1,721,111 research outputs found
Corrections to: Regularization of SAR tomography for 3-D height reconstruction in urban areas (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2019) 12:2 (648–659) DOI: 10.1109/JSTARS.2018.2889428)
No full textCorrections have been made to author affiliations in the paper, Regularization of SAR Tomography for 3-D Height Reconstruction in Urban Areas, (Aghababaee, et al.), IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 12, no. 2, pp. 648-659, Feb. 2019
Corporeità, movimento e benessere: il potenziale educativo di itinerari motorio-sportivi inclusivi giovanili.
Full text linkOver the past few decades, the adoption of an inclusive approach to education has stimulated a reflection on the educational value of body and movement within teaching-learning process in order to break down all barriers to learning and promote the full participation of young people to school activities. Indeed, body and movement represent an important didactic “medium” for developing individualized and personalized learning paths that take into account the specific needs and characteristics of students thus contributing to their global and harmonious developmen
Analysis on the Building of Training Dataset for Deep Learning SAR Despeckling
No full textIn the framework of deep learning for synthetic aperture radar (SAR) speckle reduction, the methods presented in the literature mainly focus on the definition of new architectures and cost functions for better catching and preserving the properties of a real SAR image. The achieved results are interesting and promising but with many left open issues. The main critical problem, shared by all the methods, is the construction of a training dataset. This is due to the lack of a noise-free reference. In this work, a comparison among different training approaches (synthetic, multitemporal, and hybrid) is carried out in order to analyze their benefits and drawbacks. Four convolutional neural network (CNN)-based methods have been trained with the three different datasets for their assessment. Results on real SAR images have been carried out showing the peculiarities of each training approach
Multi-Objective CNN-Based Algorithm for SAR Despeckling
No full textDeep learning (DL) in remote sensing has nowadays become an effective operative tool: it is largely used in applications, such as change detection, image restoration, segmentation, detection, and classification. With reference to the synthetic aperture radar (SAR) domain, the application of DL techniques is not straightforward due to the nontrivial interpretation of SAR images, especially caused by the presence of speckle. Several DL solutions for SAR despeckling have been proposed in the last few years. Most of these solutions focus on the definition of different network architectures with similar cost functions, not involving SAR image properties. In this article, a convolutional neural network (CNN) with a multi-objective cost function taking care of spatial and statistical properties of the SAR image is proposed. This is achieved by the definition of a peculiar loss function obtained by the weighted combination of three different terms. Each of these terms is dedicated mainly to one of the following SAR image characteristics: spatial details, speckle statistical properties, and strong scatterers identification. Their combination allows balancing these effects. Moreover, a specifically designed architecture is proposed to effectively extract distinctive features within the considered framework. Experiments on simulated and real SAR images show the accuracy of the proposed method compared with the state-of-art despeckling algorithms, both from a quantitative and qualitative point of view. The importance of considering such SAR properties in the cost function is crucial for correct noise rejection and details preservation in different underlined scenarios, such as homogeneous, heterogeneous, and extremely heterogeneous
Ratio-based nonlocal anisotropic despeckling approach for sar images
No full textAlthough the first filtering algorithms have been proposed more than 30 years ago, despeckling of synthetic aperture radar images is still an open issue. A new boost has been provided by nonlocal (NL) means filters. The idea of NL filters is to move from the exploitation of spatial neighboring pixels to the exploitation of similar pixels found across the image. The difference between the NL algorithms is mainly related to the definition of the similarity between pixels and how similar pixels are exploited in the restoration process. Generally, to define the similarity, the patches are adopted. In this paper, a new similarity criterion for selecting similar pixels is presented. It is based on the definition of the ratio patch between the patch containing the pixel to be restored and the patch containing a candidate similar pixel. If the two pixels are similar, it is expected that the corresponding ratio patch will follow a specific statistical distribution. A modified version of the Kolmogorov-Smirnov distance is introduced to decide whether the statistical distribution of the ratio patch follows the expected one. To reduce the possible artifacts, anisotropy is exploited. Considering the proposed approach, the designed algorithm turns to be unbiased, able to provide the restored solution without any thresholding procedure, in which the tuning is substantially unsupervised and able to work with both single-look and multilook images. The algorithm has been tested on different simulated and real data. Qualitative and quantitative analyses validate the proposed approach, showing very good despeckling capabilities
Polarization analysis of the impact of temporal decorrelation in synthetic aperture radar (SAR) tomography
