Search Results for author: Begüm Demir

Found 43 papers, 7 papers with code

Estimating Physical Information Consistency of Channel Data Augmentation for Remote Sensing Images

no code implementations21 Mar 2024 Tom Burgert, Begüm Demir

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.

Data Augmentation Multi-Label Image Classification +1

Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment

no code implementations15 Feb 2024 Angelos Zavras, Dimitrios Michail, Begüm Demir, Ioannis Papoutsis

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.

Cross-Modal Retrieval Image Classification +1

Radio Map Estimation -- An Open Dataset with Directive Transmitter Antennas and Initial Experiments

no code implementations12 Jan 2024 Fabian Jaensch, Giuseppe Caire, Begüm Demir

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

no code implementations10 Nov 2023 Barış Büyüktaş, Gencer Sumbul, Begüm Demir

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).

Federated Learning Image Classification +2

Ben-ge: Extending BigEarthNet with Geographical and Environmental Data

1 code implementation4 Jul 2023 Michael Mommert, Nicolas Kesseli, Joëlle Hanna, Linus Scheibenreif, Damian Borth, Begüm Demir

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.

Land Cover Classification

Annotation Cost Efficient Active Learning for Content Based Image Retrieval

no code implementations20 Jun 2023 Julia Henkel, Genc Hoxha, Gencer Sumbul, Lars Möllenbrok, Begüm Demir

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.

Active Learning Content-Based Image Retrieval +2

Label Noise Robust Image Representation Learning based on Supervised Variational Autoencoders in Remote Sensing

no code implementations14 Jun 2023 Gencer Sumbul, Begüm Demir

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.

Representation Learning

Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images

no code implementations12 Jun 2023 Lars Möllenbrok, Begüm Demir

In recent years, deep neural networks (DNNs) have been found very successful for multi-label classification (MLC) of remote sensing (RS) images.

Active Learning Multi-Label Classification

Transformer-based Multi-Modal Learning for Multi Label Remote Sensing Image Classification

no code implementations2 Jun 2023 David Hoffmann, Kai Norman Clasen, Begüm Demir

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.

Image Classification Multi-Label Classification +1

LiT-4-RSVQA: Lightweight Transformer-based Visual Question Answering in Remote Sensing

no code implementations1 Jun 2023 Leonard Hackel, Kai Norman Clasen, Mahdyar Ravanbakhsh, Begüm Demir

Visual question answering (VQA) methods in remote sensing (RS) aim to answer natural language questions with respect to an RS image.

Question Answering Visual Question Answering

Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning

no code implementations1 Jun 2023 Barış Büyüktaş, Gencer Sumbul, Begüm Demir

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.

Federated Learning Image Classification

Generative Adversarial Networks for Spatio-Spectral Compression of Hyperspectral Images

no code implementations15 May 2023 Akshara Preethy Byju, Martin Hermann Paul Fuchs, Alisa Walda, Begüm Demir

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.

Image Compression

Deep Active Learning for Multi-Label Classification of Remote Sensing Images

no code implementations2 Dec 2022 Lars Möllenbrok, Gencer Sumbul, Begüm Demir

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.

Active Learning Clustering +2

Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learning

1 code implementation2 Dec 2022 Gencer Sumbul, Begüm Demir

Our approach aims to model the complementary characteristics of discriminative and generative reasoning for IRL under noisy labels.

Representation Learning

Multi-Modal Fusion Transformer for Visual Question Answering in Remote Sensing

no code implementations10 Oct 2022 Tim Siebert, Kai Norman Clasen, Mahdyar Ravanbakhsh, Begüm Demir

To make the intrinsic information of each RS image easily accessible, visual question answering (VQA) has been introduced in RS.

Question Answering Representation Learning +1

Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from Remote Sensing Images

no code implementations5 Oct 2022 Tom-Lukas Breitkopf, Leonard W. Hackel, Mahdyar Ravanbakhsh, Anne-Karin Cooke, Sandra Willkommen, Stefan Broda, Begüm Demir

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.

Nutrition

Satellite Image Search in AgoraEO

no code implementations23 Aug 2022 Ahmet Kerem Aksoy, Pavel Dushev, Eleni Tzirita Zacharatou, Holmer Hemsen, Marcela Charfuelan, Jorge-Arnulfo Quiané-Ruiz, Begüm Demir, Volker Markl

To address this limitation, we have recently proposed MiLaN, a content-based image retrieval approach for fast similarity search in satellite image archives.

