129 citations · 420 across the 22 of their papers we have counts for
22 papers
Cross-Domain Dead Tree Detection via Knowledge Distillation in Aerial Imagery
Anis Ur Rahman, Mete Ahishali, Einari Heinaro +1
Detecting dead trees in aerial imagery is vital for assessing forest health, especially as tree mortality increases globally due to climate change, but domain variability and scarc…
Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation
Mete Ahishali, Anis Ur Rahman, Einari Heinaro +2
Mapping standing dead trees is crucial for acquiring information on the effects of climate change on forests and forest biodiversity. However, leveraging high-quality aerial imager…
Multi-Scale Tensorial Summation and Dimensional Reduction Guided Neural Network for Edge Detection
Lei Xu, Mehmet Yamac, Mete Ahishali +1
Edge detection has attracted considerable attention thanks to its exceptional ability to enhance performance in downstream computer vision tasks. In recent years, various deep lear…
ADA-Net: Attention-Guided Domain Adaptation Network with Contrastive Learning for Standing Dead Tree Segmentation Using Aerial Imagery
Mete Ahishali, Anis Ur Rahman, Einari Heinaro +1
Information on standing dead trees is important for understanding forest ecosystem functioning and resilience but has been lacking over large geographic regions. Climate change has…
Dual-Task Learning for Dead Tree Detection and Segmentation with Hybrid Self-Attention U-Nets in Aerial Imagery
Anis Ur Rahman, Einari Heinaro, Mete Ahishali +1
Mapping standing dead trees is critical for assessing forest health, monitoring biodiversity, and mitigating wildfire risks, for which aerial imagery has proven useful. However, de…
Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description
Sertac Kilickaya, Mete Ahishali, Cansu Celebioglu +5
The frequent breakdowns and malfunctions of industrial equipment have driven increasing interest in utilizing cost-effective and easy-to-deploy sensors, such as microphones, for ef…
Echocardiography to Cardiac MRI View Transformation for Real-Time Blind Restoration
Ilke Adalioglu, Serkan Kiranyaz, Mete Ahishali +5
Echocardiography is the most widely used imaging to monitor cardiac functions, serving as the first line in early detection of myocardial ischemia and infarction. However, echocard…
Dropout Concrete Autoencoder for Band Selection on HSI Scenes
Lei Xu, Mete Ahishali, Moncef Gabbouj
Deep learning-based informative band selection methods on hyperspectral images (HSI) recently have gained intense attention to eliminate spectral correlation and redundancies. Howe…
SAF-Net: Self-Attention Fusion Network for Myocardial Infarction Detection using Multi-View Echocardiography
Ilke Adalioglu, Mete Ahishali, Aysen Degerli +2
Myocardial infarction (MI) is a severe case of coronary artery disease (CAD) and ultimately, its detection is substantial to prevent progressive damage to the myocardium. In this s…
Operational Support Estimator Networks
Mete Ahishali, Mehmet Yamac, Serkan Kiranyaz +1
In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task. Support Estimation (SE) is defined as finding the l…
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification
Sertac Kilickaya, Mete Ahishali, Fahad Sohrab +2
Hyperspectral image (HSI) classification is an important task in many applications, such as environmental monitoring, medical imaging, and land use/land cover (LULC) classification…
Improved Active Fire Detection using Operational U-Nets
Ozer Can Devecioglu, Mete Ahishali, Fahad Sohrab +2
As a consequence of global warming and climate change, the risk and extent of wildfires have been increasing in many areas worldwide. Warmer temperatures and drier conditions can c…
R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 Classification
Mete Ahishali, Aysen Degerli, Serkan Kiranyaz +3
Restoration of poor quality images with a blended set of artifacts plays a vital role for a reliable diagnosis. Existing studies have focused on specific restoration problems such…
SRL-SOA: Self-Representation Learning with Sparse 1D-Operational Autoencoder for Hyperspectral Image Band Selection
Mete Ahishali, Serkan Kiranyaz, Iftikhar Ahmad +1
The band selection in the hyperspectral image (HSI) data processing is an important task considering its effect on the computational complexity and accuracy. In this work, we propo…
Representation Based Regression for Object Distance Estimation
Mete Ahishali, Mehmet Yamac, Serkan Kiranyaz +1
In this study, we propose a novel approach to predict the distances of the detected objects in an observed scene. The proposed approach modifies the recently proposed Convolutional…
Reliable COVID-19 Detection Using Chest X-ray Images
Aysen Degerli, Mete Ahishali, Serkan Kiranyaz +2
Coronavirus disease 2019 (COVID-19) has emerged the need for computer-aided diagnosis with automatic, accurate, and fast algorithms. Recent studies have applied Machine Learning al…
Classification of Polarimetric SAR Images Using Compact Convolutional Neural Networks
Mete Ahishali, Serkan Kiranyaz, Turker Ince +1
Classification of polarimetric synthetic aperture radar (PolSAR) images is an active research area with a major role in environmental applications. The traditional Machine Learning…
COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
Aysen Degerli, Mete Ahishali, Mehmet Yamac +6
Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numer…
Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
Mete Ahishali, Aysen Degerli, Mehmet Yamac +6
Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning…
Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
Mehmet Yamac, Mete Ahishali, Aysen Degerli +3
Coronavirus disease (Covid-19) has been the main agenda of the whole world since it came in sight in December 2019. It has already caused thousands of causalities and infected seve…
Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing
Mehmet Yamac, Mete Ahishali, Serkan Kiranyaz +1
Support estimation (SE) of a sparse signal refers to finding the location indices of the non-zero elements in a sparse representation. Most of the traditional approaches dealing wi…
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing
Mehmet Yamac, Mete Ahishali, Nikolaos Passalis +3
Security monitoring via ubiquitous cameras and their more extended in intelligent buildings stand to gain from advances in signal processing and machine learning. While these innov…