In this paper, we propose a vein-based biometric verification system relying on deep learning. A novel approach consisting of a convolutional neural network (CNN), trained in a supervised manner, cascaded with an auto-encoder, trained in an unsupervised way, is here exploited. In more detail, a novel densely-connected convolutional autoencoder is here used on top of backbone CNNs. This architecture aims at increasing the discriminative capability of the features generated from hand vein patterns. Experimental tests on finger, palm, and dorsal veins show that the proposed approach leads to an improvement of the recognition rates with respect to the use of the sole CNNs for feature extraction. The achieved performance are superior to the current state of the art in vein biometric verification.

We introduce an unsupervised learning method that aims to identify building anomalies using Interferometric Synthetic Aperture Radar (InSAR) time-series data. Specifically, we leverage data obtained from the European Ground Motion Service to develop our proposed approach, which employs a long short-term memory autoencoder model and a reconstruction loss function based on a soft variant of the dynamic time warping, namely “soft-DTW”. We deliberately utilize this loss function for its ability to compare time-series that are not aligned in time, unlike the other conventional reconstruction losses that do not account for time shifts. Moreover, we enhance the performance of anomaly detection by smoothing inputs with a Hann window and defining the learning objective to reconstruct the time order of randomly permuted input series. Our experimental findings, based on persistent scatterer data from Rome, Italy, reveal that our method outperforms several unsupervised machine learning and deep learning methods in detecting various types of building displacement, such as trend, noise, and step anomalies. Additionally, quantitative and qualitative evaluations validate the efficacy of our approach in identifying potentially anomalous buildings. Thus, our method offers a promising solution for detecting anomalies in PS-InSAR time-series, which could have substantial implications in the fields of urban monitoring and infrastructure management.

In this study, we examine the potential of leveraging self-supervised learning (SSL) and transfer learning methodologies for forest disturbance mapping using Earth Observation (EO) data. Our focus is on natural disturbances caused by windthrow and snowload damages. Particularly, we investigate the potential of knowledge distillation-based contrastive learning approaches to obtain comprehensive representations of forest structure changes using Copernicus Sentinel-1 and Sentinel-2 satellite imagery. Leveraging pre-trained backbone models from knowledge distillation, we employ transfer learning based on Deep Change Vector Analysis (DCVA) to delineate forest changes. We demonstrate developed approaches on two use-cases, namely mapping windthown forest using bi-temporal Sentinel-1 and Sentinel-2 images, and mapping forest areas damaged by excessive snowload using bi-temporal Sentinel-1 images. Developed self-supervised models were compared in a benchmarking exercise. The best results were provided by pixel-level contrastive learning for Sentinel-1 based snowload damage mapping with an overall accuracy of 84% and $F_{1}$ score of 0.567, and for Sentinel-2 based forest windthrow mapping with an overall accuracy of 76.5% and $F_{1}$ score of 0.692. We expect that developed methodologies can be useful for mapping also other types of forest disturbances using Copernicus Sentinel images or similar EO data. Our findings underscore the potential of SSL and transfer learning for enhancing forest disturbance detection using EO.

Enhancing agricultural methods through the utilization of Earth observation and artificial intelligence (AI) has emerged as a significant concern. The ability to quantify soil parameters on a large scale can play a pivotal role in optimizing the fertilization process. While techniques for noninvasive estimation of soil parameters from hyperspectral images (HSIs) exist, their validation typically occurs across different datasets and employs varying validation protocols. This diversity renders them inherently challenging (or even impossible) to compare objectively.