1. Pixel Clustering and Hyperspectral Image Segmentation for Ocean Colour Remote Sensing Xuexing Zeng 1, Jinchang Ren 1, David Mckee 2 Samantha Lavender 3 and Stephen Marshall 1 1 CeSIP, Department of Electronic & Electrical Engineering University of Strathclyde, Glasgow, G1, 1XW, U.K 2 Department of Physics, University of Strathclyde, Glasgow, G4 0NG, U.K 3 ARGANS Limited, 19 Research Way, Plymouth, PL6 8BT, U.K

2. Ocean Colour Remote Sensing using Hyperspectral Imaging Marine Spectral Reflectance Ocean colour is the measurement of spectral distribution of radiance (or reflectance) upwelling from the ocean in the visible regime. http://oceancolor.gsfc.nasa.gov

3. Ocean Colour Remote Sensing using Hyperspectral Imaging To measure phytoplankton from space and evaluate impacts of 1.Cyanobacteria on human health 2.Coccolithophore on Fisheries 3.Hurricane Floyd on natural disasters Also to measure sea surface temperature and water depth.

4. Hyperspectral Pixel Clustering and Image Segmentation for Ocean Colour Remote Sensing Region growing is proposed to classify Ocean hyperspectral dataset whilst maintain the spatial consistency. Good classification results can be obtained by simply adjusting one key parameter to specify the pixel similarity. Another parameter: size threshold is used to filter small regions as post- processing.

5. Algorithm Let I represents N bands hyperspectral image, and I n represents one of band Image with size w by h. Let S represents seed and S ij represents one of seed with coordinates i and j. Step 1: Generate one w by h zero matrix J as initial output. Step 2: Select uniformly distributed seed pixels S ij. seed pixels S ij

6. Algorithm Step 3: The region will grow from the first seed S 11 by adding its 4-connected neighbours that is most similar with mean value vector. Note that one of neighbour of S ij contains N pixels that can be represented by 1 by N vector. The initial mean value vector is just the pixel vector corresponding to the first seed S 11. After each growing, the mean value vector will be updated by the new mean value vector re-calculated on all the added pixel vectors that include seed itself. For any grown region from S ij, let I n,pq represents the grown pixels of I n,, where p, q are coordinates, size represents the number of grown pixel of I n, and M represents mean value vector of I, M n represents mean value of grown pixels of I n respectively, and can be expressed as:

7. Step 4: When the growth stops, all the added pixel will be labelled on the output matrix J, and the next seed pixel that does not yet belong to any region will be chosen and start grow again until all the seeds are grown. Euclidean distance is used to measure the similarity between pixels. Let represents the pixel values vector of one neighbour of S ij, then the Euclidean distance E dist between neighbour and mean value vector can be expressed as: Algorithm If the Euclidean distance between M n and a n is smaller than the threshold, this neighbour is considered that it is similar with this grown region, and this neighbour will be added to this growing region.

8. Results of Segmentation Dataset description: The hyperspectral ocean dataset around U.K that collected on May, 2007 will be used for classification. This dataset include 9 bands with wavelengths: 412, 433, 488, 531, 547, 667, 678, 748 and 869 nm respectively. Each band image has size 1000 by 1000 pixels. For lower bands: band 1, 2 and 3, they represent data from spectral range of blue and green thus contain more information. Higher spectrum band: band 7 contains much less information than lower bands in the dataset we used. The first 3 bands will be used for this hyperspectral ocean dataset.

9. Results of Segmentation Band 1: wavelength = 412 nmBand 2: wavelength = 433 nm Band Samples

10. Band 3: wavelength = 488 nm Results of Segmentation Band 7: wavelength = 678 More Band Samples

11. Results of Segmentation Threshold = 0.05 Threshold = 0.03 Initial results from region growing

12. Results of Segmentation Threshold = 0.01 Threshold = 0.005 Initial results from region growing

13. Results of Segmentation Threshold = 0.003 Threshold = 0.001 Initial results from region growing

14. After merge small region ( size threshold: 150) Results of Segmentation Threshold = 0.05 Threshold = 0.03

15. Results of Segmentation After merge small region ( size threshold: 150) Threshold = 0.01 Threshold = 0.005

16. Results of Segmentation After merge small region ( size threshold: 150) Threshold = 0.003 Threshold = 0.001

17. The change of number of regions using different threshold and after merging the small region that contains few number of pixels. Results of Segmentation

18. Coloured Results: Only 20 regions represented by different colour are remained, by simply merging regions if the regions have similar mean values.

19. Conclusions Pixel clustering for hyperspectral Ocean image segmentation is presented using seeded region growing. With one key parameter, the segmented results can be adjusted to preserve more or less details in the segmented results. With a size threshold for post-processing, the results can be further refined. The results from the first three bands have suggested great potential of the proposed approach in ocean colour remote sensing. Further investigation includes evaluation of various similarity metrics and statistical analysis of each region.

20. Thank you for your attention! Any Questions?