Search Results for author:
Found 51 papers, 10 papers with code
Radio Map Prediction from Aerial Images and Application to Coverage Optimization
We focus on predicting path loss radio maps using convolutional neural networks, leveraging aerial images alone or in combination with supplementary height information.
HyCoT: A Transformer-Based Autoencoder for Hyperspectral Image Compression
The development of learning-based hyperspectral image (HSI) compression models has recently attracted significant interest.
reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
To construct reBEN, we initially consider the Sentinel-1 and Sentinel-2 tiles used to construct the BigEarthNet dataset and then divide them into patches of size 1200 m x 1200 m. We apply atmospheric correction to the Sentinel-2 patches using the latest version of the sen2cor tool, resulting in higher-quality patches compared to those present in BigEarthNet.
Multi-Modal Vision Transformers for Crop Mapping from Satellite Image Time Series
Using images acquired by different satellite sensors has shown to improve classification performance in the framework of crop mapping from satellite image time series (SITS).
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval
ANNEAL aims to create a small but informative training set made up of similar and dissimilar image pairs to be utilized for accurately learning a metric space.
Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classification
Federated learning (FL) aims to collaboratively learn deep learning model parameters from decentralized data archives (i. e., clients) without accessing training data on clients.
MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters
To address this issue, in this paper we present the MagicBathyNet, which is a benchmark dataset made up of image patches of Sentinel2, SPOT-6 and aerial imagery, bathymetry in raster format and annotations of seabed classes.
A Label Propagation Strategy for CutMix in Multi-Label Remote Sensing Image Classification
To address this problem, we introduce a label propagation (LP) strategy that allows the effective application of CutMix in the context of MLC problems in RS without being affected by label noise.
Estimating Physical Information Consistency of Channel Data Augmentation for Remote Sensing Images
The application of data augmentation for deep learning (DL) methods plays an important role in achieving state-of-the-art results in supervised, semi-supervised, and self-supervised image classification.
Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment
Our two-stage procedure, comprises of robust fine-tuning CLIP in order to deal with the distribution shift, accompanied by the cross-modal alignment of a RS modality encoder, in an effort to extend the zero-shot capabilities of CLIP.
Exploring Masked Autoencoders for Sensor-Agnostic Image Retrieval in Remote Sensing
To this end, we present a systematic overview on the possible adaptations of the vanilla MAE to exploit masked image modeling on multi-sensor RS image archives (denoted as cross-sensor masked autoencoders [CSMAEs]) in the context of CBIR.
Radio Map Estimation -- An Open Dataset with Directive Transmitter Antennas and Initial Experiments
Over the last years, several works have explored the application of deep learning algorithms to determine the large-scale signal fading (also referred to as ``path loss'') between transmitter and receiver pairs in urban communication networks.
Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification
After presenting an extensive overview of the selected algorithms, a theoretical comparison of the algorithms is conducted based on their: 1) local training complexity; 2) aggregation complexity; 3) learning efficiency; 4) communication cost; and 5) scalability in terms of number of clients.
Ben-ge: Extending BigEarthNet with Geographical and Environmental Data
Based on this dataset, we showcase the value of combining different data modalities for the downstream tasks of patch-based land-use/land-cover classification and land-use/land-cover segmentation.
Annotation Cost Efficient Active Learning for Content Based Image Retrieval
Unlike the existing AL methods for CBIR, at each AL iteration of ANNEAL a human expert is asked to annotate the most informative image pairs as similar/dissimilar.
Label Noise Robust Image Representation Learning based on Supervised Variational Autoencoders in Remote Sensing
To address this issue, in this paper we propose a label noise robust IRL method that aims to prevent the interference of noisy labels on IRL, independently from the learning task being considered in RS.
Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images
In recent years, deep neural networks (DNNs) have been found very successful for multi-label classification (MLC) of remote sensing (RS) images.
Transformer-based Multi-Modal Learning for Multi Label Remote Sensing Image Classification
In this paper, we introduce a novel Synchronized Class Token Fusion (SCT Fusion) architecture in the framework of multi-modal multi-label classification (MLC) of remote sensing (RS) images.
Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning
The MF module performs iterative model averaging to learn without accessing data on clients in the case that clients are associated with different data modalities.
LiT-4-RSVQA: Lightweight Transformer-based Visual Question Answering in Remote Sensing
Visual question answering (VQA) methods in remote sensing (RS) aim to answer natural language questions with respect to an RS image.
HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods
The development of learning-based hyperspectral image compression methods has recently attracted great attention in remote sensing.
Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward
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.
Generative Adversarial Networks for Spatio-Spectral Compression of Hyperspectral Images
The development of deep learning-based models for the compression of hyperspectral images (HSIs) has recently attracted great attention in remote sensing due to the sharp growing of hyperspectral data archives.
