The Canadian Space Agency (CSA) in collaboration with the Naval Research Laboratory (NRL) and NASA are considering a coastal and inland water color hyperspectral imager as a complement to the NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. This hyperspectral imager with 100 m spatial sampling would be specifically designed to sample the coastal oceans, estuaries and lakes, and will help improve the monitoring and better understand quality and productivity of coastal and inland waters and their impact on the coastal communities' well-being and economic activities. This paper describes the user community needs for spaceborne fine-spatial-resolution coastal and inland water hyperspectral imaging, the specifications of the hyperspectral payload that addresses the users' needs, payload concept design and related technical challenges.

The goal of the Canadian Hyperspectral Mission (CHM) is to provide a new and powerful space-based tool for delivering frequently updated hyperspectral remote sensing information products for a broad range of operational users in governments and industries worldwide. The resulting CHM concept includes a relatively low cost constellation of three microsatellites and a supporting ground segment which, together, would be capable of imaging the entire land mass and coastal areas of Canada, USA and Europe every 3 days.

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.

In order to meet the needs of Canadian government departments for operational land and ocean applications the Canadian Space Agency in collaboration with its industry has carried out activities and initiated early phase studies to investigate the feasibility of hyperspectral imager that is compatible with a microsatellite platform. Feasibility studies on two different concepts of microsatellite hyperspectral imagers are ongoing. A user evaluation study that assesses the key parameters of the microsatellite has completed. This paper briefly summarizes the development and early results of the feasibility studies and user assessment.

In this paper, we develop a novel technology that can enhance the spatial resolution of hyperspectral data cube without using any additional images, as would be the case in image fusion. The technology exploits interband spatial misregistration or distortion (often referred to as “keystone”) of the sensor that acquired the data cube and uses it as additional information to increase the spatial resolution of the data cube. Three methods have been developed to derive subpixel-shifted images from the data cube itself in exploiting the sensor's intrinsic characteristics. Two schemes were proposed to organize the derived subpixel-shifted images before being integrated into the high-resolution image using the iterative-back-projection fusion. Experimental results show that the technology can enhance the spatial resolution in the cross-track direction of hyperspectral data cubes by a factor of two.

In this paper, a new denoising method is proposed for hyperspectral data cubes that already have a reasonably good signal-to-noise ratio (SNR) (such as 600 : 1). Given this level of the SNR, the noise level of the data cubes is relatively low. The conventional image denoising methods are likely to remove the fine features of the data cubes during the denoising process. We propose to decorrelate the image information of hyperspectral data cubes from the noise by using principal component analysis (PCA) and removing the noise in the low-energy PCA output channels. The first PCA output channels contain a majority of the total energy of a data cube, and the rest PCA output channels contain a small amount of energy. It is believed that the low-energy channels also contain a large amount of noise. Removing noise in the low-energy PCA output channels will not harm the fine features of the data cubes. A 2-D bivariate wavelet thresholding method is used to remove the noise for low-energy PCA channels, and a 1-D dual-tree complex wavelet transform denoising method is used to remove the noise of the spectrum of each pixel of the data cube. Experimental results demonstrated that the proposed denoising method produces better denoising results than other denoising methods published in the literature.

The objective of this paper is to develop novel classification structures for military targets detection and recognition by employing different fusion techniques. In real applications, the great diversity of materials in the background areas and the similarity between the background and target signatures result in high false alarm rates and large miss classification errors. In this paper, two novel target detection and recognition systems are proposed using different fusion techniques: decision fusion and classification fusion employing confidence vectors. These new systems are tested using an experimental data set to assess their effectiveness.

In this paper, we propose a new nonlinear dimensionality reduction method by combining Locally Linear Embedding (LLE) with Laplacian Eigenmaps, and apply it to hyperspectral data. LLE projects high dimensional data into a low-dimensional Euclidean space while preserving local topological structures. However, it may not keep the relative distance between data points in the dimension-reduced space as in the original data space. Laplacian Eigenmaps, on the other hand, can preserve the locality characteristics in terms of distances between data points. By combining these two methods, a better locality preserving method is created for nonlinear dimensionality reduction. Experiments conducted in this paper confirms the feasibility of the new method for hyperspectral dimensionality reduction. The new method can find the same number of endmembers as PCA and LLE, but it is more accurate than them in terms of endmember location. Moreover, the new method is better than Laplacian Eigenmap alone because it identifies more pure mineral endmembers.

