8 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…
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…