Timothy S. Wilkinson

PICASSO: an end-to-end image simulation tool for space and airborne imaging systems

The design of any modern imaging system is the end result of many trade studies, each seeking to optimize image quality within real world constraints such as cost, schedule and overall risk. Image chain analysis - the prediction of image quality from fundamental design parameters - is an important part of this design process. At The Aerospace Corporation we have been using a variety of image chain analysis tools for many years, the Parameterized Image Chain Analysis & Simulation SOftware (PICASSO) among them. In this paper we describe our PICASSO tool, showing how, starting with a high quality input image and hypothetical design descriptions representative of the current state of the art in commercial imaging satellites, PICASSO can generate standard metrics of image quality in support of the decision processes of designers and program managers alike.

JPEG-2000 compression using 3D wavelets and KLT with application to HYDICE data

JPEG-2000 is the new image compression standard currently under development by ISO/IEC. Part I of this standard provides a “baseline” compression technology appropriate for grayscale and color imagery. Part II of the standard will provide extensions that allow for more advanced coding options, including the compression of multiple component imagery. Several different multiple component compression techniques are currently being investigated for inclusion in the JPEG-2000 standard. In this paper we apply some of these techniques toward the compression of HYDICE data. Two decorrelation techniques, 3D wavelet and Karhunen-Loeve Transform (KLT), were used along with two quantization techniques, scalar and trellis-coded (TCQ), to encode two HYDICE scenes at five different bit rates (4.0, 2.0, 1.0, 0.5, 0.25 bits/pixel/band). The chosen decorrelation and quantization techniques span the range from the simplest to the most complex multiple component compression systems being considered for inclusion in JPEG-2000. This paper reports root-mean-square-error (RMSE) and peak signal-to-noise ratio (PSNR) metrics for the compressed data. A companion paper [1] that follows reports on the effects of these compression techniques on exploitation of the HYDICE scenes.

Mission history of reflective solar band calibration performance of VIIRS

Environmental Data Records (EDR) from the Visible Infrared Imaging Radiometer Suite (VIIRS) have a need for Reflective Solar Band (RSB) calibration errors of less than 0.1%. Throughout the mission history of VIIRS, the overall instrument calibrated response scale factor (F factor) has been calculated with a manual process that uses data at least one week old and up to two weeks old until a new calibration Look Up Table (LUT) is put into operation. This one to two week lag routinely adds more than 0.1% calibration error. In this paper, we discuss trending the solar diffuser degradation (H factor), a key component of the F factor, improving H factor accuracy with improved bidirectional reflectance distribution function (BRDF) and attenuation screen LUTs , trending F factor, and how using RSB Automated Calibration (RSBAutoCal) will eliminate the lag and look-ahead extrapolation error.

Application of video-based coding to hyperspectral imagery

As sensor technology evolves into the 21st Century, the volume of data available from both airborne and spaceborne sources will increase rapidly. High resolution hyperspectral remote sensing systems may offer hundreds of bands of data. Efficient use, transmission, storage, and manipulation of such data will require some type of bandwidth compression. Current image compression standards are not specifically optimized to accommodate hyperspectral data. Nonetheless, users who require such compression will be driven toward current standards for reasons of simplicity, cost, and interoperability. This paper begins an examination of the performance of the MPEG-like video compression methodologies for hyperspectral image data. Compression fidelity is examined with an objective metric as a function of the achieved compression ratio. Comparisons are made between different coding configurations which resemble those available in the basic MPEG scheme. Observations regarding the use of standards-based compression of hyperspectral data are offered along with suggestions for future investigations.

Impacts of VIIRS polarization sensitivity on non-ocean scenes

The Visible and Infrared Imaging Radiometer Suite (VIIRS) collects Earth science data continually in a sun-synchronous orbit. VIIRS raw data records (RDRs) are processed by ground software to generate a variety of environmental data records (EDRs). Over open ocean, ground software produces measurements of chlorophyll concentration based on subsurface reflectance estimates. Considering that about 90% of the top of the atmosphere (TOA) radiance reaching a sensor over open ocean can be attributed to atmosphere or surface reflectance, it is possible to introduce large chlorophyll estimate errors by ignoring ordinarily small contributions due to polarization sensitivity. For chlorophyll determination, instrument polarization sensitivity measurements are used in combination with atmospheric models to compensate for polarization phenomenon. VIIRS ground software does not compensate for polarization phenomenon when processing land scenes. It is therefore natural to consider the impacts of ignoring VIIRS polarization phenomenon for land surface reflectance estimates. In this work, pre-flight polarization sensitivity characterization data is used in conjunction with a polarized atmospheric propagation model to analyze potential impacts on retrieved TOA reflectance. Impacts are analyzed across several collection conditions, including ground surface type, atmospheric visibility, general atmospheric profile and collection geometry. Actual pre-flight characterization data is used for both NPP and J1 VIIRS.

