Deep learning methods have proven to be a powerful tool in the analysis ...
Deep metric learning (DML) based methods have been found very effective ...
Due to the publicly available thematic maps and crowd-sourced data, remo...
In recent years, deep neural networks (DNNs) have been found very succes...
In this paper, we introduce a novel Synchronized Class Token Fusion (SCT...
The development of federated learning (FL) methods, which aim to learn f...
Visual question answering (VQA) methods in remote sensing (RS) aim to an...
The development of learning-based hyperspectral image compression method...
Deep learning-based image compression methods have led to high
rate-dist...
Earth observation (EO) is a prime instrument for monitoring land and oce...
The development of deep learning based image representation learning (IR...
The use of deep neural networks (DNNs) has recently attracted great atte...
With the new generation of satellite technologies, the archives of remot...
Subsurface tile drainage pipes provide agronomic, economic and environme...
The growing operational capability of global Earth Observation (EO) crea...
The development of accurate methods for multi-label classification (MLC)...
The development of cross-modal retrieval systems that can search and ret...
Machine Learning (ML) techniques are employed to analyze and process big...
The development of accurate and scalable cross-modal image-text retrieva...
Due to the availability of multi-modal remote sensing (RS) image archive...
This paper introduces a novel deep metric learning-based semi-supervised...
Due to the availability of large-scale multi-modal data (e.g., satellite...
The collection of a high number of pixel-based labeled training samples ...
Remote sensing (RS) images are usually stored in compressed format to re...
This paper presents a novel graph-theoretic deep representation learning...
This paper presents the multi-modal BigEarthNet (BigEarthNet-MM) benchma...
In remote sensing (RS), collecting a large number of reliable training i...
Learning the similarity between remote sensing (RS) images forms the
fou...
The development of accurate methods for multi-label classification (MLC)...
This paper analyzes and compares different deep learning loss functions ...
To reduce the storage requirements, remote sensing (RS) images are usual...
Deep neural networks (DNNs) have been recently found popular for image
c...
This chapter presents recent advances in content based image search and
...
Interferometric phase restoration has been investigated for decades and ...
Success of deep neural networks in the framework of remote sensing (RS) ...
This paper presents a Generative Adversarial Network based super-resolut...
Hashing methods have been recently found very effective in retrieval of
...
This paper presents a novel framework that jointly exploits Convolutiona...
This paper presents a new large-scale multi-label Sentinel-2 benchmark
a...