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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
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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 a 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 d b c Xi,j predicted = median[ c, a, c + a – b] Ei,j (Error) = (Xi,j - Xi,jpredicted) -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 -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 K-1 band K band (current band) d a e h g b c f Difference 1,k = e - a X i,j,k predicted = d + (Diff1+Diff2+Diff3)/3 E i,j (Error) = (Xi,j,k - Xi,j,k predicted) Difference 2,k = g - c Difference 3,k = f - b • 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 K-1 band K-1 band K-1 band K-1 band K-1 band d d d d d a a a a a b b b b b c c c c c