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20072023
most citedClassification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning

127 citations · 232 across the 9 of their papers we have counts for

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cs.CV20231 cited

Sea Ice Segmentation From SAR Data by Convolutional Transformer Networks

Nicolae-Catalin Ristea, Andrei Anghel, Mihai Datcu

Sea ice is a crucial component of the Earth's climate system and is highly sensitive to changes in temperature and atmospheric conditions. Accurate and timely measurement of sea ic…

cs.CV2022

Deep Learning-Based Anomaly Detection in Synthetic Aperture Radar Imaging

Max Muzeau, Chengfang Ren, Sébastien Angelliaume +2

In this paper, we proposed to investigate unsupervised anomaly detection in Synthetic Aperture Radar (SAR) images. Our approach considers anomalies as abnormal patterns that deviat…

cs.CV2022

Guided deep learning by subaperture decomposition: ocean patterns from SAR imagery

Nicolae-Catalin Ristea, Andrei Anghel, Mihai Datcu +1

Spaceborne synthetic aperture radar can provide meters scale images of the ocean surface roughness day or night in nearly all weather conditions. This makes it a unique asset for m…

cs.CV2021

LUAI Challenge 2021 on Learning to Understand Aerial Images

Gui-Song Xia, Jian Ding, Ming Qian +33

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 a…

cs.CV202133 cited

CrossATNet - A Novel Cross-Attention Based Framework for Sketch-Based Image Retrieval

Ushasi Chaudhuri, Biplab Banerjee, Avik Bhattacharya +1

We propose a novel framework for cross-modal zero-shot learning (ZSL) in the context of sketch-based image retrieval (SBIR). Conventionally, the SBIR schema mainly considers simult…

cs.CV2020

A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

Ushasi Chaudhuri, Biplab Banerjee, Avik Bhattacharya +1

Conventional existing retrieval methods in remote sensing (RS) are often based on a uni-modal data retrieval framework. In this work, we propose a novel inter-modal triplet-based z…