Dr. Riccardo La Grassa Profile

Dr. Riccardo La Grassa

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Proceedings Article | 3 April 2024 Paper

Distortion-aware super-resolution for planetary exploration images

N. Landro, I. Gallo, F. Pelosi, R. La Grassa, A. Rehman

Proceedings Volume 13072, 1307211 (2024) https://doi.org/10.1117/12.3023378

KEYWORDS: Distortion, Super resolution, Transformers, Image processing

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Super-resolution is crucial in computer vision and digital image processing, aiming to enhance low-quality images’ resolution and visual quality. This paper focuses on correcting the distortion introduced by fisheye lenses and improving the resolution of images for better detail representation. Specifically, we propose an evaluation approach that benchmarks three state-of-the-art models in different categories: Real-ESRGAN (convolutions), SwinIR (transformers), and SR3 (diffusion). We evaluate their performance in super-resolution and distortion correction tasks using metrics such as PSNR and SSIM. To facilitate this evaluation, we create and release a new dataset of lunar surface images with fisheye distortion applied. Our experiments demonstrate the effectiveness of each model in handling distortion and improving image resolution. The results show that large models generally outperform medium models, and PSNR models achieve higher PSNR and SSIM scores than GAN models. Additionally, we evaluate the distortion correction by comparing the corrected images with ground truth. Our findings contribute to understanding different model categories and their performance in super-resolution and distortion correction tasks. The proposed dataset and evaluation approach can be valuable resources for future research.

Proceedings Article | 3 April 2024 Paper

Enhancing crop segmentation in satellite image time-series with transformer networks

I. Gallo, M. Gatti, N. Landro, C. Loschiavo, M. Boschetti, R. La Grassa, A. Rehman

Proceedings Volume 13072, 1307208 (2024) https://doi.org/10.1117/12.3023389

KEYWORDS: Image segmentation, Transformers, Satellite imaging, Remote sensing, Earth observing sensors

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Recent studies have shown that Convolutional Neural Networks (CNNs) achieve impressive results in crop segmentation of Satellite Image Time-Series (SITS). However, the emergence of transformer networks in various vision tasks raises the question of whether they can outperform CNNs in crop segmentation of SITS. This paper presents a revised version of the Transformer-based Swin UNETR model adapted specifically for crop segmentation of SITS. The proposed model demonstrates significant advancements, achieving a validation accuracy of 96.14% and a test accuracy of 95.26% on the Munich dataset, surpassing the previous best results of 93.55% for validation and 92.94% for the test. Additionally, the model’s performance on the Lombardia dataset is comparable to UNet3D and superior to FPN and DeepLabV3. Experiments of this study indicate that the model will likely achieve comparable or superior accuracy to CNNs while requiring significantly less training time. These findings highlight the potential of transformer-based architectures for crop segmentation in SITS, opening new avenues for remote sensing applications.

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