A systems analysis framework for assessing performance of long wave infra-red (LWIR) hyperspectral chemical imaging sensors (HCIS) is presented. The trade space study includes assessment of HCIS detection sensitivity and deployment impact on meeting specified mission requirements.
Current image quality approaches are designed to assess the utility of single band images by trained image analysts. While analysts today are certainly involved in the exploitation of spectral imagery, automated tools are generally used as aids in the analysis and offer hope in the future of significantly reducing the analysis timeline and analyst work load. Thus, there is a recognized need for spectral image quality metrics that include the effects of automated algorithms.
Previously, we have reported on candidate approaches for spectral quality metrics in the context of unresolved object detection. We have continued these efforts through the use of empirical trade studies in the context of ground cover terrain classification. HYDICE airborne hyperspectral imagery have been analyzed for the effects on scene classification accuracy of spatial resolution, signal-to-noise ratio, and number of spectral channels. Various classification algorithms including Gaussian maximum likelihood, spectral angle mapper, and Euclidean minimum distance have been considered. Performance metrics included classification accuracy, confusion matrices, and the Kappa coefficient. An extension of the previously developed Spectral Quality Equation (SQE) has been developed for the terrain classification application.
As expected, the accuracy of terrain classification shows only modest sensitivity to the parameters considered, except at the extreme cases of high noise, few bands, and small ground resolution. However, these results are useful in continuing to develop the quantitative relationships necessary for characterizing the quality of spectral imagery in various applications.
Current image quality approaches are designed to assess the utility of single band images by trained image analysts. While analysts today are certainly involved in the exploitation of spectral imagery, automated tools are generally used as aids in the analysis and offer hope in the future of significantly reducing the timeline and analysis load. Thus, there is a recognized need for spectral image quality metrics that include the effects of automated algorithms. We have begun initial efforts in this area through the use of a parametric modeling tool to gain insight into parameter dependence on system performance in unresolved object detection applications. An initial Spectral Quality Equation (SQE) has been modeled after the National Imagery Interpretation Rating Scale General Image Quality Equation (NIIRS GIQE). The parameter sensitivities revealed through the model-based trade studies were assessed through comparison to analogous studies conducted with available data. This current comparison has focused on detection applications using sensors operating in the VNIR and SWIR spectral regions. The SQE is shown with key image parameters and sample coefficients. Results derived from both model-based trade studies and empirical data analyses are compared. Extensions of the SQE approach to additional application areas such as material identification and terrain classification are also discussed.
Hyperspectral imaging (HSI) sensors provide imagery with hundreds of spectral bands, typically covering VNIR and/or SWIR wavelengths.
This high spectral resolution aids applications such as terrain classification and material identification, but it can also produce imagery that occupies well over 100 MB, which creates problems for
storage and transmission. This paper investigates the effects of lossy compression on a representative HSI cube, with background classification serving as an example application. The compression scheme first performs principal components analysis spectrally, then discards many of the lower-importance principal-component (PC) images, and then applies JPEG2000 spatial compression to each of the individual retained PC images. The assessment of compression effects considers both general-purpose distortion measures, such as root mean square difference, and statistical tests for deciding whether compression causes significant degradations in classification. Experimental results demonstrate the effectiveness of proper PC-image rate allocation, which enabled compression at ratios of 100-340 without producing significant classification differences. Results also indicate that distortion might serve as a predictor of compression-induced changes in application performance.
To better understand the capabilities of hyperspectral imaging spectrometers, a number of organizations planned and carried out a data collection exercise at a desert site in the southwestern United States. As part of this collection, eight soil 'panels' were constructed; four filled with a coarse gravel/sand mixture and four flled with fine soil. Each set of four panels was prepared to represent two moisture and density conditions: wet versus dry and compacted versus loose. Unlike laboratory soil specimens, which use 'purified' samples, these soil flats contained more variability. They therefore better represented the 'natural' environment that would be viewed by an airborne hyperspectral imaging sensor, while still allowing an experimental study under more controlled conditions. This paper examines how well the eight soil types and conditions can be distinguished based on their VNIR/SWIR reflectance spectra derived from field measurements and from airborne hyperspectral measurements made at nearly the same time. A brief review of the phenomenology of soil reflectance spectra will be given. Based on physical attributes of the soils, some new classification approaches have been developed and were applied to the soil panels. These phenomenological methods include examining contrast in certain broadband features and, based on these, calculating various broadband spectral ratios over subsets of the VNIR/SWIR spectral region. The separability of the reflectance spectra from the eight soil panels were also analyzed by applying the Spectral Angle Mapper (SAM) hyperspectral distance metric to quantify the separations between all pairs of soil types and conditions. Finally, a neural network approach was applied to determine distinguishing features of the spectra. The phenomenological approaches, SAM analyses, and the neural network results will be compared.
