Transcript Slide 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
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
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.
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 postprocessing.
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.
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seed pixels Sij
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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:
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.
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.
Results of Segmentation
Band Samples
Band 1: wavelength = 412 nm
Band 2: wavelength = 433 nm
Results of Segmentation
More Band Samples
Band 3: wavelength = 488 nm
Band 7: wavelength = 678
Results of Segmentation
Initial results from region growing
Threshold = 0.05
Threshold = 0.03
Results of Segmentation
Initial results from region growing
Threshold = 0.01
Threshold = 0.005
Results of Segmentation
Initial results from region growing
Threshold = 0.003
Threshold = 0.001
Results of Segmentation
After merge small region ( size threshold: 150)
Threshold = 0.05
Threshold = 0.03
Results of Segmentation
After merge small region ( size threshold: 150)
Threshold = 0.01
Threshold = 0.005
Results of Segmentation
After merge small region ( size threshold: 150)
Threshold = 0.003
Threshold = 0.001
Results of Segmentation
The change of number of regions using different threshold
and after merging the small region that contains few number
of pixels.
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.
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.
Thank you for your attention!
Any Questions?