A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data

The automatic classification of hyperspectral data is made complex by several factors, such as the high cost of true sample labeling coupled with the high number of spectral bands, as well as the spatial correlation of the spectral signature. In this paper, a transductive collective classifier is proposed for dealing with all these factors in hyperspectral image classification. The transductive inference paradigm allows us to reduce the inference error for the given set of unlabeled data, as sparsely labeled pixels are learned by accounting for both labeled and unlabeled information. The collective inference paradigm allows us to manage the spatial correlation between spectral responses of neighboring pixels, as interacting pixels are labeled simultaneously. In particular, the innovative contribution of this study includes: (1) the design of an application-specific co-training schema to use both spectral information and spatial information, iteratively extracted at the object (set of pixels) level via collective inference; (2) the formulation of a spatial-aware example selection schema that accounts for the spatial correlation of predicted labels to augment training sets during iterative learning and (3) the investigation of a diversity class criterion that allows us to speed-up co-training classification. Experimental results validate the accuracy and efficiency of the proposed spectral-spatial, collective, co-training strategy

Transductive hyperspectral image classification: toward integrating spectral and relational features via an iterative ensemble system

Remotely sensed hyperspectral image classification is a very challenging task due to the spatial correlation of the spectral signature and the high cost of true sample labeling. In light of this, the collective inference paradigm allows us to manage the spatial correlation between spectral responses of neighboring pixels, as interacting pixels are labeled simultaneously. The transductive inference paradigm allows us to reduce the inference error for the given set of unlabeled data, as sparsely labeled pixels are learned by accounting for both labeled and unlabeled information. In this paper, both these paradigms contribute to the definition of a spectral-relational classification methodology for imagery data. We propose a novel algorithm to assign a class to each pixel of a sparsely labeled hyperspectral image. It integrates the spectral information and the spatial correlation through an ensemble system. For every pixel of a hyperspectral image, spatial neighborhoods are constructed and used to build application-specific relational features. Classification is performed with an ensemble comprising a classifier learned by considering the available spectral information (associated with the pixel) and the classifiers learned by considering the extracted spatio-relational information (associated with the spatial neighborhoods). The more reliable labels predicted by the ensemble are fed back to the labeled part of the image. Experimental results highlight the importance of the spectral-relational strategy for the accurate transductive classification of hyperspectral images and they validate the proposed algorithm

Multivariate Analysis Applications in X-ray Diffraction

: Multivariate analysis (MA) is becoming a fundamental tool for processing in an efficient way the large amount of data collected in X-ray diffraction experiments. Multi-wedge data collections can increase the data quality in case of tiny protein crystals; in situ or operando setups allow investigating changes on powder samples occurring during repeated fast measurements; pump and probe experiments at X-ray free-electron laser (XFEL) sources supply structural characterization of fast photo-excitation processes. In all these cases, MA can facilitate the extraction of relevant information hidden in data, disclosing the possibility of automatic data processing even in absence of a priori structural knowledge. MA methods recently used in the field of X-ray diffraction are here reviewed and described, giving hints about theoretical background and possible applications. The use of MA in the framework of the modulated enhanced diffraction technique is described in detail

A Comparison of 2 Mitral Annuloplasty Rings for Severe Ischemic Mitral Regurgitation: Clinical and Echocardiographic Outcomes.

Controversies regarding the choice of annuloplasty rings for treatment of ischemic mitral regurgitation still exist. Aim of the study is to compare early performance of 2 different rings in terms of rest and exercise echocardiographic parameters (transmitral gradient, systolic pulmonary artery pressure, and mitral valve area), clinical outcomes, and recurrence of mitral regurgitation. From January 2008 till December 2013, prospectively collected data of patients who underwent coronary artery bypass grafting and undersizing mitral valve annuloplasty for severe chronic ischemic mitral regurgitation at our Institution were reviewed. A total of 93 patients were identified; among them 44 had semirigid Memo 3D ring implanted (group A) whereas 49 had a rigid profile 3D ring (group B). At 6 months, recurrent ischemic mitral regurgitation, equal or more than moderate, was observed in 4 and 6 patients in the group A and B, respectively (P = 0.74). Group A showed certain improved valve geometric parameters such as posterior leaflet angle, tenting area, and coaptation depth. Transmitral gradient was significantly higher at rest in the group B (P < 0.0001). During exercise, significant increase of transmitral gradient and systolic pulmonary artery pressure was observed in group B (P < 0.0001). Mitral valve area was not statistically significantly smaller at rest in between groups (P = 0.09); however, it significantly decreased with exercise in group B (P = 0.01). At midterm follow-up, patients in group B were more symptomatic. In patients with chronic ischemic mitral regurgitation, use of semirigid Memo 3D ring when compared to the rigid Profile 3D may be associated with early improved mitral valve geometrical conformation and hemodynamic profile, particularly during exercise. No difference was observed between both groups in recurrent mitral regurgitation.Peer reviewe

Multidisciplinary studies on a sick-leader syndrome-associated mass stranding of sperm whales (Physeter macrocephalus) along the Adriatic coast of Italy

