Compression of hyperspectral imagery

Motta, Giovanni; Rizzo, F.; Storer, James A.

doi:10.1109/dcc.2003.1194024

Cited by 58 publications

(26 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SLSQ-OPT [8] uses linear prediction based both on pixels within and outside the current band. The standardized JPEG-LS's results are from [17]. [7] Table 2 shows BH's compression and decompression speeds.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Block-Based Inter-Band Lossless Hyperspectral Image Compressor

Slyz

Zhang

Data Compression Conference

View full text Add to dashboard Cite

Abstract:We propose a hyperspectral image compressor called BH which considers its input image as being partitioned into square blocks, each lying entirely within a particular band, and compresses one such block at a time by using the following steps: first predict the block from the corresponding block in the previous band, then select a predesigned code based on the prediction errors, and finally encode the predictor coefficient and errors. Apart from giving good compression rates and being fast, BH can provide random access to spatial locations in the image.We hypothesize that BH works well because it accommodates the rapidly changing image brightness that often occurs in hyperspectral images. We also propose an intraband compressor called LM which is worse than BH, but whose performance helps explain BH's performance.

show abstract

Section: Resultsmentioning

confidence: 99%

“…[5] uses vector quantization to clas-sify the image spectra, but then uses inter-band linear prediction. LPVQ's predictor [17] is based on vector quantization and isn't linear. It's somewhat similar to LM in that it predicts within bands, and that the predictions in one band indirectly influence the predictions in others.…”

Section: Resultsmentioning

confidence: 99%

A Block-Based Inter-Band Lossless Hyperspectral Image Compressor

Slyz

Zhang

Data Compression Conference

View full text Add to dashboard Cite

show abstract

“…M-NVQ results compare favorably with other compression techniques. Motta et al [18] proposed a VQ based algorithm that involved locally optimal design of a partitioned vector quantizer for the encoding of source vectors drawn from hyperspectral images. Pickering and Ryan [22] jointly optimized spatial M-NVQ and spectral Discrete Cosine Transform (DCT) to produce compression ratios significantly better than those obtained by the optimized spatial M-NVQ technique alone.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Wavelet-Based Compression of Hyperspectral Images

Tang

Pearlman

Hyperspectral Data Compression

120

View full text Add to dashboard Cite

Hyperspectral images may be treated as a three-dimensional data set for the purposes of compression. Here we present some compression techniques based on a three-dimensional wavelet transform that produce compressed bit streams with many useful properties. These properties are progressive quality encoding and decoding, progressive lossyto-lossless encoding, and progressive resolution decoding. We feature an embedded, block-based, image coding algorithm of low complexity, called SPECK (Set Partitioning Embedded bloCK), that has been proposed originally for single images and is modified and extended to three dimensions. The resultant algorithm, Three-Dimensional Set Partitioning Embedded bloCK (3D-SPECK), efficiently encodes 3D volumetric image data by exploiting the dependencies in all dimensions. We describe the use of this coding algorithm in two implementations, first in a purely quality or rate scalable mode and secondly in a resolution scalable mode. We utilize both integer and floating point wavelet transforms, whereby the former one enables lossy and lossless decompression from the same bit stream, and the latter one achieves better performance in lossy compression. The structure of hyperspectral images reveals spectral responses that would seem ideal candidates for compression by 3D-SPECK. We demonstrate that 3D-SPECK, a wavelet domain algorithm, like other time domain algorithms, can preserve spectral profiles ¡

show abstract

“…With very fine spectral resolution, hyperspectral images can provide better performance in object detection, classification, and identification than traditional multispectral imagery [1]- [3]. However, hyperspectral image processing and analysis are challenging due to vast data volume [4]. In particular, the Hough phenomenon [5], also called "curse of dimensionality" [6], makes many traditional image analysis algorithms infeasible.…”

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction of Hyperspectral Imagery

Yang¹

Optical Satellite Signal Processing and Enhancement

View full text Add to dashboard Cite

In this dissertation, dimensionality reduction for hyperspectral remote sensing imagery is investigated to alleviate practical application difficulties caused by high data dimension. Band selection and band clustering are applied for this purpose. Based on availability of object prior information, supervised, semi-supervised, and unsupervised techniques are proposed. To take advantage of modern computational architecture, parallel implementations on cluster and graphics processing units (GPU) are developed.The impact of dimensionality reduction on the following data analysis is also evaluated.Specific contributions are as below.1. A similarity-based unsupervised band selection algorithm is developed to select distinctive and informative bands, which outperforms other existing unsupervised band selection approaches in the literature.2. An efficient supervised band selection method based on minimum estimated abundance covariance is developed, which outperforms other frequently-used metrics.Template Created By: James Nail 2010This new method does not need to conduct classification during band selection process or examine original bands/band combinations as do traditional approaches.3. An efficient semi-supervised band clustering method is proposed, which uses class signatures to conduct band partition. Compared to traditional unsupervised clustering, computational complexity is significantly reduced.4. Parallel GPU implementations with computational cost saving strategies for the developed algorithms are designed to facilitate onboard processing.5. As an application example, band selection results are used for urban land cover classification. With a few selected bands, classification accuracy can be greatly improved, compared to the one using all the original bands or those from other frequently-used dimensionality reduction methods.ii DEDICATION I would like to dedicate this dissertation to my parents and my brother.iii ACKNOWLEDGMENTS

show abstract

Compression of hyperspectral imagery

Cited by 58 publications

References 6 publications

A Block-Based Inter-Band Lossless Hyperspectral Image Compressor

A Block-Based Inter-Band Lossless Hyperspectral Image Compressor

Three-Dimensional Wavelet-Based Compression of Hyperspectral Images

Dimensionality Reduction of Hyperspectral Imagery

Contact Info

Product

Resources

About