Martin Bergeron

Near lossless data compression onboard a hyperspectral satellite

To deal with the large volume of data produced by hyperspectral sensors, the Canadian Space Agency (CSA) has developed and patented two near lossless data compression algorithms for use onboard a hyperspectral satellite: successive approximation multi-stage vector quantization (SAMVQ) and hierarchical self-organizing cluster vector quantization (HSOCVQ). This paper describes the two compression algorithms and demonstrates their near lossless feature. The compression error introduced by the two compression algorithms was compared with the intrinsic noise of the original data that is caused by the instrument noise and other noise sources such as calibration and atmospheric correction errors. The experimental results showed that the compression error was not larger than the intrinsic noise of the original data when a test data set was compressed at a compression ratio of 20:1. The overall noise in the reconstructed data that contains both the intrinsic noise and the compression error is even smaller than the intrinsic noise when the data is compressed using SAMVQ. A multi-disciplinary user acceptability study has been carried out in order to evaluate the impact of the two compression algorithms on hyperspectral data applications. This paper briefly summarizes the evaluation results of the user acceptability study. A prototype hardware compressor that implements the two compression algorithms has been built using field programmable gate arrays (FPGAs) and benchmarked. The compression ratio and fidelity achieved by the hardware compressor are similar to those obtained by software simulation

Recent Developments in the Hyperspectral Environment and Resource Observer (HERO) Mission

In 1997, the Canadian Space Agency (CSA) and Canadian industry began developing enabling technologies for hyperspectral satellites. Since then, the CSA has conducted mission and payload concept studies in preparation for launch of the first Canadian hyperspectral earth observation satellite. This Canadian hyperspectral remote sensing project is now named the Hyperspectral Environment and Resource Observer (HERO) Mission. In 2005, the Preliminary System Requirement Review (PSRR) and the Phase A (Preliminary Mission Definition) were concluded. Recent developments regarding the payload include an extensive comparison of potential optical designs. The payload uses separate grating spectrometers for the visible near-infrared and short-wave infrared portions of the spectrum. The instrument covers a swath of >30 km, has a ground sampling distance of 30 m, a spectral range of 400-2500 nm, and a spectral sampling interval of 10 nm. Smile and keystone are minimized. Recent developments regarding the mission include requirements simplification, data compression studies, and hyperspectral data simulation capability. In addition, a Prototype Data Processing Chain (PDPC) has been defined for 3 key hyperspectral applications. These are: geological mapping in the arctic environment, dominant species identification for forestry, and leaf area index for estimating foliage cover as well as forecasting crop growth and yield in agriculture.

A multidisciplinary user acceptability study of hyperspectral data compressed using an on‐board near lossless vector quantization algorithm

To deal with the extremely high data rate and huge data volume generated on‐board a hyperspectral satellite, the Canadian Space Agency (CSA) has developed two fast on‐board data compression techniques for hyperspectral imagery. The CSA is planning to place a data compressor on‐board a proposed Canadian hyperspectral satellite using these techniques to reduce the requirement for on‐board storage and provide a better match to available downlink capacity. Since the compression techniques are lossy, it is essential to assess the usability of the compressed data and the impact on remote sensing applications. In this paper, 11 hyperspectral data users covering a wide range of application areas and a variety of hyperspectral sensors assessed the usability of the compressed data using their well understood datasets and predefined evaluation criteria. Double blind testing was adopted to eliminate bias in the evaluation. Four users had ground truth available. They qualitatively and quantitatively compared the products derived from the compressed data to the ground truth at compression ratios from 10 : 1 to 50 : 1 to examine whether the compressed data provided the same amount of information as the original for their applications. They accepted all the compressed data. The users who did not have ground truths available evaluated the compression impact by comparing the products derived from the compressed data with those derived from the original data. They accepted most of the compressed data.

Evaluation and comparison of JPEG2000 and vector quantization based onboard data compression algorithm for hyperspectral imagery

This paper evaluates and compares JPEG 2000 and Successive Approximation Multi-stage Vector Quantization (SAMVQ) compression algorithms for hyperspectral imagery. PSNR was used to measure the statistical performance of the two compression algorithms. The SAMVQ outperforms JPEG 2000 by 17 dB of PSNR at the same compression ratios. The preservation of both spatial and spectral features was evaluated qualitatively and quantitatively. The SAMVQ outperforms JPEG 2000 in both spatial and spectral features preservation.

Impact of pre-processing and radiometric conversion on data compression onboard a hyperspectral satellite

This paper evaluates the impact of pre-processing and radiometric conversion to radiance on data compression onboard a hyperspectral satellite to examine whether or not they should be applied onboard before compression. Three hyperspectral datacubes acquired using a compact airborne spectrographic imager (cast) flown over crop field for retrieval of leaf area index (LAI) in agriculture applications and one datacube acquired using a Short Wave Infrared Full Spectrum Imager II (SFSI-II) for target detection were tested. Remote sensing applications are used as metrics to evaluate the impact. Double blind test approach was adopted in the evaluation. The evaluation results show that pre-processing and radiometric conversion applied before or after compression has no impact on retrieval of LAI products of the cast datacubes, but has impact on the target detection application of the SFSI-II datacube.

