Visualization of High-dimensional Remote- Sensing Data Products

Transcription

1 Visualization of High-dimensional Remote- Sensing Data Products An Innovative Graduate Student Research Proposal Principle Innovator Hongqin Zhang, Graduate Student Chester F. Carlson Center for Image Science Rochester Institute of Technology Dr. Ethan D. Montag, Advisor Co-Innovator Dr. David W. Messinger, Scientist Chester F. Carlson Center for Image Science Rochester Institute of Technology Abstract The purpose of this proposal is to obtain funding for a collaborative project between members of the Munsell Color Science Laboratory (MCSL) and Digital Imaging and Remote Sensing Laboratory (DIRS) to investigate the application of color in the visualization of highdimensional remote-sensing products for interpretation and exploitation. Using a spectral unmixing algorithm for land classification of hyperspectral imagery, a data cube can be derived containing two dimensions of spatial information, and a third dimension of land-class abundance. Visualization of these data is a particularly challenging and unsolved problem. Color, as an important natural tool to convey information, can be used to facilitate the task of data analysis. Based on data products derived from analysis of a spacecraft-borne sensor (the Hyperion sensor on the EO-1 satellite) we will develop visualization techniques based on principles of human vision, color appearance, and color perception. The effectiveness of the techniques will be evaluated through psychophysical experiments including the assessment of accuracy in judging land-class abundances and evaluation of preference and judgments of usefulness of the various visualization techniques by a range of observers spanning from novices to expert data analysts. Dollar Request: $6,000 Desired Funding Dates: June 1, 2005 May 31, 2006

2 PROPOSAL Scientific Justification Remotely-sensed hyperspectral imagery (HSI) is, by its very nature, a high-dimensional source of data. Images are collected as cubes, with two spatial dimensions (typically several hundreds of pixels wide and thousands of pixels long) and one spectral dimension (typically collected in scores of narrow, contiguous bands). The obvious advantage is the ability to perform analyses using techniques based on spectroscopy. Traditionally, large-scale land classification is a common use for such data, in which each pixel is assigned to a single class (e.g., water, urban, agricultural, bare soil, etc.). Unfortunately, remotely sensed HSI typically have large pixel sizes (10-30 m for spacebased sensors), a result of the trade-off between spatial resolution and spectral resolution. Given these spatial scales, it can be incorrect to assume a given pixel is composed of a single land class. Using the spectral data for each pixel, and given a spectral representation of the various components, Linear Mixing Models can be developed and applied to un-mix the spectral signature of the pixel on the ground. Using this technique for land classification results in multiple images : the derived products that contain abundances of each land class in each pixel. Under some constraints, the abundances must sum to one, under others they are only constrained to be non-negative. In either case, a cube of data is derived containing two dimensions of spatial information, and multiple planes of land-class abundances. Visualization of these data is a particularly challenging and unsolved problem due to the multidimensional nature of the data. Imagery from the Hyperion 1 sensor on the Earth Observing 1 (EO-1) spacecraft will be used as the initial data set for this project. This sensor can image a 7.5 km by 100 km land area per image in 220 spectral bands ranging from 0.4 to 2.5 µm with a 30- meter resolution per pixel. An urban 2 image of an area near San Francisco will be analyzed using techniques developed by co-pi David Messinger to create 15 abundance maps. The raw radiance data have been converted to reflectance through an atmospheric compensation scheme to aid the un-mixing process. Additional imagery is available from the Hyperion site as well as other imagery provided by DIRS. The goal of this project is to develop techniques to visualize these data for more efficient interpretation and improved data mining. This goal is especially vital considering the increased use of this data for environmental, surveillance, and homeland security applications. The increase in our ability to collect data via airborne and spacecraft based sensors has outpaced the ability of analysts to interpret the data. Color, as an important natural tool to convey information, can be used to facilitate the task of data analysis. Much research and experimental work 3,4,5,6,7 has been carried out to identify the principles for effective use of color and to build computational models for constructing color schemes to represent data values. However, few of these schemes have been guided by visual appearance models. Therefore, in this project, one approach is to develop visualization schemes based on color appearance and the multidimensionality of human color perception. It is expected that effective rendering of the data can be achieved via these perceptually based techniques. One simple example of this idea is that even a single abundance map can be

3 rendered using a perceptually uniform lightness scale based on the characterization of the display thereby creating a linear relationship between abundance and perceived lightness. Typically, the land-class abundance is displayed as a gray scale map for each land-class. In this case, it is hard to relate the abundance of each class for each pixel among the maps, especially when one has a large number of land-classes. A number of techniques will be applied to allow the visualization of multiple dimensions of these data. One scheme involves using a nominal color defined by a prototypical pixel of the reflectance class in the visible spectrum to encode those areas that match that class. Color mixture, based on color appearance, can then be used for areas that represent mixtures of classes. A second scheme involves the development and implementation of bivariate and trivariate color maps that represent abundances in two and three dimensions extending previous work on the use of pseudocolor for multidimensional graphical information display. 3 Color categorization 8 can also be used to represent more than three dimensions at a time. We will also explore the use of the above techniques in conjunction with the use of interactivity by which users can select which information is displayed. Users can, for example, choose different abundance maps to display at one time or change the visibility of one set of data versus another. Another approach is to investigate the use of transparency 9 in visualization whereby abundance maps can be used as virtual transparent overlays over an underlying grayscale image representing the luminance information in the image. With this approach we can combine the abundance maps into one image and develop transparency techniques to illustrate the desired properties resulting in better interpretation of the data. Additionally, our knowledge of human vision can allow us to address other aspects of visualization. For example, we can adjust the spatial frequency content in the image to compensate for the contrast sensitivity of the human eye. We will also consider other aspects of visualization such as texture and depth, which may enable us to increase the information content in an image. The effectiveness of these techniques lies in how much they can help users visually interpret and use the data, and will be evaluated through psychophysical experiments. For example, to measure the effectiveness of various color encoding schemes, we can evaluate the accuracy in judging abundances in target areas in the image. We also propose experiments that gauge the preference and scientific usefulness in both experts and non-experts point of view of the various visualization techniques. Techniques such as paired-comparison analyzed using Thurstone s Law of Comparative Judgment and Dual Scaling allow us to measure what is typically believed to be subjective and analyze individual differences. Due the availability of experts in the remote sensing field in the Center, we are uniquely situated to evaluate the success of our techniques. This project will also supply partial funding for the presentation of this research at the SPIE Defense and Security Symposium, Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery, April 2006.

