Class-prioritized compression of multispectral imagery data

A joint classification-compression scheme that provides the user with added capability to prioritize classes of interest in the compression process is proposed. The dual compression system includes a primary unit for conventional coding of a multispectral image set followed by an auxiliary unit to code the resulting error induced on pixel vectors that represent classes of interest. This technique effectively allows classes of interest in the scene to be coded at a relatively higher level of precision than nonessential classes. Prioritized classes are selected from a thematic map or directly specified by their unique spectral signatures. Using the specified spectral signatures of the prioritized classes as end members, a modified linear spectral unmixing procedure is applied to the original data as well as to the decoded data. The resulting two sets of concentration maps, which represent classes prioritized before and after compression, are compared and the differences between them are coded via an auxiliary compression unit and transmitted to the receiver along with a conventionally coded image set. At the receiver, the differences found are blended back into the decoded data for enhanced restoration of the prioritized classes. The utility of this approach is that it works with any multispectral compression scheme. This method has been applied to test the imagery from various platforms including NOAA’s AVHRR (1.1 km GSD), and LANDSAT 5 TM (30 m GSD), LANDSAT 5 MSS (79 m GSD).

[1]  Andrew G. Tescher,et al.  Viable end-member selection scheme for spectral unmixing of multispectral satellite imagery data , 1999, Optics & Photonics.

[2]  Brian Curtiss,et al.  A method for manual endmember selection and spectral unmixing , 1996 .

[3]  Jeffrey Scott Vitter,et al.  New methods for lossless image compression using arithmetic coding , 1991, [1991] Proceedings. Data Compression Conference.

[4]  Scott D. Briles Real-time onboard hyperspectral-image compression system for a parallel push broom sensor , 1997, Defense, Security, and Sensing.

[5]  Fabio Maselli,et al.  Multiclass spectral decomposition of remotely sensed scenes by selective pixel unmixing , 1998, IEEE Trans. Geosci. Remote. Sens..

[6]  Andrew G. Tescher,et al.  Environmental data compression issues , 1997, Optics & Photonics.

[7]  N. A. Quarmby,et al.  Linear mixture modelling applied to AVHRR data for crop area estimation , 1992 .

[8]  Andrew G. Tescher,et al.  Spectral-signature-preserving compression of multispectral data , 1999 .

[9]  Douglas W. Couwenhoven,et al.  ADPCM for advanced LANDSAT downlink applications , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[10]  Allen Gersho,et al.  Feature predictive vector quantization of multispectral images , 1992, IEEE Trans. Geosci. Remote. Sens..

[11]  Allen Gersho,et al.  Variable-rate multistage vector quantization of multispectral imagery with greedy bit allocation , 1993, Other Conferences.

[12]  Sandeep Jaggi Investigative study of multispectral lossy data compression using vector quantization , 1992, Defense, Security, and Sensing.

[13]  Glen P. Abousleman Adaptive wavelet coding of hyperspectral imagery , 1996, Defense + Commercial Sensing.

[14]  John R. Schott,et al.  Spectrally and spatially adaptive hyperspectral data compression , 1996, Optics + Photonics.

[15]  Yosio Edemir Shimabukuro,et al.  The least-squares mixing models to generate fraction images derived from remote sensing multispectral data , 1991, IEEE Trans. Geosci. Remote. Sens..

[16]  S. S. Shen,et al.  Effects of multispectral compression on machine exploitation , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[17]  Glen P. Abousleman,et al.  Coding of hyperspectral imagery using adaptive classification and trellis-coded quantization , 1997, Defense, Security, and Sensing.

[18]  John F. Arnold,et al.  Lossy compression of hyperspectral data using vector quantization , 1997 .

[19]  Michael W. Marcellin,et al.  Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT , 1995, IEEE Trans. Geosci. Remote. Sens..

[20]  J. Settle,et al.  Linear mixing and the estimation of ground cover proportions , 1993 .

[21]  Yao Wang,et al.  Segmented Adaptive DPCM for Lossy Compression of Multispectral MR Images , 1997, J. Vis. Commun. Image Represent..

[22]  John A. Antoniades,et al.  Use of filter vectors in hyperspectral data analysis , 1995, Optics & Photonics.

[23]  Andrew G. Tescher,et al.  Spaced-based data compression issues , 1999, J. Electronic Imaging.

[24]  Luciano Alparone,et al.  Advantages of bidirectional spectral prediction for the reversible compression of multispectral data , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[25]  John F. Arnold,et al.  The lossless compression of AVIRIS images by vector quantization , 1997, IEEE Trans. Geosci. Remote. Sens..

[26]  Chikkannan Eswaran,et al.  Neural network based lossless coding schemes for telemetry data , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[27]  Michael W. Marcellin,et al.  Hyperspectral image compression using entropy-constrained predictive trellis coded quantization , 1997, IEEE Trans. Image Process..

[28]  Andrew G. Tescher,et al.  Practical issues for transform coding of multispectral imagery , 1995, Remote Sensing.

[29]  Rajesh Hingorani,et al.  Multispectral KLT-wavelet data compression for Landsat thematic mapper images , 1992, Data Compression Conference, 1992..

[30]  Ashok K. Rao,et al.  Multispectral Data Compression Using , 1996 .

[31]  Shen-En Qian,et al.  Study of real-time lossless data compression for hyperspectral imagery , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[32]  J. Saghri,et al.  Near-lossless bandwidth compression for radiometric data , 1991 .

[33]  R. E. Roger,et al.  Lossless compression of AVIRIS images , 1996, IEEE Trans. Image Process..

[34]  Shen-En Qian,et al.  Impact of vector quantization compression on the surface reflectance retrieval: a case study , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[35]  Alan R. Gillespie,et al.  Vegetation in deserts. I - A regional measure of abundance from multispectral images. II - Environmental influences on regional abundance , 1990 .