Sidike Paheding and Ashraf Saleem, both Department of Applied Computing faculty members, are guest editors for a special issue of Remote Sensing, an open access journal published by MDPI. The objective of the special issue is to provide a forum for cutting-edge research works that address the ongoing challenges in remote sensing image classification.
Special Issue topics include, but are not limited to:
- Land-use land-cover mapping
- Hyperspectral image classification
- Data fusion technologies
- High-performance computing paradigm for remote sensing image classification
- Dimensionality reduction for remote sensing data
- Spatial and spectral feature extraction methods
- Big data analytics
- Data visualization of classification results
- Data augmentation for image classification
- New image classification architectures
- New datasets for remote sensing image classification with deep learning
- Image enhancement for image classification
Publication abstract: In recent years, we have been witnessing the tremendous success of deep learning in diverse research areas and applications, ranging from natural language processing, health care, wide-area surveillance, network security, and precision agriculture. The significance of deep learning in remote sensing image analysis has also been observed and is continuously being increased. Thanks to the rapid advancement of sensors, including high-resolution RGB, thermal, Lidar, and multi-/hyper-spectral cameras, and emerging sensing platforms, such as satellites and aerial vehicles, remote sensing image scenes can now be captured by multi-temporal, multi-senor, and provide a wider view of sensing devices. This undoubtfully facilitates remote sensing research fields, and, at the same time, introduces challenges. These challenges not only bring difficulties for image analytics and interpretation but also demands more advanced computational methods. The objective of this Special Issue is to provide a forum for cutting-edge research works that address the ongoing challenges in remote sensing image classification.
Sidike Paheding is an assistant professor in the Department of Applied Computing at Michigan Tech. His research interests cover a variety of topics in image/video processing, machine learning, deep learning, computer vision, and remote sensing. Paheding has authored/coauthored over 100 research articles, and he is a recipient of the 2020 Best Paper Award from the MDPI journal Electronics and the 2021 Best Paper from the journal MDPI Remote Sensing.
Ashraf Saleem is an assistant professor in the Department of Applied Computing at Michigan Tech. His research interests are unified under the theme, “deployment of robotics systems and artificial intelligence in the field of remote sensing”, and his research focuses on solving real-life problems such as monitoring environmental pollution. He is also interested in developing real-time smart controllers for different engineering systems which include electromechanical, electro-pneumatic, and piezoelectric based systems.
Download a publication flyer below.