SYNOPSIS

Fine spatial resolution sensor of optical remote sensing provides detailed information of the Earth’s surface. There are ~40 satellite systems with a spatial resolution < 3 m, such as IKONOS, QuickBird, WorldView, etc. For example, QuickBird can offer spatial resolution of 0.6 m in its panchromatic band. The main drawback of the fine system is the cost of the imagery. The basic image product, without pre-processing, will cost ~US$20/km2. Processed imagery may increase the cost several times. On the other hand, moderate and coarse resolution sensors provide inexpensive solution for this issue. For example, Système Probatoire d’Observation de la Terre (SPOT) with a spatial resolution of 4-10 m, costs only ~US$5/km2. However, the moderate and coarse systems provide less detailed information about the land cover on the Earth’s surface. Several attempts were made to increase the spatial resolution of the remote sensing imagery and provide land cover classification at a subpixel scale using a variety of super-resolution mapping (SRM) algorithms. This talk presents state-of-the-art SRM algorithms and highlight key challenges for the future research in this field.


BIODATA OF SPEAKER

Anuar Mikdad Muad received a Bachelor and Master Degrees in Electrical, Electronic and Systems Engineering from Universiti Kebangsaan Malaysia in 1999 and 2005, respectively. He received a PhD. degree in Remote Sensing from the University of Nottingham, United Kingdom in 2011. He has two years working experience in developing digital image radiography system that is used in non-destructive testing (NDT) industries while working for Malaysia Nuclear Agency from 1999 to 2001. He is currently a senior lecturer in the Department of Electrical, Electronic and Systems Engineering at Universiti Kebangsaan Malaysia. His main research interest are signal and image processing, and machine learning especially for remote sensing applications. He serves as a regular reviewer for articles in IEEE Transactions on Geoscience and Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, and International Journal of Remote Sensing.