Deep Active Learning for Multi-Label Classification of Remote Sensing Images
Unlike the existing AL query functions (which are defined for single-label classification or semantic segmentation problems), each query function in this paper is based on the evaluation of two criteria: i) multi-label uncertainty; and ii) multi-label diversity.
Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learning
Our approach aims to model the complementary characteristics of discriminative and generative reasoning for IRL under noisy labels.
Multi-Modal Fusion Transformer for Visual Question Answering in Remote Sensing
To make the intrinsic information of each RS image easily accessible, visual question answering (VQA) has been introduced in RS.
Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from Remote Sensing Images
In this study, we introduce two DL-based models: i) improved U-Net architecture; and ii) Visual Transformer-based encoder-decoder in the framework of tile drainage pipe detection.
Satellite Image Search in AgoraEO
To address this limitation, we have recently proposed MiLaN, a content-based image retrieval approach for fast similarity search in satellite image archives.
On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification
In this paper, we investigate three different noise robust CV SLC methods and adapt them to be robust for multi-label noise scenarios in RS.
Unsupervised Contrastive Hashing for Cross-Modal Retrieval in Remote Sensing
To address this problem, in this paper we introduce a novel unsupervised cross-modal contrastive hashing (DUCH) method for text-image retrieval in RS.
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.
An Unsupervised Cross-Modal Hashing Method Robust to Noisy Training Image-Text Correspondences in Remote Sensing
The proposed CHNR includes two training phases: i) meta-learning phase that uses a small portion of clean (i. e., reliable) data to train the noise detection module in an adversarial fashion; and ii) the main training phase for which the trained noise detection module is used to identify noisy correspondences while the hashing module is trained on the noisy multi-modal training set.
Deep Metric Learning-Based Semi-Supervised Regression With Alternate Learning
The proposed DML-S2R method aims to mitigate the problems of insufficient amount of labeled samples without collecting any additional sample with a target value.
A Novel Self-Supervised Cross-Modal Image Retrieval Method In Remote Sensing
Due to the availability of multi-modal remote sensing (RS) image archives, one of the most important research topics is the development of cross-modal RS image retrieval (CM-RSIR) methods that search semantically similar images across different modalities.
Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing
To address this problem, in this paper we introduce a novel deep unsupervised cross-modal contrastive hashing (DUCH) method for RS text-image retrieval.
Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods
The collection of a high number of pixel-based labeled training samples for tree species identification is time consuming and costly in operational forestry applications.
A Novel Framework to Jointly Compress and Index Remote Sensing Images for Efficient Content-Based Retrieval
We also introduce a two stage learning strategy with gradient manipulation techniques to obtain image representations that are compatible with both RS image indexing and compression.
A Novel Graph-Theoretic Deep Representation Learning Method for Multi-Label Remote Sensing Image Retrieval
Unlike the other graph-based methods, the proposed method contains a novel learning strategy to train a deep neural network for automatically predicting a graph structure of each RS image in the archive.
BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval
In our experiments, we show the potential of BigEarthNet-MM for multi-modal multi-label image retrieval and classification problems by considering several state-of-the-art DL models.
Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval
The proposed method selects a small set of the most representative and informative triplets based on two main steps.
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification
To address this problem, the publicly available thematic products, which can include noisy labels, can be used to annotate RS images with zero-labeling cost.
A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification
This paper analyzes and compares different deep learning loss functions in the framework of multi-label remote sensing (RS) image scene classification problems.
Remote Sensing Image Scene Classification with Deep Neural Networks in JPEG 2000 Compressed Domain
This is achieved by taking codestreams associated with the coarsest resolution wavelet sub-band as input to approximate finer resolution sub-bands using a number of transposed convolutional layers.
SD-RSIC: Summarization Driven Deep Remote Sensing Image Captioning
The first step obtains the standard image captions by jointly exploiting convolutional neural networks (CNNs) with long short-term memory (LSTM) networks.
Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives
This chapter presents recent advances in content based image search and retrieval (CBIR) systems in remote sensing (RS) for fast and accurate information discovery from massive data archives.
Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration
Interferometric phase restoration has been investigated for decades and most of the state-of-the-art methods have achieved promising performances for InSAR phase restoration.
BigEarthNet Dataset with A New Class-Nomenclature for Remote Sensing Image Understanding
This is achieved by interpreting and arranging the CLC Level-3 nomenclature based on the properties of Sentinel-2 images in a new nomenclature of 19 classes.
An Approach to Super-Resolution of Sentinel-2 Images Based on Generative Adversarial Networks
Then, we formulate the adversarial learning of the generator and discriminator networks as a min-max game.
Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images
Hashing methods have been recently found very effective in retrieval of remote sensing (RS) images due to their computational efficiency and fast search speed.
A Novel Multi-Attention Driven System For Multi-Label Remote Sensing Image Classification
The first module aims to extract preliminary local descriptors of RS image bands that can be associated to different spatial resolutions.
BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding
This paper presents the BigEarthNet that is a new large-scale multi-label Sentinel-2 benchmark archive.
