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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

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Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction

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  1. Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction Presented by: Robert Lipscomb and Hemalatha Sampath

  2. 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

  3. 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

  4. Example Original Bands 4,14,18,30,50,100,150,160,200 of the Moffett Field Image

  5. 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

  6. Dimensions • A hyperspectral image has 2 types of correlations • Spatial (Intraband correlation) • ith(row) and jth(column) dimension • Spectral (Interband correlation) • Kth(band) dimension

  7. 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

  8. 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

  9. 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

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