No full textAfter almost two decades of long investigations into 3D imaging of natural environments, synthetic aperture radar (SAR) tomography (TomoSAR) is now at an operational level. Yet, a major problem that limits the potential of TomoSAR is related to the temporal decorrelation of natural scatterers during multitemporal multibaseline data acquisition. In this paper, a comparative investigation into the effect of temporal decorrelation between employed polarizations is presented. A particular focus is put on practical and statistical analysis of the dispersion of polarimetric vertical reflectivity in the presence of temporal decorrelation. The analysis is based on the synthesis of all feasible polarimetric responses of a given scatterer from its measurements of a linear orthonormal basis. Such an analysis offers a comprehension of the expected level of temporal decorrelation in TomoSAR focusing with respect to employed polarization. The analysis was performed by simulating temporal decorrelation with different terms, depending on the vertical structure and polarization, which are important aspects in a forest scenario. Moreover, the experiment was extended to a P-band dataset relative to the forest site of Remningstorp, Sweden, which was acquired through the German Aerospace Center's experimental SAR (E-SAR) airborne system in the framework of the European Space Agency (ESA) campaign BioSAR
A CNN-based model for pansharpening of worldview-3 images
No full textFusing a multispectral image with a co-registered higher resolution single panchromatic band, provided by any multiresolution satellite systems, to rise the resolution of the former to that of the latter is known as pansharpening, and can be regarded as a guided super-resolution problem. Recently the use of convolutional neural networks (CNNs) has been extended to the pansharpening problem achieving state-of-the-art performance. Following this research line, the objective of this work was two-fold: provide a trained CNN model fitted to a specific sensor (WorldView-3) and explore a range of architectural configurations varied in both width and depth, seeking for the optimal one. Numerical and visual results show that the proposed solution compares favourably against reference methods
Assessment of GPU-Based Enhanced Wiener Filter on Very High Resolution Images
No full textDespeckling of Synthetic Aperture Radar (SAR) images is still an open issue in the remote sensing community. Many filtering approaches have been adopted by the scientific community in order to reduce speckle noise effects including, such as spatial-based and non-local techniques. Mainly all existing approaches present some limitation in terms of accuracy or computational time or edge preservation. Recently a frequency-based solution presented as an improvement of the classical Wiener filter was proposed in order to limit the previous mentioned critical issues. The algorithm was already tested on both simulated and Sentinel-1 data. In this paper, we assess and validate the effectiveness of the proposed solution exploiting different very high-resolution (VHR) data such as, COSMO-SkyMed and RADARSAT SAR images. The results are interesting proving that the developed filter can effectively deal with the case of different VHR SAR images and can provide accurate results within a very limited time
Differential SAR Tomography Reconstruction Robust to Temporal Decorrelation Effects
No full textTemporal decorrelation is one of the major problems in synthetic aperture radar (SAR) tomography (TomoSAR) of a natural environment that leads to blurring and spreading in focused image space. In the context of spatiotemporal focusing using the multioral multi-baseline (MB) SAR data, a model-based differential TomoSAR is employed. Along this and with the aim of temporal decorrelation-robust focusing, a differential tomography framework based on generalized Capon estimator is investigated. The method can cope with temporal decorrelation of the distributed environment by spatiotemporal focusing with optimal bandwidth of the distributed signal. In addition, the method employs an additional parameter for coherence channel balancing in the model of generalized Capon that benefits from it in characterizing the spatiotemporal backscattering by mitigating the inconsistency between channels. The analysis is performed with a realistic simulation of temporal decorrelation in the presence of different decorrelation sources and taking into account the dependence on the vertical structure of the forested area. Effectiveness of the proposed framework has been assessed on both simulated and real data sets by evaluation and characterization of the canopy and under foliage ground in terms of deviation between the estimated covariance matrix and one of the generalized TomoSAR models
Ratio-Based Similarity Criteria for Polarimetric SAR Image
No full textDealing with multi-look polarimetric synthetic aperture radar (PolSAR) images requires averaging several independent looks to generate a sample covariance matrix of similar target scattering vectors. Along this, estimation of optimal similarity between target scattering vectors is still an open issue. In the literature, this intrinsic task has been mainly addressed in the information-based, geometric-based and detection-based frameworks. However, the derived measures mainly rely on the model assumption such as fully developed speckle and circular complex Gaussian distribution of the scattering vectors, which may not be held in high-resolution images of urban environments. To cope with this possible issue a discriminative model-free measure is proposed, where the similarity of target scattering is computed in the framework of non-local or patch based algorithm. In particular, the discriminative measure is constructed using the ratio between two pre-estimated covariance matrices of the scattering vectors. Experimental validation of the proposed measure is provided using ALOS-PALSAR image and compared with existing criterions in the literature
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