Hyperspectral Image Compression: Adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding

Abstract

Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties.

Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

“…1.5 to 3.5 dB, than JPEG2000 multi-component at various compression bit rates (Tang and Pearlman 2006b). Nevertheless, 3D SPIHT provided comparable results to JPEG2000 multicomponent as shown by Christophe et al (Christophe, Mailhes, and Duhamel 2008).…”

“…Hyperspectral imagery (HSI), through capturing hundreds of bands in a broad spectral range covering visible, near-infrared and beyond, has proved successful in many areas (Christophe, Mailhes, and Duhamel 2008). Typical examples include traditional applications in remote sensing and military surveillance (Zhao et al 2013) as well as newly emerging platforms for food quality analysis (Sun 2010;Kelman, Ren, and Marshall 2013), pharmaceutical (Roggo et al 2005) and forensics/security (Payne et al 2007;Brewer et al 2008;Gill et al 2011).…”

A Simple and Fast Algorithm for Image Coding Based on SPIHT

“…SPIHT can be very useful for applications where the user can quickly inspect the image and decide if it should be really downloaded. Therefore, since a few years ago, there has been a growing interest in the SPIHT [4,[7][8][9][10]. Even when SPIHT is not a standard, it is used in many video applications that use image compression with the embedded coding property [11][12] and medical imaging applications with SPIHT as a core [10,[13][14][15].…”

Modified set partitioning in hierarchical trees algorithm based on hierarchical subbands

“…7 Since a few years ago, there has been a growing interest in the set partitioning in hierarchical trees (SPIHT) coder that is an improved version of the embedded zerotree wavelet algorithm. [8][9][10][11][12] SPIHT-based image compression can achieve rate scalability and high coding performance. [13][14][15][16][17][18][19][20] The encoder makes use of progressive transmission to exploit the selfsimilarity of the DWT coefficients across different scales.…”