A significant amount of the produced solid waste reaching the oceans is made of plastics. The amount of plastic debris in the ocean and coastal areas is steadily increasing and is now a major global environmental issue. The monitoring of marine plastic litter, ground-based monitoring systems and/or field campaigns are time-consuming, expensive, require great organisational efforts, and provide very limited information in terms of the spatial and temporal dynamics of marine debris. Earth Observation (EO) by satellite can contribute significantly to marine plastic litter detection. In 2019, a new hyperspectral satellite, called PRISMA, was launched by the Italian Space Agency. The high spectral resolution of PRISMA may allow for better detection of floating plastic materials. At the same time, Machine Learning (ML) algorithms have the potential to find hidden patterns and identify complex relations among data and are increasingly employed in EO. This paper presents the development of a new method of identifying floating plastic objects in coastal areas by exploiting pan-sharpened hyperspectral PRISMA data, based on the combination of unsupervised and supervised ML algorithms. The study consisted of a configuration phase, during which the algorithms were trained in a fully controlled test, and a validation phase, in which the pre-trained algorithms were applied to satellite data collected at different sites and in different periods of the year. Despite the limited input data, results suggest that the tested ML approach, applied to pan-sharpened PRISMA data, can effectively recognise floating objects and plastic targets. The study indicates that increasing input datasets can help achieve higher-quality results.

The interest in change detection in the field of remote sensing has increased in the last few years. Searching for changes in satellite images has many useful applications, ranging from land cover and land use analysis to anomaly detection. In particular, urban change detection provides an efficient tool to study urban spread and growth through several years of observation. At the same time, change detection is often a computationally challenging and time-consuming task; therefore, a standard approach with manual detection of the elements of interest by experts in the domain of Earth Observation needs to be replaced by innovative methods that can guarantee optimal results with unquestionable value and within reasonable time. In this paper, we present two different approaches to change detection (semantic segmentation and classification) that both exploit convolutional neural networks to address these particular needs, which can be further refined and used in post-processing workflows for a large variety of applications.

Various applications in remote sensing demand automatic detection of changes in optical satellite images of the same scene acquired over time. This paper investigates how to leverage autoencoders in change vector analysis, in order to better delineate possible changes in a couple of co-registered, optical satellite images. Let us consider both a primary image and a secondary image acquired over time in the same scene. First an autoencoder artificial neural network is trained on the primary image. Then the reconstruction of both images is restored via the trained autoencoder so that the spectral angle distance can be computed pixelwise on the reconstructed data vectors. Finally, a threshold algorithm is used to automatically separate the foreground changed pixels from the unchanged background. The assessment of the proposed method is performed in three couples of benchmark hyperspectral images using different criteria, such as overall accuracy, missed alarms and false alarms. In addition, the method supplies promising results in the analysis of a couple of multispectral images of the burned area in the Majella National Park (Italy).

Remote sensing images find application in several different domains, such as land cover or land usage observation, environmental monitoring, and urbanization. This latter field has recently witnessed an interesting development with the use of remote sensing for infrastructural monitoring. In this work, we present an analysis of Sentinel-1 images, which were used to monitor the Italian provinces of Bologna and Modena located at the Emilia Region Apennines foothill. The goal of this study was the development of a machine learning-based detection system to monitor the deterioration of public aqueduct infrastructures based on Persistent Scatterer Interferometry (PSI). We evaluated the land deformation over a temporal range of five years; these series feed a k-means clustering algorithm to separate the pixels of the region according to different deformation patterns. Furthermore, we defined the critical areas as those areas where different patterns collided or overlapped. The proposed approach provides an informative tool for the structural health monitoring of underground infrastructures.