New hyperspectral compression options in JPEG-2000 and their effects on exploitation

This paper describes a continuing study effort investigating the impact of hyperspectral compression on the utility of compressed and subsequently reconstructed data. The current study involved the application of new compression options in JPEG-2000 to hyperspectral data and the investigation of their effects on exploitation. Part II of the JPEG-2000 standard (ISO/IEC 15444-2) provides extensions to the baseline JPEG-2000 compression algorithms (ISO/IEC 15444-2) that allow for the compression of hyperspectral data. In this study, Karhunen-Loeve Transform (KLT) was used for spectral decorrelation along with wavelet compression and scalar quantization to encode two HYDICE scenes at five different average bit rates (4.0, 2.0, 1.0, 05., 02.5 bits/pixel/band). Part II of the JPEG- 2000 standard also introduces the notion of component collections, which may be used to spectrally segment (and spectrally permute) hyperspectral data. Component collections were used in conjunction with KLT to reduce computation complexity and improve numeric stability. Two exploitation tasks, anomaly detection and material identification, were performed on these compressed and reconstructed data. We report the conventional root-mean- square-error (RMSE) and peak signal-to-noise ration (PSNR) metrics. We also report the exploitation results to facilitate the determination of acceptable bit rate for each exploitation task and the comparison amongst different compression algorithms. Comparisons are also made with previously reported results using an earlier version of JPEG-2000 to compress the HYDICE data.

Multicomponent compression in JPEG 2000 Part II

JPEG2000 Part I provides a host of compression options and data ordering choices which enable powerful applications and create tremendous flexibility in the handling of still images. Part I, however, is restricted to handle multiple component images (with the exception of three-component images) a single component at a time. In general, Part I allows no exploitation of inter-component correlation that may exist. Part II introduces a robust multiple component transform capability which is applied prior to the Part I spatial wavelet decomposition and compression. This paper describes some of the multiple component transform capabilities in JPEG2000 Part II, including prediction, traditional decorrelation, wavelet transformations, and reversible integer transformations.

S-NPP VIIRS day-night band calibration and status update

The Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) onboard the Suomi-National Polar Orbiting Partnership (S-NPP) satellite measures broadband visible radiances providing imagery of clouds and other Earth features over illumination levels spanning 7 orders of magnitude, ranging from quarter moon to full sunlight (3e-5 to 200 W/m2/sr). Due to its original purpose to provide imagery rather than radiometric products as well as this extensive radiance range, the DNB calibration requirements are less restrictive as compared to the other 21 VIIRS bands. Over time the importance of the DNB data product imagery has engendered efforts to improve the calibration stability of the DNB over its mission. This paper discusses The Aerospace Corporations support of the DNB calibration over the S-NPP mission.

Gray-scale image compression with wavelet transforms

The wavelet transform has recently become popular as a tool for multiresolution image decomposition. Simultaneous localization of spatial and spectral information makes the wavelet transform an excellent candidate for the decorrelation stage of an image compression algorithm. This paper describes a simple but effective method of grayscale image compression that uses the wavelet transform. Nonuniform quantizers are applied to the detail subimages of the transform. Detail subimages are then grouped by decomposition level and entropy coded. The lowest resolution image, which results from the all low-pass path through the decomposition, is uniformly quantized and coded directly. Reductions in output bit rate to those of interest to the NITF low bitrate evaluation are achieved through simple scaling of the quantizers and inclusion of run-length coding.

Multispectral image compression for future LANDSAT remote sensing systems

Multispectral image compression has been identified as a critical technology area for future advanced land remote sensing systems. The nature of multispectral imagery is such that, with correlated spectral bands, additional redundancy exists between registered pixels which can be exploited for compression gains. Because of the stringent requirements placed upon multispectral compression by various exploitation activities, designing a multispectral compression algorithm is not trivial. This paper will concentrate, not on compression algorithms, but upon some of the system design requirements, image properties and other issues that face compression algorithm designers.<<ETX>>