain to obtain high tracking performance with a limited computational burden
Extended target tracking applied to X-band marine radar data
No full textX-band marine radar systems are flexible and low-cost tools for monitoring multiple targets in a surveillance area. They are able to provide high resolution measurements in both space and time. Such features offer the opportunity to get accurate information not only about the targets' kinematics but also about the targets' extents. The tracking of these kinds of data is usually called extended target tracking (ETT). In this paper, we propose a signal processing chain mainly composed by a pixel-wise detector and a joint probabilistic data association tracker to handle the problem of multiple ETT. The performance assessment is conducted on real data acquired by an X-band marine radar located in the Gulf of La Spezia, Italy. The experimental results demonstrate that the processing chain is able to reach high performance with a limited computational burden
Intersensor statistical matching for pansharpening: Theoretical issues and practical solutions
No full textIn this paper, the authors investigate the statistical matching of the panchromatic (Pan) image to the multispectral (MS) bands, also known as the histogram matching, for the two main classes of pansharpening methods, i.e., those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods. Also, hybrid methods combining CS with MRA, like the widespread additive wavelet luminance proportional (AWLP), are investigated. It is shown that all spectral, spatial, and hybrid methods must perform a dynamics matching of the enhancing Pan to the individual MS bands for MRA or a combination of them (the component that shall be substituted) for CS. For hybrid methods, the problem is more complex and both types of histogram matching may be suitable. Such an intersensor balance may be either explicit or implicitly performed by the detailinjection model, e.g., the popular projective and multiplicative injection models. An experimental setup exploiting IKONOS and WorldView-2 data sets demonstrates that a correct histogram matching is the key to attain extra performance from established methods. As a first result of this paper, the AWLP method has been revisited and its performance significantly improved by simply performing the histogram matching of Pan to the individual MS bands, rather than to the intensity component, thereby losing the original proportionality feature
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