Search Results for author:
Found 23 papers, 3 papers with code
Exploring Masked Autoencoders for Sensor-Agnostic Image Retrieval in Remote Sensing
We finally derive a guideline to exploit masked image modeling for uni-modal and cross-modal CBIR problems in RS.
Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification
To this end, we initially provide a systematic review of the FL algorithms presented in the computer vision community for image classification problems, and select several state-of-the-art FL algorithms based on their effectiveness with respect to training data heterogeneity across clients (known as non-IID data).
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.
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.
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.
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.
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 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 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.
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.
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.
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.
Multisource Region Attention Network for Fine-Grained Object Recognition in Remote Sensing Imagery
Fine-grained object recognition concerns the identification of the type of an object among a large number of closely related sub-categories.
Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery
Fine-grained object recognition that aims to identify the type of an object among a large number of subcategories is an emerging application with the increasing resolution that exposes new details in image data.
