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Three-Dimensional Wavelet-Based Compression of Hyperspectral Images

Create New Folder. Folder Name. Folder Description. Image compression. Hyperspectral imaging.

Graphics processing units. Computer architecture.

"Lossless compression of hyperspectral images" by Sushil Kamalchand Jain

Error analysis. Image quality. Parallel computing. Show All Keywords. Real time lossy compression of hyperspectral images using iterative error Modulated lapped transform vs JPEG implications for compression of Fast synchronization recovery for lossy image transmission with a suffix The proposed compression method is evaluated in terms of different objective metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including JPEG For the proposed hyperspectral image classification method, we utilize the sparse coefficients for training support vector machine SVM and k-nearest neighbour kNN classifiers.


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  • Three-Dimensional Wavelet-Based Compression of Hyperspectral Images.

In particular, the discriminative character of the sparse coefficients is enhanced by incorporating contextual information using local mean filters. The classification performance is evaluated and compared to a number of similar or representative methods. The results show that our approach could outperform other approaches based on SVM or sparse representation.

This thesis makes the following contributions. It provides a relatively thorough investigation of applying sparse representation to lossy hyperspectral image compression.

Specifically, it reveals the effectiveness of sparse representation for the exploitation of spectral correlations in hyperspectral images. In addition, we have shown that the discriminative character of sparse coefficients can lead to superior performance in hyperspectral image classification. Size: Format: PDF. Description: Main article.