Hyperspectral Environment and Resource Observer (HERO) mission

The Canadian Space Agency (CSA) has conducted mission and payload concept studies in preparation for launch of the first Canadian hyperspectral Earth observation satellite. Named the Hyperspectral Environment and Resource Observer (HERO) mission, its objective is to provide information-rich optical imagery that enhances decision-making and stewardship of sensitive ecosystems and natural resources. The mission is designed to provide accurate forest inventory and health information, map the geology of the north, assess environmental impacts, and strategically extend the Canadian investment in Earth observations. The mission builds on the Canadian industry experience and expertise in satellite development and remote sensing and will make new capabilities available for a wide variety of users worldwide. In 2005, the preliminary system requirement review (PSRR) and phase A (preliminary mission definition) were concluded. The resulting mission characteristics are a swath width greater than 30 km, a ground sampling distance of 30 m, a spectral range from 400 to 2500 nm, and a spectral sampling interval of 10 nm. HERO is primarily a flexible tasking mission with a raw capacity of ~600 000 km2 daily ground area coverage. Large-area mapping is to be performed as a background mission. The proposed instrument design consists of dual spectrometers and telescope assemblies. The fore-optics is composed of a three-mirrors anastigmatic (TMA) telescope. The Offner-type spectrometers have separate visible near infrared (VNIR) and short-wave infrared (SWIR) detectors. Expected performance includes a signal-to-noise ratio (SNR) of 600:1 in the VNIR and 200:1 in the SWIR, F/2.2 spectrometers with minimized smile and keystone, and instrument modulation transfer function (MTF) of at least 0.3 at the Nyquist frequency for all wavelengths and fields.

PCW/PHEOS-WCA: Quasi-geostationary Arctic measurements for weather, climate and air quality from highly eccentric orbits

The PCW (Polar Communications and Weather) mission is a dual satellite mission with each satellite in a highly eccentric orbit with apogee ~42,000 km and a period (to be decided) in the 12–24 hour range to deliver continuous communications and meteorological data over the Arctic and environs. Such as satellite duo can give 24×7 coverage over the Arctic. The operational meteorological instrument is a 21-channel spectral imager similar to the Advanced Baseline Imager (ABI). The PHEOS-WCA (weather, climate and air quality) mission is intended as an atmospheric science complement to the operational PCW mission. The target PHEOS-WCA instrument package considered optimal to meet the full suite of science team objectives consists of FTS and UVS imaging sounders with viewing range of ~4.5° or a Field of Regard (FoR) ~ 3400×3400 km2 from near apogee. The goal for the spatial resolution at apogee of each imaging sounder is 10×10 km2 or better and the goal for the image repeat time is targeted at ~2 hours or better. The FTS has 4 bands that span the MIR and NIR with a spectral resolution of 0.25 cm−1. They should provide vertical tropospheric profiles of temperature and water vapour in addition to partial columns of many other gases of interest for air quality. The two NIR bands target columns of CO2, CH4 and aerosol optical depth (OD). The UVS is an imaging spectrometer that covers the spectral range of 280–650 nm with 0.9 nm resolution and targets the tropospheric column densities of O3 and NO2 and several other Air Quality (AQ) gases as well the Aerosol Index (AI).

Considerations of data handling system of a spaceborne imaging spectrometer with onboard data compression

This paper outlines the data handling system (DHS) of a spaceborne hyperspectral imager with an onboard data compressor. The data compression techniques to be used are successive approximation multistage vector quantization and hierarchical self-organizing cluster vector quantization. Considerations and implementation aspects of the DHS related to the onboard data compression are addressed. The impact of anomalies (spikes, saturation, etc.) in the raw data on compression performance is evaluated for the purpose of determining whether or not onboard data scrubbing is required before compression. The evaluation results show that anomalies in raw data have no significant effect on compression. This paper evaluates the impact of preprocessing and the conversion of raw data to radiance units on data compression using remote sensing applications to examine whether or not they should be applied on board before compression. The evaluation results show that preprocessing and radiometric conversion applied either before or after compression have no impact on an application using leaf area index but have impact on a target detection application using spectral unmixing. This paper also examines the combination of the two compression techniques to see if there is a performance improvement over a single technique. The experimental results show that the combined compression system does not perform better than either technique alone. Lastly, the resilience of the two compression techniques against bit errors caused by single event upsets is examined. The experimental results show that there is no loss of data fidelity when the error rate is ≤10–6.

Effect of anomalies on data compression onboard a hyperspectral satellite

The Canadian Space Agency (CSA) is developing a pre-operational spaceborne Hyperspectral Environment and Resource Observer (HERO). HERO will be a Canadian optical Earth observation mission that will address the stewardship of natural resources for sustainable development within Canada and globally. To deal with the challenge of extremely high data rate and the huge data volume generated onboard, CSA has developed two near lossless data compression techniques for use onboard a satellite. CSA is planning to place a data compressor onboard HERO using these techniques to reduce the requirement for onboard storage and to better match the available downlink capacity. Anomalies in the raw hyperspectral data can be caused by detector and instrument defects. This work focuses on anomalies that are caused by dead detector elements, frozen detector elements, overresponsive detector elements and saturation. This paper addresses the effect of these anomalies in raw hyperspectral imagery on data compression. The outcome of this work will help to decide whether or not an onboard data preprocessing to remove these anomalies is required before compression. Hyperspectral datacubes acquired using two hyperspectral sensors were tested. Statistical measures were used to evaluate the data compression performance with or without removing the anomalies. The effect of anomalies on compressed data was also evaluated using a remote sensing application.

Concept study of Canadian hyperspectral mission

The Canadian Space Agency (CSA) has initiated several studies on possible missions addressing the needs of Canadian government departments for operational land and ocean applications. Three parallel studies have been completed that reviewed the requirements with an objective to identify microsatellite mission concepts. The CSA has further awarded two contracts to the Canadian industry teams to investigate the feasibility of hyperspectral imagers that are compatible with a microsatellite platform. Two different concepts of low-mass imaging systems have been proposed and studied. In order to address to the main challenges identified, the CSA has extended the two feasibility studies. This paper briefly reports the updates of the feasibility studies in the extension period and new development in the ongoing concept study of a Canadian hyperspectral mission.