Content-Based Image Retrieval Deep Hashing +1

On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification

no code implementations28 Jul 2022 Tom Burgert, Mahdyar Ravanbakhsh, Begüm Demir

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.

Image Classification Multi-Label Classification +1

Unsupervised Contrastive Hashing for Cross-Modal Retrieval in Remote Sensing

no code implementations19 Apr 2022 Georgii Mikriukov, Mahdyar Ravanbakhsh, Begüm Demir

To address this problem, in this paper we introduce a novel unsupervised cross-modal contrastive hashing (DUCH) method for text-image retrieval in RS.

Binarization Cross-Modal Retrieval +2

Coreset of Hyperspectral Images on Small Quantum Computer

no code implementations10 Apr 2022 Soronzonbold Otgonbaatar, Mihai Datcu, Begüm Demir

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

1 code implementation26 Feb 2022 Georgii Mikriukov, Mahdyar Ravanbakhsh, Begüm Demir

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.

Meta-Learning Retrieval +1

A Novel Self-Supervised Cross-Modal Image Retrieval Method In Remote Sensing

no code implementations23 Feb 2022 Gencer Sumbul, Markus Müller, Begüm Demir

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.

Image Retrieval Retrieval

Deep Metric Learning-Based Semi-Supervised Regression With Alternate Learning

no code implementations23 Feb 2022 Adina Zell, Gencer Sumbul, Begüm Demir

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.

Metric Learning regression

Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing

no code implementations20 Jan 2022 Georgii Mikriukov, Mahdyar Ravanbakhsh, Begüm Demir

To address this problem, in this paper we introduce a novel deep unsupervised cross-modal contrastive hashing (DUCH) method for RS text-image retrieval.

Binarization Cross-Modal Retrieval +2

A Novel Framework to Jointly Compress and Index Remote Sensing Images for Efficient Content-Based Retrieval

no code implementations17 Jan 2022 Gencer Sumbul, Jun Xiang, Nimisha Thekke Madam, Begüm Demir

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.

Content-Based Image Retrieval Image Compression +1

A Novel Graph-Theoretic Deep Representation Learning Method for Multi-Label Remote Sensing Image Retrieval

no code implementations1 Jun 2021 Gencer Sumbul, Begüm Demir

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.

Image Retrieval Representation Learning +1

Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification

1 code implementation19 Dec 2020 Ahmet Kerem Aksoy, Mahdyar Ravanbakhsh, Begüm Demir

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.

Image Classification Multi-Label Classification +2

A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification

no code implementations29 Sep 2020 Hichame Yessou, Gencer Sumbul, Begüm Demir

This paper analyzes and compares different deep learning loss functions in the framework of multi-label remote sensing (RS) image scene classification problems.

General Classification Image Classification +2

Remote Sensing Image Scene Classification with Deep Neural Networks in JPEG 2000 Compressed Domain

no code implementations20 Jun 2020 Akshara Preethy Byju, Gencer Sumbul, Begüm Demir, Lorenzo Bruzzone

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.

Classification General Classification +1

SD-RSIC: Summarization Driven Deep Remote Sensing Image Captioning

no code implementations15 Jun 2020 Gencer Sumbul, Sonali Nayak, Begüm Demir

The first step obtains the standard image captions by jointly exploiting convolutional neural networks (CNNs) with long short-term memory (LSTM) networks.

Image Captioning

Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives

no code implementations3 Apr 2020 Gencer Sumbul, Jian Kang, Begüm Demir

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.

Deep Hashing Image Retrieval

Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration

no code implementations6 Mar 2020 Jian Kang, Danfeng Hong, Jialin Liu, Gerald Baier, Naoto Yokoya, Begüm Demir

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.

Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images

1 code implementation2 Apr 2019 Subhankar Roy, Enver Sangineto, Begüm Demir, Nicu Sebe

Hashing methods have been recently found very effective in retrieval of remote sensing (RS) images due to their computational efficiency and fast search speed.

Computational Efficiency Deep Hashing +1

A Novel Multi-Attention Driven System For Multi-Label Remote Sensing Image Classification

no code implementations28 Feb 2019 Gencer Sumbul, Begüm Demir

The first module aims to extract preliminary local descriptors of RS image bands that can be associated to different spatial resolutions.

General Classification Image Classification +1

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