Hyperspectral image compression : adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding

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

Lunar observations verifier editor programmer's manual, revision 1

The prime purpose of the programmer's manual is to aid the programmer in understanding the programming aspects of the program. A description of the input, the printout, the deck setup, and tape configuration may be obtained from the LOVE user's guide

Civil Space Technology Initiative: a First Step

This is the first published overview of OAST's focused program, the Civil Space Technology Initiative, (CSTI) which started in FY88. This publication describes the goals, technical approach, current status, and plans for CSTI. Periodic updates are planned

Adaptively Lossy Image Compression for Onboard Processing

More efficient image-compression codecs are an emerging requirement for spacecraft because increasingly complex, onboard image sensors can rapidly saturate downlink bandwidth of communication transceivers. While these codecs reduce transmitted data volume, many are compute-intensive and require rapid processing to sustain sensor data rates. Emerging next-generation small satellite (SmallSat) computers provide compelling computational capability to enable more onboard processing and compression than previously considered. For this research, we apply two compression algorithms for deployment on modern flight hardware: (1) end-to-end, neural-network-based, image compression (CNN-JPEG); and (2) adaptive image compression through feature-point detection (FPD-JPEG). These algorithms rely on intelligent data-processing pipelines that adapt to sensor data to compress it more effectively, ensuring efficient use of limited downlink bandwidths. The first algorithm, CNN-JPEG, employs a hybrid approach adapted from literature combining convolutional neural networks (CNNs) and JPEG; however, we modify and tune the training scheme for satellite imagery to account for observed training instabilities. This hybrid CNN-JPEG approach shows 23.5% better average peak signal-to-noise ratio (PSNR) and 33.5% better average structural similarity index (SSIM) versus standard JPEG on a dataset collected on the Space Test Program – Houston 5 (STP-H5-CSP) mission onboard the International Space Station (ISS). For our second algorithm, we developed a novel adaptive image-compression pipeline based upon JPEG that leverages the Oriented FAST and Rotated BRIEF (ORB) feature-point detection algorithm to adaptively tune the compression ratio to allow for a tradeoff between PSNR/SSIM and combined file size over a batch of STP-H5-CSP images. We achieve a less than 1% drop in average PSNR and SSIM while reducing the combined file size by 29.6% compared to JPEG using a static quality factor (QF) of 90

GSFC Annual Scan Technology Review SpaceCube On-Board Processor Update

No abstract availabl

Vision Science and Technology at NASA: Results of a Workshop

A broad review is given of vision science and technology within NASA. The subject is defined and its applications in both NASA and the nation at large are noted. A survey of current NASA efforts is given, noting strengths and weaknesses of the NASA program

Lossy Multi/Hyperspectral Compression HW Implementation at high data rate

Image compression is becoming more and more important, as new multispectral and hyperspectral instruments are going to generate very high data rates due to the increased spatial and spectral resolutions. Transmitting all the acquired data to the ground segment is a serious bottleneck, and compression techniques are a feasible solution to this problem. The CCSDS has established a working group (WG) on multispectral and Hyperspectral Data Compression (MHDC), which has the purpose of standardizing compression techniques to be used onboard. The WG has already standardized a lossless compression algorithm for multispectral and hyperspectral images, and has started working on a lossy compression algorithm. The complexity of lossless compression algorithms is typically larger than that of lossy ones, leading to potentially lower throughputs. Therefore, a careful assessment is required in order to identify techniques that are able to sustain very high data rates. The increased complexity can also lead to increased resource occupancy on a hardware device such as an FPGA. Lossy compression introduces information losses in the images, and these losses must be accurately characterized, and their effect on the applications investigated. For these reasons, developing a lossy algorithm requires a more elaborate process. Under an ESA contract primed by Politecnico of Torino, TSD is currently designing an IP core for FPGA and/or ASIC implementation of a lossy compression algorithm that is being proposed for CCSDS standardization. In addition to the IP core, TSD is developing a HW platform based on the Xilinx Virtex-5 XQR5VFX130, the industry's first high performance rad-hard reconfigurable FPGA for processing-intensive for space systems. Advanced results along with details of electronic platform design will be presented in this paper

An Hardware Implementation of a Novel Algorithm For Onboard Compression of Multispectral and Hyperspectral Images

New multispectral and hyperspectral instruments are going to generate very high data rates due to the increased spatial and spectral resolution. In this context, the compression is a very important part of any onboard data processing system for Earth observation and astronomical missions. More recently, lossless compression has started to be routinely used for spaceborne Earth observation satellites. The CCSDS has established a working group (WG) on Multispectral and Hyperspectral Data Compression (MHDC), which has the purpose of standardizing compression techniques to be used onboard. The WG has already standardized a lossless compression algorithm for multispectral and hyperspectral images, and has started working on a lossy compression algorithm. Under an ESA contract, aimed to investigate new techniques for Lossy multi/hyperspectral compression for very high data rate instruments (HYDRA), TSD in collaboration with Politecnico of Torino, designed an IP core for FPGA and/or ASIC implementation of a lossy compression algorithm. In addition to the IP core, TSD developed a HW platform based on the Xilinx Virtex-5 XQR5VFX130, the industry's first high performance rad-hard reconfigurable FPGA for processing-intensive for space systems. Advanced results along with details of electronic platform design will be presented in this paper