Dr. Elena Donini Profile

Dr. Elena Donini

at Fondazione Bruno Kessler

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Publications (3)

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Proceedings Article | 26 October 2022 Paper

Unsupervised semantic segmentation of radar sounder data using contrastive learning

Elena Donini, Mattia Amico, Lorenzo Bruzzone, Francesca Bovolo

Proceedings Volume 12267, 122670N (2022) https://doi.org/10.1117/12.2636437

KEYWORDS: Computer programming, Associative arrays, Image segmentation, Radar, Remote sensing, Logic, Neural networks, Feature extraction, Feature selection, Information visualization

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Radar Sounders (RSs) are active sensors widely used for planetary exploration and Earth observation that probe the subsurface in a non-intrusive way by acquiring vertical profiles, called radargrams. Radargrams contain information on subsurface geology and are analyzed with neural networks for segmentation and target detection. However, most of these methods rely on supervised training, which requires a large amount of labeled data that is hard to retrieve. Hence, a need emerges for a novel method for unsupervised radargram segmentation. This paper proposes a novel method for unsupervised radargram segmentation by analyzing semantically meaningful features extracted from a deep network trained with a contrastive logic. First, the network (encoder) is trained using a pretext task to extract meaningful features (query). Considering a dictionary of possible features (keys), the encoder training loss can be defined as a dictionary look-up problem. Each query is matched to a key in a large and consistent dictionary. Although such a dictionary is not available for RS data, it is dynamically computed by extracting meaningful features with another deep network called the momentum encoder. Secondly, deep feature vectors are extracted from the encoder for all radargram pixels. After the feature selection, the feature vectors are binarized. Since pixels of the same class are expected to have similar feature vectors, we compute the similarity between the feature vectors to generate a cluster of pixels for each class. We applied the proposed method to segment radargrams acquired in Greenland by the MCoRDS-3 sensor, achieving good overall accuracy.

Proceedings Article | 12 September 2021 Presentation + Paper

Transfer learning for the semantic segmentation of cryoshpere radargrams

Miguel Hoyo García, Elena Donini, Francesca Bovolo

Proceedings Volume 11862, 118620T (2021) https://doi.org/10.1117/12.2600237

KEYWORDS: Image segmentation, Remote sensing, Radar, Free space, Multimedia, Interfaces, Convolution, Network architectures, Data acquisition

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Radar sounders (RSs) mounted on airborne platforms are active sensors widely employed to acquire subsurface data of the cryosphere for Earth observation. RS data, also called radargrams, provide information on the buried geology by identifying dielectric discontinuities in the subsurface. Recently, a strong effort can be observed in designing automatic techniques to identify the main targets of the cryosphere. However, most of the methods are based on target-specific handcrafted features. Newly convolutional neural networks (CNNs) automatically extract meaningful features from data. However, supervised training requires numerous labeled data that are hard to retrieve in the RS domain. In this work, we adopt a CNN pre-trained in domains other than RS for automatically segmenting cryosphere radargrams. To adapt to the radargram characteristics, we introduce convolutional layers at the beginning of the pre-trained network. We modify the top layers of the network to a U- fashion autoencoder to extract relevant features for the target task. The new layers are fine-tuned with few labeled radargrams to identify and segment five targets: free space, continental ice layering, floating ice, bedrock, and EFZ and thermal noise. The pre-trained weights are not updated during fine-tuning. We applied the proposed approach to radargrams from Antarctica acquired by MCoRDS3, obtaining high overall accuracy. These results demonstrate the effectiveness of the method in segmenting radargrams and discriminating continental and coastal ice structures.

Proceedings Article | 7 October 2019 Presentation + Paper

An automatic approach to map refreezing ice in radar sounder data

Elena Donini, Sanchari Thakur, Francesca Bovolo, Lorenzo Bruzzone

Proceedings Volume 11155, 111551B (2019) https://doi.org/10.1117/12.2533169

KEYWORDS: Radar, Associative arrays, Process modeling, Data modeling, Reflection, Modeling, Data acquisition, Optical inspection, Visual process modeling, Dielectrics

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Radar sounders mounted on airborne platforms have acquired data of the subsurface of the Earth's icy areas over the last decades. These data, called radargrams, contain information on the dielectric discontinuities in the ice-sheets, and thus on the buried geological structures and the related processes. Conventionally, these structures have been characterized and mapped by visually inspecting the radargrams. However, visual inspection is subjective and time-consuming and can lead to misinterpretations. Recently, state-of-the-art automatic techniques are proposed to map the position of the bedrock, the ice layering, and the noise in the radargram. However, there are no automatic techniques for mapping the basal refreezing, which is an important ice target that controls the rate of sea-ward ow of the ice-sheets. This paper proposes an automatic method to map the refreezing ice in radargrams. We model the refreezing ice considering its geophysical and radiometric properties. Then, we design a set of features considering this model to perform a classification of the radargrams into four classes, i.e., ice layering, echo-free zone (EFZ) and thermal noise, bedrock, and the refreezing ice. We applied the proposed method to radargrams acquired in the north Greenland by Multichannel Coherent Radar Depth Sounder (MCoRDS3), a radar sounder designed by the Center for Remote Sensing of Ice Sheets (CReSIS). The results indicate a good overall accuracy. The accuracy of refreezing ice is high, while that of the other classes is comparable with the state-of-the-art techniques. The results indicate the effectiveness of the proposed features in mapping the refreezing ice.

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