Matthew Klimesh

Exploiting Calibration-Induced Artifacts in Lossless Compression of Hyperspectral Imagery

Algorithms for compression of hyperspectral data are commonly evaluated on a readily available collection of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images. These images are the end product of processing raw data from the instrument, and their sample value distributions contain artificial regularities that are introduced by the conversion of raw data values to radiance units. It is shown that some of the best reported lossless compression results for these images are achieved by algorithms that significantly exploit these artifacts. This fact has not been widely reported and may not be widely recognized. Compression performance comparisons involving such algorithms and these standard AVIRIS images can be misleading if they are extrapolated to images that lack such artifacts, such as unprocessed hyperspectral images. In fact, two of these algorithms are shown to achieve rather unremarkable compression performance on a set of more recent AVIRIS images that do not contain appreciable calibration-induced artifacts. This newer set of AVIRIS images, which contains both calibrated and raw images, is made available for compression experiments. To underscore the potential impact of exploiting calibration-induced artifacts in the standard AVIRIS data sets, a compression algorithm is presented that achieves noticeably smaller compressed sizes for these data sets than is reported for any other algorithm.

Mathematical structure of entanglement catalysis

The majorization relation has been shown to be useful in classifying which transformations of jointly held quantum states are possible using local operations and classical communication. In some cases, a direct transformation between two states is not possible, but it becomes possible in the presence of another state (known as a catalyst); this situation is described mathematically by the trumping relation, an extension of majorization. The structure of the trumping relation is not nearly as well understood as that of majorization. We give an introduction to this subject and derive some new results. Most notably, we show that the dimension of the required catalyst is in general unbounded; there is no integer

GPU lossless hyperspectral data compression system for space applications

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Fast and adaptive lossless on-board hyperspectral data compression system for space applications

such that it suffices to consider catalysts of dimension

Hardware Implementation of Lossless Adaptive and Scalable Hyperspectral Data Compression for Space

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Spectral Ringing Artifacts in Hyperspectral Image Data Compression

or less in determining which states can be catalyzed into a given state. We also show that almost all bipartite entangled states are potentially useful as catalysts.

Applying Radiation Hardening by Software to Fast Lossless compression prediction on FPGAs

On-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. At JPL, a novel, adaptive and predictive technique for lossless compression of hyperspectral data, named the Fast Lossless (FL) algorithm, was recently developed. This technique uses an adaptive filtering method and achieves state-of-the-art performance in both compression effectiveness and low complexity. Because of its outstanding performance and suitability for real-time onboard hardware implementation, the FL compressor is being formalized as the emerging CCSDS Standard for Lossless Multispectral & Hyperspectral image compression. The FL compressor is well-suited for parallel hardware implementation. A GPU hardware implementation was developed for FL targeting the current state-of-the-art GPUs from NVIDIA®. The GPU implementation on a NVIDIA® GeForce® GTX 580 achieves a throughput performance of 583.08 Mbits/sec (44.85 MSamples/sec) and an acceleration of at least 6 times a software implementation running on a 3.47 GHz single core Intel® Xeon™ processor. This paper describes the design and implementation of the FL algorithm on the GPU. The massively parallel implementation will provide in the future a fast and practical real-time solution for airborne and space applications.

Error containment in compressed data using sync markers

Efficient on-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. The technique also improves signature extraction, object recognition and feature classification capabilities by providing exact reconstructed data on constrained downlink resources. At JPL a novel, adaptive and predictive technique for lossless compression of hyperspectral data was recently developed. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that far exceeds state-of-the-art techniques currently in use. The JPL-developed ‘Fast Lossless’ algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. It is of low computational complexity and thus well-suited for implementation in hardware, which makes it practical for flight implementations of pushbroom instruments. A prototype of the compressor (and decompressor) of the algorithm is available in software, but this implementation may not meet speed and real-time requirements of some space applications. Hardware acceleration provides performance improvements of 10x–100x vs. the software implementation (about 1M samples/sec on a Pentium IV machine). This paper describes a hardware implementation of the ‘Fast Lossless’ compression algorithm on a Field Programmable Gate Array (FPGA). The FPGA implementation targets the current state-of-the-art FPGAs (Xilinx Virtex IV and V families) and compresses one sample every clock cycle to provide a fast and practical real-time solution for Space applications.

Telecommunications IT and navigation for future Mars exploration missions

Efficient on-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. The technique also improves signature extraction, object recognition and feature classification capabilities by providing exact reconstructed data on constrained downlink resources. At JPL a novel, adaptive and predictive technique for lossless compression of hyperspectral data was recently developed. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that far exceeds state-of-the-art techniques currently in use. The JPL-developed ‘Fast Lossless’ algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. It is of low computational complexity and thus well-suited for implementation in hardware. It was modified for pushbroom instruments and makes it practical for flight implementations. A prototype of the compressor (and decompressor) of the algorithm is available in software, but this implementation may not meet speed and real-time requirements of some space applications. Hardware acceleration provides performance improvements of 10x-100x vs. the software implementation (about 1M samples/sec on a Pentium IV machine). This paper describes a hardware implementation of the ‘Modified Fast Lossless’ compression algorithm for pushbroom instruments on a Field Programmable Gate Array (FPGA). The FPGA implementation targets the current state-of-the-art FPGAs (Xilinx Virtex IV and V families) and compresses one sample every clock cycle to provide a fast and practical real-time solution for Space applications.

Capacity of the generalized PPM channel

When using 3-D wavelet transforms for hyperspectral image compression, systematic variations in signal level of different spectral bands can cause widely-varying mean values in spatial planes of spatially low-pass subbands. Failing to account for this phenomenon can have detrimental effects on image compression, including reduced effectiveness in compressing spatially low-pass subband data, and biases in some reconstructed spectral bands.