Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction
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Transcript of Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction
Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction
Presented by: Robert Lipscomb and Hemalatha Sampath
What is a hyperspectral image?
• Think of a hyperspectral image as series of pictures (or bands) of the same target in a single state
– Each band represents a view of that target using a different wave length
– Details not visible to the human eye can be determined from these additional views and become beneficial when predicting the weather, studying geology, etc.
– When the bands are stacked together they create a 3-Dimensional cube that represents the image
– Each band can be accessed and processed individually
Our Image Data
• 512 pixels x 512 pixels x 224 bands
• Used a block of 128x128 pixels from each band due to large processing times
• 16-bit representation used for each pixel
• The results from the paper used all of the pixels in each band and the image size was 512x614x224
Example
Original Bands 4,14,18,30,50,100,150,160,200 of the Moffett Field Image
Low-Complexity Algorithms
• A large majority of these images are obtained from detectors aboard spacecrafts which have strict power limitations
• Other more advanced methods have been proposed, but most are of a high complexity
• Algorithms must be of low complexity because low processing times lead to a smaller power consumption
Dimensions
• A hyperspectral image has 2 types of correlations
• Spatial (Intraband correlation)– ith(row) and jth(column) dimension
• Spectral (Interband correlation)– Kth(band) dimension
LP (Linear Prediction) Step: 1
Xi,j-1 Xi,j
Xi-1,j-1 Xi-1,j
a
bc
d IB = {1….8} so the first eight bands are predicted using the intraband median predictor
-each band will be encoded within the spatial domain so the kth dimension will not be used
Xi,j predicted = median[ c, a, c + a – b]
Ei,j (Error) = (Xi,j - Xi,j predicted)
-The errors are stored for each predicted value and this matrix of value is sent to the encoder to be compressed
-This predictor takes advantage of the spatial correlation within the band
LP (Linear Prediction) Step: 2
X i,j-1,k-1 X i,j,k-1
X i-,j-1,k-1 X i-1,j,k-1
X i,j-,k X i,j,k
X i-1,j-,k X i-1,j,k
-The remaining 216 bands need to be predicted using interband linear predictor
-Because this is now an interband predictor the kth dimension will be used
a
b c
d e h
f g
K-1 band K band (current band)
Difference 1,k = e - aDifference 2,k = g - cDifference 3,k = f - b
X i,j,k predicted = d + (Diff1+Diff2+Diff3)/3
E i,j (Error) = (Xi,j,k - Xi,j,k predicted)
-Once again the Error values are stored in a matrix and sent to the encoder
-This method takes advantage of the spectral correlation in the images
SLSQ(Linear Prediction) Step: 1
X i,j-1,k-1 X i,j,k-1
X i-,j-1,k-1 X i-1,j,k-1
a
b c
d
K-1 band
X i,j-1,k-1 X i,j,k-1
X i-,j-1,k-1 X i-1,j,k-1
a
b c
d
K-1 bandX i,j-1,k-1 X i,j,k-1
X i-,j-1,k-1 X i-1,j,k-1a
b c
d
K-1 band
X i,j-1,k-1 X i,j,k-1
X i-,j-1,k-1 X i-1,j,k-1
a
b c
d
K-1 band
X i,j-1,k-1 X i,j,k-1
X i-,j-1,k-1 X i-1,j,k-1
a
b c
d
K-1 band