The EO-1 satellite is part of NASA's New Millennium Program (NMP). It consists of three imaging sensors: the multi-spectral Advanced Land Imager (ALI), Hyperion and Atmospheric Corrector. Hyperion provides a high-resolution hyperspectral imager capable of resolving 220 spectral bands (from 0.4 to 2.5 micron) with a 30 m resolution. The instrument images a 7.5 km by 100 km land area per image. Hyperion is currently the only space-borne HSI data source since the launch of EO-1 in late 2000. The discussion begins with the unique capability of hyperspectral sensing to coastal characterization: (1) most ocean feature algorithms are semi-empirical retrievals and HSI has all spectral bands to provide legacy with previous sensors and to explore new information, (2) coastal features are more complex than those of deep ocean that coupled effects are best resolved with HSI, and (3) with contiguous spectral coverage, atmospheric compensation can be done with more accuracy and confidence, especially since atmospheric aerosol effects are the most pronounced in the visible region where coastal feature lie. EO-1 data from Chesapeake Bay from 19 February 2002 are analyzed. In this presentation, it is first illustrated that hyperspectral data inherently provide more information for feature extraction than multispectral data despite Hyperion has lower SNR than ALI. Chlorophyll retrievals are also shown. The results compare favorably with data from other sources. The analysis illustrates the potential value of Hyperion (and HSI in general) data to coastal characterization. Future measurement requirements (air borne and space borne) are also discussed.
To demonstrate the utility of EO-1 data, combined analysis of panchromatic, multispectral (ALI, Advanced Land Imager) and hyperspectral (Hyperion) data was conducted. In particular, the value added by HSI with additional spectral information will be illustrated. Data sets from Coleambally Irrigation Area, Australia on 7 March 2000 and San Francisco Bay area on 17 January 2000 are employed for the analysis. Analysis examples are shown for surface characterization, anomaly detection, spectral unmixing and image sharpening.
Hyperspectral imaging (HSI) sensors collect spatially resolved data in hundreds of spectral channels. While the technology matures and finds broad applications, data downlink from the collection platform and near real-time processing remain key challenges, especially for near-term spaceborne sensors. It is desirable to process the data on-board for near real-time analysis and downlink compressed data allowing near full spectral recovery for post-mission analysis. Principal component analysis (PCA) can be used to determine the reduced dimensionality and separate noise components in the data. While PCA is useful for image feature analysis such as smoke/cloud discrimination (Griffin, et al., 2000), it can also be used as a data compression tool. With PCA, the majority of information in an HSI data cube is effectively compressed to a small number of principal components. The data volume is significantly reduced while the feature contrast is enhanced. Spectral information can be recovered from the compressed data with minimal loss. In this paper, the reconstructed data are compared to the original "truth" data with difference analysis using sample AVIRIS imagery. This methodology also allows for the HSI data to be used adaptively for various multispectral band simulations without the constraint of data volume and processing burden. Based on AVIRIS data, emulation of MODIS sensor bands are carried out and compared with the PCA-reconstructed data. Two products are also derived and compared: Normalized Difference Vegetation Index (NDVI) and the integrated column water vapor (CWV) using the full set of AVIRIS data and the reconstructed spectral information.
Two approaches, one for discriminating features in a set of AVIRIS scenes dominated by areas of smoke, plumes, clouds and burning grassland as well as scarred (burned) areas and another for identifying those features are presented here. A semiautomated feature extraction approach using principal components analysis was used to separate the scenes into feature classes. Typically, only 3 component images were used to classify the image. A physics-based approach which utilized the spectral diversity of the features in the image was used to identify the nature of the classes produced in the component analysis. The results from this study show how the two approaches can be used in unison to fully characterize a smoke or cloud-filled scene.