The 2012 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society (GRSS) aimed at investigating the potential use of very high spatial resolution (VHR) multi-modal/multi-temporal image fusion. Three different types of data sets, including spaceborne multi-spectral, spaceborne synthetic aperture radar (SAR), and airborne light detection and ranging (LiDAR) data collected over the downtown San Francisco area were distributed during the Contest. This paper highlights the three awarded research contributions which investigate (i) a new metric to assess urban density (UD) from multi-spectral and LiDAR data, (ii) simulation-based techniques to jointly use SAR and LiDAR data for image interpretation and change detection, and (iii) radiosity methods to improve surface reflectance retrievals of optical data in complex illumination environments.In particular, they demonstrate the usefulness of LiDAR data when fused with optical or SAR data. We believe these interesting investigations will stimulate further research in the related areas.

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Pushbroom-style imaging systems exhibit several advantages over line scanners when used on space-borne platforms as they typically achieve higher signal-to-noise and reduce the need for moving parts. Pushbroom sensors contain thousands of detectors, each having a unique radiometric response, which will inevitably lead to streaking and banding in the raw data. To take full advantage of the potential exhibited by pushbroom sensors, a relative radiometric correction must be performed to eliminate pixel-to-pixel non-uniformities in the raw data. Side slither is an on-orbit calibration technique where a 90-degree yaw maneuver is performed over an invariant site to flatten the data. While this technique has been utilized with moderate success for the QuickBird satellite [1] and the RapidEye constellation [2], further analysis is required to enable its implementation for the Landsat 8 sensors, which have a 15-degree field-of-view and a 0.5% pixel-to-pixel uniformity requirement. This work uses the DIRSIG model to analyze the side slither maneuver as applicable to the Landsat sensor. A description of favorable sites, how to adjust the maneuver to compensate for the curvature of "linear" arrays, how to efficiently process the data, and an analysis to assess the quality of the side slither data, are presented.

ABSTRACT

The optical properties of a surface may change significantly in response to contaminants from the environment and/or human activity. We utilize a first principles, physics-based radiometric ray tracing software package to evaluate the spectral polarimetric bi-directional reflectance distribution function (p-BRDF) of the virgin and contaminated surfaces. In the absence of contaminants and allowing only single bounces, we find the simulated reflectance properties of randomly rough Gaussian surfaces to be well represented by micro-facet based p-BRDF analytical models. However the addition of contaminants introduces phenomenology that falls outside the basic assumptions of the micro-facet analytical p-BRDF models. We will present initial results of p-BRDF simulations of random Gaussian surfaces with liquid contaminants.

This work presents a radiometric model of a spatial heterodyne spectrometer (SHS) and a corresponding interferogram-processing algorithm for the calculation of calibrated spectral radiance measurements. The SHS relies on Fourier Transform Spectroscopy (FTS) principles, and shares design similarities with the Michelson Interferometer. The advantages of the SHS design, including the lack of moving parts, high throughput, and instantaneous spectral measurements, make it suitable as a field-deployable instrument. Operating in the long-wave infrared (LWIR), the imaging SHS design example included provides the capability of performing chemical detection based on reflectance and emissivity properties of surfaces of organic compounds. This LWIR SHS model outputs realistic, interferometric data and serves as a tool to find optimal SHS design parameters for desired performance requirements and system application. It also assists in the data analysis and system characterization. The interferogram-processing algorithm performs flat-fielding and phase corrections as well as apodization before recovering the measured spectral radiance from the recorded interferogram via the Inverse Fourier Transform (IFT). The model and processing algorithm demonstrate results comparable to those in the literature with a noise-equivalent change in temperature of 0.35K. Additional experiments show the algorithm's real-time processing capability, indicating the LWIR SHS system presented is feasible.

Abstract:The next Landsat satellite, which is scheduled for launch in early 2013, will carry two instruments: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). Significant design changes over previous Landsat instruments have been made to these sensors to potentially enhance the quality of Landsat image data. TIRS, which is the focus of this study, is a dual-band instrument that uses a push-broom style architecture to collect data. To help understand the impact of design trades during instrument build, an effort was initiated to model TIRS imagery. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool was used to produce synthetic "on-orbit" TIRS data with detailed radiometric, geometric, and digital image characteristics. This work presents several studies that used DIRSIG simulated TIRS data to test the impact of engineering performance data on image quality in an effort to determine if the image data meet specifications or, in the event that they do not, to determine if the resulting image data are still acceptable. OPEN ACCESSRemote Sens. 2012, 4 2478

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.