Mass strandings of sperm whales (Physeter macrocephalus) are rare in the Mediterranean Sea. Nevertheless, in 2014 a pod of 7 specimens stranded alive along the Italian coast of the Central Adriatic Sea: 3 individuals died on the beach after a few hours due to internal damages induced by prolonged recumbency; the remaining 4 whales were refloated after great efforts. All the dead animals were genetically related females; one was pregnant. All the animals were infected by dolphin morbillivirus (DMV) and the pregnant whale was also affected by a severe nephropathy due to a large kidney stone. Other analyses ruled out other possible relevant factors related to weather conditions or human activities. The results of multidisciplinary post-mortem analyses revealed that the 7 sperm whales entered the Adriatic Sea encountering adverse weather conditions and then kept heading northward following the pregnant but sick leader of the pod, thereby reaching the stranding site. DMV infection most likely played a crucial role in impairing the health condition and orientation abilities of the whales. They did not steer back towards deeper waters, but eventually stranded along the Central Adriatic Sea coastline, a real trap for sperm whales

SATURN: A Technological Demonstration Mission for Distributed SAR Imaging

The OHB-Italia S.p.A-led consortium is in the midst of Phase B of SATURN (Synthetic AperTure radar cUbesat foRmation flyiNg), part of ALCOR, an Italian Space Agency (ASI) programme promoting the development of the next generation Italian CubeSats. SATURN is a demonstration mission that features Multiple-Input-Multiple-Output (MIMO) technology applied to a Swarm of CubeSats equipped with Synthetic Aperture Radar (SAR) for Earth Observation. MIMO is based on cooperative active sensors, where each one transmits signals and receives the illuminated common area backscatter related to the entire swarm, increasing measurement performances with a trend approximatively equal to the square of the number of sensors. The complete SATURN constellation features 16 mini-swarms, each of 3 CubeSats, spread over 4 SSOs equally spaced by 3 hours of local time. The constellation is designed to provide an average revisit time of 1.5 h and an interferometric revisit time of 1 day worldwide. The aim of this demonstration mission is to verify MIMO technology applied to SAR on a mini-swarm of 3 CubeSats in close formation on a Low Earth Down-Dusk Sun Synchronous Orbit. Using OHB-I’s M3Multi Mission Modular platform equipped with a miniaturized SAR Instrument, developed by ARESYS S.r.l. and Airbus Italia S.p.A., our mission is able to achieve a resolution of 5x5 m over a 30 km swath. Thus, SATURN enables low-cost, scalable SAR missions for affordable access to space for public and private entities, overcoming the single point of failure of one large and complex satellite. Subsequent swarms, deploying from 3 to 48 CubeSats, are expected to bring technological innovations and improve Italy’s competitiveness in the European and global Earth Observation scenario

Multivariate Analysis Applications in X-ray Diffraction

Multivariate analysis (MA) is becoming a fundamental tool for processing in an efficient way the large amount of data collected in X-ray diffraction experiments. Multi-wedge data collections can increase the data quality in case of tiny protein crystals; in situ or operando setups allow investigating changes on powder samples occurring during repeated fast measurements; pump and probe experiments at X-ray free-electron laser (XFEL) sources supply structural characterization of fast photo-excitation processes. In all these cases, MA can facilitate the extraction of relevant information hidden in data, disclosing the possibility of automatic data processing even in absence of a priori structural knowledge. MA methods recently used in the field of X-ray diffraction are here reviewed and described, giving hints about theoretical background and possible applications. The use of MA in the framework of the modulated enhanced diffraction technique is described in detail

ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU

In recent years, hyperspectral sensors for Earth remote sensing have become very popular. Such systems are able to provide the user with images having both spectral and spatial information. The current hyperspectral spaceborne sensors are able to capture large areas with increased spatial and spectral resolution. For this reason, the volume of acquired data needs to be reduced on board in order to avoid a low orbital duty cycle due to limited storage space. Recently, literature has focused the attention on efficient ways for on-board data compression. This topic is a challenging task due to the difficult environment (outer space) and due to the limited time, power and computing resources. Often, the hardware properties of Graphic Processing Units (GPU) have been adopted to reduce the processing time using parallel computing. The current work proposes a framework for on-board operation on a GPU, using NVIDIA’s CUDA (Compute Unified Device Architecture) architecture. The algorithm aims at performing on-board compression using the target’s related strategy. In detail, the main operations are: the automatic recognition of land cover types or detection of events in near real time in regions of interest (this is a user related choice) with an unsupervised classifier; the compression of specific regions with space-variant different bit rates including Principal Component Analysis (PCA), wavelet and arithmetic coding; and data volume management to the Ground Station. Experiments are provided using a real dataset taken from an AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) airborne sensor in a harbor area

Exploiting spatial correlation of spectral signature for training data selection in hyperspectral image classification

Supervised classification is commonly used to produce a thematic map from hyperspectral data. A classifier is learned from training pixels and used to assign a known class (theme) to each pixel (imagery data example). However, supervised classification requires a sufficient number of representative training samples to be accurate. These samples are usually selected by expert visual inspection or field survey. Consequently, collecting representative samples is a very challenging task due to the high cost of true sample selecting and labeling. This paper introduces an unsupervised learning schema, where the most suitable pixels to train the classifier are selected via image segmentation. This reduces the expert effort required for choosing training samples. In our proposal, clustering is performed by accounting for the property of spatial correlation of pixel-level spectral information, so that thematic objects can be retrieved via unsupervised learning and representative training data can be sampled throughout clusters. Experimental results highlight that the pixel classification accuracy outperforms the results of a random selection scheme