4 Collaboration Benefits The Chester F. Carlson Center for Imaging Science places an emphasis both in research and in education on a systems approach to imaging that encompasses the complete imaging chain. While individual labs may focus on particular links in the chain, this collaborative project will bring together aspects of data analysis and human perception in a way that benefits two areas that traditionally have been quite distinct in the history of the Center. The application of visualization to data products that are used in remote sensing will serve to increase the functional usefulness of the analytical techniques used to reduce and interpret hyperspectral imagery. In return, applying visualization techniques to remote sensing data serves as a practical test bed for the development of techniques that can be applied to other areas of imaging ranging from medicine to astronomy in which multidimensional data are used. More specifically, the project described here is one that will have tangible benefits to the PI, Hongqin (Cathy) Zhang, and co-pi, David Messinger. This research will be of primary focus for Cathy s PhD dissertation. Her thesis is on the development, implementation, and evaluation of visualization techniques for multidimensional graphical information display. It is recognized that although there are broad perceptual issues that can be applied to various visualization tasks, the specifics of implementation will be application specific. As such, this project will facilitate her continued research in this area as well as provide an opportunity to present her findings to a broader audience outside of the color science community. David Messinger s research involves the investigation of physical and geophysical processes and properties through analysis of remotely sensed data. In particular, he is interested in the specialties of hyperspectral and multispectral image analysis. His research includes the development of techniques for the spectral unmixing of spectral signatures in the imagery discussed here. Because of the increased importance of this type of imagery, an emphasis on visualization is gaining in order to increase the utility of the techniques being developed. In conjunction with others in the Remote Sensing group, including Drs. Carl Salvaggio and John Kerekes, Dr. Messinger is spearheading an effort to build collaboration with MCSL to address this need. We believe that success in this area will lead to further collaboration within the college of science in applying visualization techniques to many different types of data. This includes both data being collected by familiar laboratories studying medical imaging and astronomy and less familiar groups involved in imaging in chemistry, physics, and environmental science. With the increased emphasis on research in the Institute, this project fits in with both the Center s and the College s strategic plan.

5 References Ethan D. Montag, The Use of Color in Multidimensional Graphical Information Display. The Seventh Color Imaging Conference: Color Science, Systems, and Application, (1999) 4. Colin Ware, Color sequences for univariate maps: theory, experiments, and principles. IEEE Computer Graphics & Applications, (1988) 5. Colin Ware, Using color dimensions to display data dimensions. Human Factors, 30(2), (1988) 6. Christopher G. Healey, Choosing effective colors for data visualization. IEEE Visualization, Proceedings of the Conference on Visualization 96, (1996) 7. Penny Rheingans, Chris Landreth, Perceptual principles for effective visualizations. In Perceptual Issues in Visualization, Georges Grinstein and Haim Levkowits, Springer- Verlag, 1995, pp (1995) 8. R. M. Boynton & C. X. Olson, Salience of chromatic basic color terms confirmed by three measures, Vision Research, 30, (1990) 9. Franz Faul and Vebjorn Ekroll, Psychophysical model of chromatic perceptual transparency based on subtractive color mixture. J.Opt.Soc.Am.A, 19, 6. June 2002.

6 Budget Request Description Amount Summer stipend $5,000 Miscellaneous Supplies (Software, etc.) & Partial Travel to SPIE conference $1,000 Total $6,000 Project plan The following table outlines the anticipated timeline for the project: 1. Preprocess data June Implement visualization schemes July Psychophysical evaluation Aug Conference submission October Conference April 2006 Milestones and Outcomes The milestones for the project are associated with each step in the project timeline: The preprocessing of the data will be accomplished by Dr. Messinger as part of his ongoing research. The processed data are accessible to Cathy over the CIS network. In addition the original data are available on the Hyperion web site. It is anticipated that the results of the visualization can lead to insight in the development of the data processing algorithms. The implementation of visualization schemes can be thought of as exploratory research in that novel techniques will be implemented and evaluated subjectively for use in the later psychophysical experiments. Psychophysical evaluation involves the implementation and execution of various experiments to gauge the effectiveness of the visualization schemes. As described above, these experiments will include tasks that measure the utility and accuracy of the visualizations as well as their utility in terms of user preference and usability. A conference submission will be prepared to present the findings of the research. The SPIE conference will provide an opportunity for Cathy to present the research and develop scientific contacts that can help her ongoing research as well as establish relationships for future employment and research opportunities.