Recent advances in the sensor technologies broaden the scope of hyperspectral imaging in many real applications. These include not only conventional Earth observation, but also in close-range remote sensing applications, e.g., medical diagnosis, precision agriculture and food inspection, where hyperspectral image was able to detect targets (e.g., diseases, fruit bruise, etc.) earlier before the effects were observed with our naked eyes. This talk will review the technologies we developed to enable these findings.
Specifically, I will introduce hyperspectral image restoration and its impacts on content interpretation. Despite advances in sensor technologies, degradation (e.g., noise, blur, low resolution, etc.) cannot be avoided during the hyperspectral images’ acquisitions, which can affect information retrieval and content interpretation. The first part of my talk will present the techniques I developed to improve the image qualities (noise reduction, sharpening, resolution enhancement, etc.), with specific applications to plant disease mapping in precision agriculture and fruit bruise detection in food inspection.
Then, I will discuss how to mine important spatial information from multi-sensor images and fuse them for better analysis and interpretation. The objectives are to: (1) model and extract the attributes (e.g., size, shape, etc.) of different objects in an image for better classification, (2) optimize the integration of the complementary information from multi-sensor images to provide a more comprehensive measurement and interpretation (plant disease detection, land-cover classification).
Wenzhi Liao received the PhD Degree in Computer Science Engineering from Ghent University (Belgium) and the PhD Degree in Engineering from South China University of Technology (China) in 2012. Since 2012, he has been working first as a Postdoc at Ghent University and then as a Research Fellow for Flanders Research Foundation (FWO) at Ghent University. His current research interests include Spectral Image Processing and Interpretation, Data Fusion and Pattern Recognition. He is also highly experienced in Machine Learning, Data Fusion, Large-scale problems and Remote Sensing, with practical experience in Python/Matlab development. In particular, Wenzhi has successfully applied signal processing and machine learning in the fields of hyperspectral image restoration and interpretation, data fusion and classification of multi-modal remote sensing imagery, food sorting and precision agriculture. He is an experienced supervisor and coach of several students both at Master and PhD levels. His work has already resulted in more than 80 publications in international journals and proceedings in the field of image processing, pattern recognition and remote sensing. He received twice the “Best Paper Challenge” Awards on both 2013 IEEE GRSS Data Fusion Contest and 2014 IEEE GRSS Data Fusion Contest. Dr. Liao is a senior member of the IEEE, serving as an Associate Editor for the IET Image Processing.