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.
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.
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.
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