1 code implementation • 29 Jan 2024 • 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.
1 code implementation • 12 Jul 2023 • Mete Ahishali, Mehmet Yamac, Serkan Kiranyaz, Moncef Gabbouj
In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task.
no code implementations • 19 Apr 2023 • Sertac Kilickaya, Mete Ahishali, Fahad Sohrab, Turker Ince, Moncef Gabbouj
Considering the imbalanced labels of the LULC classification problem and rich spectral information (high number of dimensions), the proposed classification approach is well-suited for HSI data.
no code implementations • 19 Apr 2023 • Ozer Can Devecioglu, Mete Ahishali, Fahad Sohrab, Turker Ince, Moncef Gabbouj
As a consequence of global warming and climate change, the risk and extent of wildfires have been increasing in many areas worldwide.
1 code implementation • 29 Sep 2022 • Mete Ahishali, Aysen Degerli, Serkan Kiranyaz, Tahir Hamid, Rashid Mazhar, Moncef Gabbouj
The proposed restoration approach achieves over 90% F1-Score which is significantly higher than the performance of any deep model.
1 code implementation • 20 Feb 2022 • Mete Ahishali, Serkan Kiranyaz, Iftikhar Ahmad, Moncef Gabbouj
The band selection in the hyperspectral image (HSI) data processing is an important task considering its effect on the computational complexity and accuracy.
2 code implementations • 27 Jun 2021 • Mete Ahishali, Mehmet Yamac, Serkan Kiranyaz, Moncef Gabbouj
To the best of our knowledge, this is the first representation-based method proposed for performing a regression task by utilizing the modified CSENs; and hence, we name this novel approach as Representation-based Regression (RbR).
no code implementations • 28 Jan 2021 • Aysen Degerli, Mete Ahishali, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Moncef Gabbouj
To address this need, in this study, we propose a reliable COVID-19 detection network: ReCovNet, which can discriminate COVID-19 pneumonia from 14 different thoracic diseases and healthy subjects.
no code implementations • 10 Nov 2020 • Mete Ahishali, Serkan Kiranyaz, Turker Ince, Moncef Gabbouj
In this work, to address the limitations of traditional ML and deep CNN based methods, a novel and systematic classification framework is proposed for the classification of PolSAR images, based on a compact and adaptive implementation of CNNs using a sliding-window classification approach.
no code implementations • 26 Sep 2020 • Aysen Degerli, Mete Ahishali, Mehmet Yamac, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Khalid Hameed, Tahir Hamid, Rashid Mazhar, Moncef Gabbouj
To accomplish this, we have compiled the largest dataset with 119, 316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human-machine approach.
1 code implementation • 7 Jun 2020 • Mete Ahishali, Aysen Degerli, Mehmet Yamac, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Khalid Hameed, Tahir Hamid, Rashid Mazhar, Moncef Gabbouj
The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage.
no code implementations • 8 May 2020 • Mehmet Yamac, Mete Ahishali, Aysen Degerli, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Moncef Gabbouj
Any technological tool that can be provided to healthcare practitioners to save time, effort, and possibly lives has crucial importance.
no code implementations • 2 Mar 2020 • Mehmet Yamac, Mete Ahishali, Serkan Kiranyaz, Moncef Gabbouj
Indeed, a vast majority of them use sparse signal recovery techniques to obtain support sets instead of directly mapping the non-zero locations from denser measurements (e. g., Compressively Sensed Measurements).
no code implementations • 20 Jun 2019 • Mehmet Yamac, Mete Ahishali, Nikolaos Passalis, Jenni Raitoharju, Bulent Sankur, Moncef Gabbouj
Security monitoring via ubiquitous cameras and their more extended in intelligent buildings stand to gain from advances in signal processing and machine learning.