Pixel Clustering and Hyperspectral Image Segmentation for Ocean Colour Remote Sensing

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

3. Ocean Colour Remote Sensing using Hyperspectral Imaging • To measure phytoplankton from space and evaluate impacts of • Cyanobacteria on human health • Coccolithophore on Fisheries • 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 hyperspectraldataset 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 In represents one of band Image with size w by h. Let S represents seed and Sij 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 Sij. seed pixels Sij

6. Algorithm Step 3: The region will grow from the first seed S11 by adding its 4-connected neighbours that is most similar with mean value vector. Note that one of neighbour of Sij 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 S11. 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 Sij, let In,pq represents the grown pixels of In,, where p, q are coordinates, size represents the number of grown pixel of In, and M represents mean value vector of I, Mn represents mean value of grown pixels of In respectively, and can be expressed as:

7. Algorithm 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 Sij, then the Euclidean distance Edist between neighbour and mean value vector can be expressed as: If the Euclidean distance between Mn and an 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 Samples Band 1: wavelength = 412 nm Band 2: wavelength = 433 nm

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

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

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

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

14. Results of Segmentation After merge small region ( size threshold: 150) 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. Results of Segmentation The change of number of regions using different threshold and after merging the small region that contains few number of pixels.

18. Results of Segmentation 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?