Multimodal Image and Spectral Feature Learning for Efficient Analysis of Water-Suspended Particles

Tomoko Takahashi, Zonghua Liu, Thangavel Thevar, Nicholas Burns, Dhugal Lindsay, John Watson, Sumeet Mahajan, Satoru Yukioka, SHUHEI TANAKA, YUKIKO NAGAI, Blair Thornton

Research output: Contribution to journalArticlepeer-review


We have developed a method to combine morphological and chemical information for the accurate identification of different particle types using optical measurement techniques that require no sample preparation. A combined holographic imaging and Raman spectroscopy setup is used to gather data from six different types of marine particles suspended in a large volume of seawater. Unsupervised feature learning is performed on the images and the spectral data using convolutional and single layer autoencoders. The learned features are combined, where we demonstrate that non-linear dimensional reduction of the combined multimodal features can achieve a high clustering macro F1 score of 0.88, compared to a maximum of 0.61 when only image or spectral features are used. The method can be applied to long-term monitoring of particles in the ocean without the need for sample collection. In addition, it can be applied to data from different types of sensor measurements without significant modifications.
Original languageEnglish
JournalOptics Express
Publication statusAccepted/In press - 12 Jan 2023


Dive into the research topics of 'Multimodal Image and Spectral Feature Learning for Efficient Analysis of Water-Suspended Particles'. Together they form a unique fingerprint.

Cite this