Search Results for author:
Found 22 papers, 10 papers with code
Sea Ice Segmentation From SAR Data by Convolutional Transformer Networks
Sea ice is a crucial component of the Earth's climate system and is highly sensitive to changes in temperature and atmospheric conditions.
Artificial intelligence to advance Earth observation: a perspective
Earth observation (EO) is a prime instrument for monitoring land and ocean processes, studying the dynamics at work, and taking the pulse of our planet.
Complex-Valued End-to-end Deep Network with Coherency Preservation for Complex-Valued SAR Data Reconstruction and Classification
A complex-valued (CV) end-to-end deep network is developed in this study for the reconstruction and classification of CV-SAR data.
Explainable, Physics Aware, Trustworthy AI Paradigm Shift for Synthetic Aperture Radar
The recognition or understanding of the scenes observed with a SAR system requires a broader range of cues, beyond the spatial context.
Deep Learning-Based Anomaly Detection in Synthetic Aperture Radar Imaging
Experiments are performed to show the advantages of our method compared to the conventional Reed-Xiaoli algorithm, highlighting the importance of an efficient despeckling pre-processing step.
Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval
Spaceborne synthetic aperture radar (SAR) can provide accurate images of the ocean surface roughness day-or-night in nearly all weather conditions, being an unique asset for many geophysical applications.
Coreset of Hyperspectral Images on Small Quantum Computer
Moreover, we trained the SVM on the coreset data by using both a D-Wave QA and a conventional method.
Guided deep learning by subaperture decomposition: ocean patterns from SAR imagery
Overall, we encourage the development of data centring approaches, showing that, data preprocessing could bring significant performance improvements over existing deep learning models.
Physically Explainable CNN for SAR Image Classification
In this paper, we first propose a novel physically explainable convolutional neural network for SAR image classification, namely physics guided and injected learning (PGIL).
LUAI Challenge 2021 on Learning to Understand Aerial Images
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
CrossATNet - A Novel Cross-Attention Based Framework for Sketch-Based Image Retrieval
While we define a cross-modal triplet loss to ensure the discriminative nature of the shared space, an innovative cross-modal attention learning strategy is also proposed to guide feature extraction from the image domain exploiting information from the respective sketch counterpart.
Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges
In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI.
A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images
We perform a thorough bench-marking of this dataset and demonstrate that the proposed network outperforms other state-of-the-art methods for zero-shot sketch-based retrieval framework in remote sensing.
Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning
The classification of large-scale high-resolution SAR land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging.
CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing
In particular, we are interested in two application scenarios: i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery, and ii) multi-label image retrieval between very high resolution (VHR) images and speech based label annotations.
Dialectical GAN for SAR Image Translation: From Sentinel-1 to TerraSAR-X
To improve the quality of SAR images and to reduce the costs of their generation, we propose a Dialectical Generative Adversarial Network (Dialectical GAN) to generate high-quality SAR images.
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
The fully annotated DOTA images contains $188, 282$ instances, each of which is labeled by an arbitrary (8 d. o. f.)
Ranked #52 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data
Very High Spatial Resolution (VHSR) large-scale SAR image databases are still an unresolved issue in the Remote Sensing field.
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback.
Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection
We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries.
Authorship Analysis based on Data Compression
This paper proposes to perform authorship analysis using the Fast Compression Distance (FCD), a similarity measure based on compression with dictionaries directly extracted from the written texts.
Further results on dissimilarity spaces for hyperspectral images RF-CBIR
Content-Based Image Retrieval (CBIR) systems are powerful search tools in image databases that have been little applied to hyperspectral images.
