Search Results for author:
Found 13 papers, 6 papers with code
Multi-Scale and Multimodal Species Distribution Modeling
Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables.
POLO -- Point-based, multi-class animal detection
Automated wildlife surveys based on drone imagery and object detection technology are a powerful and increasingly popular tool in conservation biology.
Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentation
Prototypical part learning is emerging as a promising approach for making semantic segmentation interpretable.
Just Project! Multi-Channel Despeckling, the Easy Way
Reducing speckle fluctuations in multi-channel SAR images is essential in many applications of SAR imaging such as polarimetric classification or interferometric height estimation.
Leveraging Vision-Language Foundation Models for Fine-Grained Downstream Tasks
Vision-language foundation models such as CLIP have shown impressive zero-shot performance on many tasks and datasets, especially thanks to their free-text inputs.
Multi-temporal speckle reduction with self-supervised deep neural networks
Speckle filtering is generally a prerequisite to the analysis of synthetic aperture radar (SAR) images.
Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images
Reducing speckle and limiting the variations of the physical parameters in Synthetic Aperture Radar (SAR) images is often a key-step to fully exploit the potential of such data.
As if by magic: self-supervised training of deep despeckling networks with MERLIN
We introduce a self-supervised strategy based on the separation of the real and imaginary parts of single-look complex SAR images, called MERLIN (coMplex sElf-supeRvised despeckLINg), and show that it offers a straightforward way to train all kinds of deep despeckling networks.
Image Restoration for Remote Sensing: Overview and Toolbox
Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community.
Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentation
This paper presents a despeckling method for Sentinel-1 GRD images based on the recently proposed framework "SAR2SAR": a self-supervised training strategy.
Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning
The proposed method combines this multi-temporal average and the image at a given date in the form of a ratio image and uses a state-of-the-art neural network to remove the speckle in this ratio image.
SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy
Many different schemes have been proposed for the restoration of intensity SAR images.
SAR2SAR: a semi-supervised despeckling algorithm for SAR images
A study with synthetic speckle noise is presented to compare the performances of the proposed method with other state-of-the-art filters.
