Manipulation and generation of synthetic satellite images using deep learning models

Generation and manipulation of digital images based on deep learning (DL) are receiving increasing attention for both benign and malevolent uses. As the importance of satellite imagery is increasing, DL has started being used also for the generation of synthetic satellite images. However, the direct use of techniques developed for computer vision applications is not possible, due to the different nature of satellite images. The goal of our work is to describe a number of methods to generate manipulated and synthetic satellite images. To be specific, we focus on two different types of manipulations: full image modification and local splicing. In the former case, we rely on generative adversarial networks commonly used for style transfer applications, adapting them to implement two different kinds of transfer: (i) land cover transfer, aiming at modifying the image content from vegetation to barren and vice versa and (ii) season transfer, aiming at modifying the image content from winter to summer and vice versa. With regard to local splicing, we present two different architectures. The first one uses image generative pretrained transformer and is trained on pixel sequences in order to predict pixels in semantically consistent regions identified using watershed segmentation. The second technique uses a vision transformer operating on image patches rather than on a pixel by pixel basis. We use the trained vision transformer to generate synthetic image segments and splice them into a selected region of the to-be-manipulated image. All the proposed methods generate highly realistic, synthetic, and satellite images. Among the possible applications of the proposed techniques, we mention the generation of proper datasets for the evaluation and training of tools for the analysis of satellite images. (c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI

More Kindness, Less Prejudice against Immigrants? A Preliminary Study with Adolescents

Prejudice against immigrants is a relevant research topic within social psychology. Researchers identified several individual variables affecting anti-immigrant prejudice, such as morality and personality. However, until now, prejudice has never been studied in relation to kindness, which might be a significant protective factor against prejudice. Based on Kohlberg's theory of moral judgement, four stage dimensions of kindness were identified, from egocentric to authentic kindness (i.e., a means for social progress and improvement). This study aims to explore the relationship between the four kindness dimensions and blatant and subtle prejudice against immigrants in adolescence, by also considering the moderating role of adolescents' sex. It involved 215 Italian participants (77% girls), who were asked to fill in a self-report questionnaire. Results showed that boys scored higher on egocentric kindness than girls, but no sex differences emerged for prejudice. Egocentric and extrinsically motivated kindness appeared to be risk factors for prejudice, whereas the most authentic form of kindness was a protective factor. In addition, adolescents' sex moderated the relationship between egocentric kindness and blatant prejudice, whereby this association was stronger for boys. The implications of these findings, the study's limitations, and suggestions for future research are discussed

Towards alien plant prioritization in Italy: methodological issues and first results

In recent decades, multiple actions have been taken to counteract the relentless expansion of invasive alien species as well as to gain a better understanding of their effects on ecosystems. Here, we describe the approach designed by the Italian Botanical Society that is aimed at selecting a list of candidate alien plants to be subjected to a prioritization procedure. We selected a total of 96 species on the basis of data related to their occurrence on both a national and regional scale, their invasiveness and their potential to invade plant communities and/or habitats of community concern. This list represents the first result obtained by applying this standardized workflow and is a first step towards the identification of those alien species that should be included in the national list according to Regulation (EU) n. 1143/2014

Modulation of Milk Allergenicity by Baking Milk in Foods: A Proteomic Investigation

Cow’s milk is considered the best wholesome supplement for children since it is highly enriched with micro and macro nutrients. Although the protein fraction is composed of more than 25 proteins, only a few of them are capable of triggering allergic reactions in sensitive consumers. The balance in protein composition plays an important role in the sensitization capacity of cow’s milk, and its modification can increase the immunological response in allergic patients. In particular, the heating treatments in the presence of a food matrix have demonstrated a decrease in the milk allergenicity and this has also proved to play a pivotal role in developing tolerance towards milk. In this paper we investigated the effect of thermal treatment like baking of cow’s milk proteins that were employed as ingredients in the preparation of muffins. A proteomic workflow was applied to the analysis of the protein bands highlighted along the SDS gel followed by western blot analyses with sera of milk allergic children in order to have deeper information on the impact of the heating on the epitopes and consequent IgE recognition. Our results show that incorporating milk in muffins might promote the formation of complex milk–food components and induce a modulation of the immunoreactivity towards milk allergens compared to milk baked in the oven at 180 °C for ten minutes. The interactions between milk proteins and food components during heating proved to play a role in the potential reduction of allergenicity as assessed by in vitro tests. This would help, in perspective, in designing strategies for improving milk tolerance in young patients affected from severe milk allergies

Exact Histogram Specification Optimized for Structural Similarity

An exact histogram specification (EHS) method modifies its input image to have a specified histogram. Applications of EHS include image (contrast) enhancement (e.g., by histogram equalization) and histogram watermarking. Performing EHS on an image, however, reduces its visual quality. Starting from the output of a generic EHS method, we maximize the structural similarity index (SSIM) between the original image (before EHS) and the result of EHS iteratively. Essential in this process is the computationally simple and accurate formula we derive for SSIM gradient. As it is based on gradient ascent, the proposed EHS always converges. Experimental results confirm that while obtaining the histogram exactly as specified, the proposed method invariably outperforms the existing methods in terms of visual quality of the result. The computational complexity of the proposed method is shown to be of the same order as that of the existing methods. Index terms: histogram modification, histogram equalization, optimization for perceptual visual quality, structural similarity gradient ascent, histogram watermarking, contrast enhancement