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The LDCM will carry two new pushbroom imagers. The nine band Operational Land Imager (OLI) and the two band Thermal Infrared Sensor (TIRS) represent significant changes in Landsat Sensor Technology, as does the plan to generate a common data product from two sensors (registered 11 band data). This effort is designed to generate synthetic images that will predict the LDCM performance pre-launch, will support trouble shooting of instrument behavior during initialization and operation, and perhaps most importantly, can support Landsat remote sensing science activities throughout the mission. This paper reports on initial results of efforts to simulate all of the phenomena that can lead to non-uniformity variations in an image. This includes detector-to-detector and array-to-array non-uniformities due to variations in relative spectral response (RSR), gain, bias, and non-linearities. The overall approach involves modifying the DIRSIG image simulation model to allow detailed modeling of the Landsat orbit and LDCM sensors parameters and generation of test scenes to allow full end-to-end image simulations incorporating illumination, atmospheric, sensor geometry, and radiometry effects as a function of time, meteorology, spectral range from the visible through the long wave infrared, and spatial effects. The results reported in this paper emphasize the modeling and initial demonstration of the non-uniformity phenomenology.

Visualization of the high-dimensional data set that makes up hyperspectral images necessitates a dimensionality reduction approach to make that data useful to a human analyst. The expression of spectral data as color images, individual pixel spectra plots, principal component images, and 2D/3D scatter plots of a subset of the data are a few examples of common techniques. However, these approaches leave the user with little ability to intuit knowledge of the full N-dimensional spectral data space or to directly or easily interact with that data. In this work, we look at developing an interactive, intuitive visualization and analysis tool based on using a Poincar� disk as a window into that high dimensional space. The Poincar� disk represents an infinite, two-dimensional hyperbolic space such that distances and areas increase exponentially as you move farther from the center of the disk. By projecting N-dimensional data into this space using a non-linear, yet relative distance metric preserving projection (such as the Sammon projection), we can simultaneously view the entire data set while maintaining natural clustering and spacing. The disk also provides a means to interact with the data; the user is presented with a "fish-eye" view of the space which can be navigated and manipulated with a mouse to "zoom" into clusters of data and to select spectral data points. By coupling this interaction with a synchronous view of the data as a spatial RGB image and the ability to examine individual pixel spectra, the user has full control over the data set for classification, analysis, and instructive use.

The Landsat Data Continuity Mission (LDCM) focuses on a next generation global coverage, imaging system to replace the aging Landsat 5 and Landsat 7 systems. The major difference in the new system is the migration from the multi-spectral whiskbroom design employed by the previous generation of sensors to modular focal plane, multi-spectral pushbroom architecture. Further complicating the design shift is that the reflective and thermal acquisition capability is split across two instruments spatially separated on the satellite bus. One of the focuses of the science and engineering teams prior to launch is the ability to provide seamless data continuity with the historic Landsat data archive. Specifically, the challenges of registering and calibrating data from the new system so that long-term science studies are minimally impacted by the change in the system design. In order to provide the science and engineering teams with simulated pre-launch data, an effort was undertaken to create a robust end-to-end model of the LDCM system. The modeling environment is intended to be flexible and incorporate measured data from the actual system components as they were completed and integrated. The output of the modeling environment needs to include not only radiometrically robust imagery, but also the meta-data necessary to exercise the processing pipeline. This paper describes how the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model has been utilized to model space-based, multi-spectral imaging (MSI) systems in support of systems engineering trade studies. A mechanism to incorporate measured focal plane projections through the forward optics is described. A hierarchal description of the satellite system is presented including the details of how a multiple instrument platform is described and modeled, including the hierarchical management of temporally correlated jitter that allows engineers to explore impacts of different jitter sources on instrument-to-instrument and band-to-band registration. The capabilities of a new, non-imaging instrument to simulate the measurement of platform ephemeris is also introduced. Finally, the geometric and radiometric foundations for modeling clouds in the DIRSIG model will be described and demonstrated as one of the more significant challenges in registering multi-spectral pushbroom sensor data products.