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.

In this paper, a new noise reduction algorithm is introduced and applied to the problem of denoising hyperspectral imagery. This algorithm resorts to the spectral derivative domain, where the noise level is elevated, and benefits from the dissimilarity of the signal regularity in the spatial and the spectral dimensions of hyperspectral images. The performance of the new algorithm is tested on two different hyperspectral datacubes: an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) datacube that is acquired in a vegetation-dominated site and a simulated AVIRIS datacube that simulates a geological site. The new algorithm provides signal-to-noise-ratio improvement up to 84.44% and 98.35% in the first and the second datacubes, respectively.

A fast search method for vector quantization is proposed in this paper. It makes use of the fact that in the generalized Lloyd algorithm (GLA) a vector in a training sequence is either placed in the same minimum distance partition (MDP) as in the previous iteration or in a partition within a very small subset of partitions. The proposed method searches for the MDP for a training vector only in this subset of partitions plus the single previous MDP. As the size of this subset is much smaller than the total number of codevectors, the search process is speeded up significantly. The creation of the subset is essential, as it has a direct effect on the improvement in computation time of the proposed method. The schemes that create the subset efficiently have been proposed. The proposed method generates a codebook identical to that generated using the GLA. It is simple and requires only minor modification of the GLA and a modest amount of additional memory. The experimental results show that the computation time of codebook training was improved by factors from 6.6 to 50.7 and from 5.8 to 70.4 for two test data sets when codebooks of sizes from N = 16 to 2048 were trained. The proposed method was also combined with an earlier published method to further improve the computation time.

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.

A fast vector quantization algorithm for data compression of hyperspectral imagery is proposed in this paper. It makes use of the fact that in the full search of the generalized Lloyd algorithm (GLA) a training vector does not require a search to find the minimum distance partition if its distance to the partition is improved in the current iteration compared to that of the previous iteration. The proposed method has the advantage of being simple, producing a large computation time saving and yielding compression fidelity as good as the GLA. Four hyperspectral data cubes covering a wide variety of scene types were tested. The loss of spectral information due to compression was evaluated using the spectral angle mapper and a remote sensing application.

Various compression schemes have been suggested for storage and distribution of hyperspectral remotely sensed data. Hyperspectral forestry applications that rely on the measurement of subtle variations in the spectral signature of the forest canopy can be affected by modification of the spectra induced by compression. As part of an experiment for the Canadian Space Agency (CSA), Hyperion data cubes acquired over the Greater Victoria Watershed District (GVWD) were compressed using the Successive Approximation Multi-stage Vector Quantization (SAMVQ) and Hierarchical Self-Organizing Cluster Vector Quantization (HSOCVQ) algorithms developed by CSA. The data were compressed using compression ratios 10:1 and 20:1 and were returned uncompressed. The data cubes were classified into forest species using the same supervised classification methodology as applied to the original data. The classification accuracies were compared. For some applications, one can achieve significant reductions in data volume through compression. Of the compression algorithms and ratios tested, SAMVQ 10:1 has the least overall effect but still reduces classification accuracies on difficult to separate classes. While uncompressed data are preferred, SAMVQ 10:1 compression may be suitable for forest inventory.

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.

In this study, impacts of vector quantization compression on prediction of leaf chlorophyll content of crops for the application to precision agriculture were evaluated. The compression algorithm tested in this paper is called successive approximation multi-stage vector quantization (SAMVQ). The hyperspectral data used were acquired by CASI over corn fields at L' Acadie experimental farm (Agriculture and Agri-Food Canada) during the summer of 2000. Nine zones in the corn fields with different fertilization levels (no fertilization, intermediate fertilization, and over-fertilization) were used to evaluate the difference between the leaf chlorophyll contents obtained from original and reconstructed reflectance data cubes. The root mean square errors (RMSEs) and the correlations between the chlorophyll content derived from the original data cube and that derived from the reconstructed data cubes were calculated in the nine zones. The spatial variability of chlorophyll content in the nine zones was also examined for the images of chlorophyll content created from the original and reconstructed reflectance data cubes. The results show that the chlorophyll content image created from the reconstructed reflectance data cube corresponding to SAMVQ with a compression ratio of 20 maintains good agreement with that derived from the original reflectance data cube in the nine zones in terms of estimated pigment mean and spatial variability. As a result, SAMVQ with a compression ratio of 20 is considered acceptable for the retrieval of crop chlorophyll content from CASI hyperspectral data for agriculture corn crops.

