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The Spectral Imaging and Source Mapping (SISM) project advances Australian capabilities in key emerging sensing technologies beyond the visible spectrum with a strong focus in national prosperity and well being. The project is one of the first of its kind worldwide combining a broad range of technologies to “see” beyond the visible spectrum and addressing the spectral image understanding problem making use of structural and statistical pattern recognition and computer vision techniques so as to develop imaging and sensing technologies with applications in health, food quality control, process control and safety and security.

The project has two main areas

• Hyperspectral sensing

Hyperspectral imaging is an information-rich representation of the object under study in which each pixel or sample is comprised by a number of wavelength-indexed measurements. As a result, hyperspectral sensing is particularly well suited for non-intrusive material identification and recognition tasks with application in the areas of security, environment and biology.

• Thermal and near-IR

Thermal and near-infrared imaging is a key technology in night-vision and surveillance applications. Thermography can also be used to monitor blood flow, body temperature and perform early, non-intrusive diagnosis of some kinds of cancer.

Since spectral imaging is non-intrusive and non-destructive, we are working to provide more efficient, accurate, non-invasive and economical ways of monitoring the environment for preservation and security. To this end, we are developing diagnostic instruments and methods aimed at Biosecurity Plant health Food quality control

This technology can, potentially, save farmers and quarantine officers hundreds of hours in laborious and time-consuming pest inspections. As a result, the project produces research relevant to Australia’s DPI future in a multidisciplinary environment with collaborators from a number of state-government departments and research institutions nationwide. We are focusing on Early detection of plant pathogens Smart trapping technologies for plant pests

These efforts have yielded promising preliminary results on the identification of a number of pests, such as some varieties of fruit fly and moth. Other promising results are those pertaining plant pathogens such as apple scab and wheat rust, amongst others.

What are the key features?

• The project produces internationally competitive research on methods aimed at
  

• Hyperspectral image understanding and spectroscopy techniques
• Image database search and retrieval
• Reliable material recognition and identification algorithms with a large margin or accuracy and very low false negative rates.

• It has produced more than 30 peer-reviewed publications in first-tier academic venues. It has also demonstrated the technologies developed at the project in a number of commercial and academic venues.
  
• The project has the capability to integrate hyperspectral imaging and sensing systems as turnkey, general purpose research vehicles.
• It provides education and training on the use of close range, ground-based spectroscopy systems.
  

Research team

Jun Zhou, Tim Raupach, Ran Wei, Antonio Robles-Kelly, Zhouyu Fu, Surya Prakash, Pattaraporn Khuwuthyakorn, Quang Anh Nguyen, Cong Phuoc Huynh

Participants

• Australian National University
• Collaborative Research Centre for National Plant Biosecurity, Australia
  
• University of York, United Kingdom

Selected Publications

Journals

Conferences 

• §J. Zhou and A. Robles-Kelly, A Quasi-random Sampling Approach to Image Retrieval, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008.
• §C. P. Huynh and A. Robles-Kelly, A NURBS-Based Spectral Reflectance Descriptor with Applications in Computer Vision and Pattern Recognition, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008.

For a complete list of publications, click here  For further information e-mail antonio.robles-kelly@nicta.com.au

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