Target-adaptive CNN-based pansharpening

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network which trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality which ensures a very good performance also in the presence of a mismatch w.r.t. the training set, and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware

Guided patch-wise nonlocal SAR despeckling

We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery. Filtering is performed by plain patch-wise nonlocal means, operating exclusively on SAR data. However, the filtering weights are computed by taking into account also the optical guide, which is much cleaner than the SAR data, and hence more discriminative. To avoid injecting optical-domain information into the filtered image, a SAR-domain statistical test is preliminarily performed to reject right away any risky predictor. Experiments on two SAR-optical datasets prove the proposed method to suppress very effectively the speckle, preserving structural details, and without introducing visible filtering artifacts. Overall, the proposed method compares favourably with all state-of-the-art despeckling filters, and also with our own previous optical-guided filter

Abundances of demersal sharks and chimaera from 1994-2009 scientific surveys in the central Mediterranean sea

Bibliographic and data gathered in scientific bottom trawl surveys carried out off the Southern Coasts of Sicily (Mediterranean Sea), from 1994 to 2009 and between a depth of 10 and 800 m, were analysed in order to prepare a checklist of demersal sharks and chimaera, which are species sensitive to fisheries exploitation. Out of the 27 previously reported demersal shark and chimaera taxa in the Mediterranean, only 23 were found in literature and 20 sampled during the surveys in the investigated area. Among the species sampled in the surveys, only 2 ubiquitous (Squalus blainville and Scyliorhinus canicula) and 3 deep-water (Chimaera monstrosa, Centrophorus granulosus and Galeus melastomus) species showed a wide geographical distribution with a consistent abundance. Excluding the rare (such as Oxynotus centrina) or uncommon shark (e.g. Squalus acanthias), the estimated frequencies of occurrence and abundance indexes show a possible risk of local extinction for the almost exclusively (e.g. angelshark, Squatina spp.) or preferential (e.g. Scyliorhinus stellaris) neritic species.peer-reviewe

Edge Preserving CNN SAR Despeckling Algorithm

SAR despeckling is a key tool for Earth Observation. Interpretation of SAR images are impaired by speckle, a multiplicative noise related to interference of backscattering from the illuminated scene towards the sensor. Reducing the noise is a crucial task for the understanding of the scene. Based on the results of our previous solution KL-DNN, in this work we define a new cost function for training a convolutional neural network for despeckling. The aim is to control the edge preservation and to better filter manmade structures and urban areas that are very challenging for KL-DNN. The results show a very good improvement on the not homogeneous areas keeping the good results in the homogeneous ones. Result on both simulated and real data are shown in the paper.Comment: Accepted to LAGIRS 202

Multi-Objective CNN Based Algorithm for SAR Despeckling

Deep 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 synthetic aperture radar (SAR) domain the application of DL techniques is not straightforward due to non trivial interpretation of SAR images, specially caused by the presence of speckle. Several deep learning 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 paper, 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 this term is dedicated mainly to one of the following SAR image characteristics: spatial details, speckle statistical properties and strong scatterers identification. Their combination allows to balance these effects. Moreover, a specifically designed architecture is proposed for effectively extract distinctive features within the considered framework. Experiments on simulated and real SAR images show the accuracy of the proposed method compared to the State-of-Art despeckling algorithms, both from quantitative and qualitative point of view. The importance of considering such SAR properties in the cost function is crucial for a correct noise rejection and details preservation in different underlined scenarios, such as homogeneous, heterogeneous and extremely heterogeneous

A New Ratio Image Based CNN Algorithm For SAR Despeckling

In SAR domain many application like classification, detection and segmentation are impaired by speckle. Hence, despeckling of SAR images is the key for scene understanding. Usually despeckling filters face the trade-off of speckle suppression and information preservation. In the last years deep learning solutions for speckle reduction have been proposed. One the biggest issue for these methods is how to train a network given the lack of a reference. In this work we proposed a convolutional neural network based solution trained on simulated data. We propose the use of a cost function taking into account both spatial and statistical properties. The aim is two fold: overcome the trade-off between speckle suppression and details suppression; find a suitable cost function for despeckling in unsupervised learning. The algorithm is validated on both real and simulated data, showing interesting performances

Lipid oxidation kinetics of ozone-processed shrimp during iced storage using peroxide value measurements

In this research, in situ generated ozone exposure/wash cycles of 1, 3, and 5 min applied to shrimp samples either before (BIS) or during iced storage (DIS) has been used to study the lipid oxidation kinetics using the peroxide values (PV). The induction period (IP) as well as PV at end of the IP (PVIP) have been obtained. The rate constants (k) as well as half-lives (t1/2) of hydroperoxides formation for different oxidation stages were calculated. The results showed that both IP and PVIP were lower with BIS (IP between 4.35±0.09 and 5.08±0.23 days; PVIP between 2.92±0.06 and 3.40±0.18 mEq kg−1) compared with DIS (IP between 5.92±0.12 and 6.14±0.09 days; PVIP between 4.49±0.17 and 4.56±0.10 mEq kg−1). The k value for DIS seemed to be the greater compared to BIS. In addition, whilst decreases and increases in t1/2 were found at propagation, respectively, for BIS and DIS, decreases and increases were only found at the induction of oxidation stage(s) for BIS. Further, the PV of ozone-processed samples would fit first order lipid oxidation kinetics independent of duration of ozone exposures. For the first time, PV measurements and fundamental kinetic principles have been used to describe how increasing ozone exposures positively affects the different oxidation stages responsible for the formation of hydroperoxides in ozone-processed shrimp

A Tale on the Demersal and Bottom Dwelling Chondrichthyes in the South of Sicily through 20 Years of Scientific Survey

In the present work, an overview of the demersal (sharks‐chimaera) and bottom dwelling (batoids) of experimental survey international bottom trawl survey in the mediterranean (MEDITS) data, from 1994 to 2013, is provided. The analysed data refer to a wide area located off the southern coast of Sicily, namely south of Sicily (according to the general fisheries commission for the mediterranean (GFCM) classification, Geographical Sub‐Area 16). A checklist of the recorded Chondrichthyes was integrated by density index, D.I. (N/Km2) and average individual weight (as the ratio between biomass index, D.I. (N/Km2) and D.I.). Results suggest that most of the Chondrichthyes in South of Sicily are in a steady state, although in the last few years, they seemed to recover. The spatial distribution of sharks‐chimaera in the geographical sub‐area (GSA) 16 is mainly concentrated in the southern and north‐western zones. Nevertheless, possible management actions to promote the recovering of these very important ecological and threatened species are discussed