In this paper, the results of different image fusion schemes for pixel level fusion and image super-resolution reconstruction algorithm are presented. A new method that performs enhancements in the spatial resolution is employed. It has the following unique features: (a) higher ability of improving image spatial resolution (b) increased reduction of pixel uncertainties due to calibration errors through a new adaptive filtering technique, and (c) improved reduction of the blurring through a new blur estimation and super-resolution restoration that employs cross-validation for regularization. Tradeoff studies with other state of the art spatial resolution enhancement algorithms are presented.

The Canadian Space Agency is investigating an onboard compressor for a hyperspectral satellite using its two innovative data compression techniques. It is essential to verify the quality of the compressed data and users' acceptability in terms of their remote sensing applications. Hyperspectral data cubes acquired by hyperspectral sensors such as casi, AVIRIS, Probe-1 and Hyperion were tested. Statistical hypothesis tests were used to assess if the means and variances in each spectral band of specified zones calculated from the reconstructed data cubes are significantly different from those calculated from the original data cube. Remote sensing end products, such as red edge, chlorophyll content and spectral unmixing were used to evaluate the compressed data. Preliminary test results show that hyperspectral data compressed using the two compression techniques at compression ratio up to 30:1 are acceptable in terms of statistical tests and remote sensing end products.

This paper evaluates lossy vector quantization-based hyperspectral data compression algorithms, using red-edge indices as end-products. Three compact airborne spectrographic imager (CASI) data sets and one airborne visible/infrared imaging spectrometer (AVIRIS) data set from vegetated areas were tested. A basic compression system for hyperspectral data called the "reference" system, and three-speed improved compression systems called systems 1, 2, and 3, respectively, were examined. Five red-edge products representing the near infrared (NIR) reflectance shoulder (Vog 1), the NIR reflectance maximum (Red rs), the difference between the reflectance maximum and the minimum (Red rd), the wavelength of the reflectance maximum (Red lo), and the wavelength of the point of inflection of the NIR vegetation reflectance curve (Red lp) were retrieved from each original data set and from their decompressed data sets. The experiments show that the reference system induces the smallest product errors of the four compression systems. System 1 and 2 perform fairly closely to the reference system. They are the recommended compression systems since they compress a data set hundreds of times faster than the reference system. System 3 performs similarly to the reference system at high compression ratios. Product errors increase with the increase of compression ratio. The overall product errors are dominated by Vog 1, Red rs, and Red rd, since the amplitude of product error for these products is over one order of magnitude greater than those for the Red-lo and Red lp products. The difference between the overall error from the reference and that from system 1 or 2 is below 0.5% at all compression ratios. The overall product error induced by system 1 or 2 is below 3.0% and 2.0% for CASI and AVIRIS data sets, respectively, when the compression ratio is 100 and below. Spatial patterns of the product errors were examined for tha AVRIS data set. For all products, the errors are uniformly distributed in vegetated areas. Errors are relatively high in nonvegeted and mixed-pixel areas.

This paper describes a spectral index (SI)-based multiple subcodebook algorithm (MSCA) for lossy hyperspectral data compression. The scene of a hyperspectral dataset to be compressed is delimited into n regions by segmenting its SI image. The spectra in each region have similar spectral characteristics. The dataset is then separated into n subsets, corresponding to the n regions. While keeping the total number of codevectors the same (i.e. the same compression ratio), not just a single codebook, but n smaller and more efficient subcodebooks are generated. Each subcodebook is used to compress the spectra in the corresponding region. With the MSCA, both the codebook generation time (CGT) and coding time (CT) can be improved by a factor of around n at almost no loss of fidelity. Four segmentation methods for delimiting the scene of the data cube were studied. Three hyperspectral vector quantization data compression systems that use the improved techniques were simulated and tested. The simulation results show that the CGT could be reduced by more than three orders of magnitude, while the quality of the codebooks remained good. The overall processing speed of the compression systems could be improved by a factor of around 1000 at an average fidelity penalty of 1.0 dB.