The fifteen-channel Multispectral Thermal Imager (MTI) provides accurately calibrated satellite imagery for a variety of scientific and programmatic purposes. To be useful, the calibrated pixels from the individual detectors on the focal plane of this pushbroom sensor must be resampled to a regular grid corresponding to the observed scene on the ground. In the LEVEL1B_R_COREG product, it is required that the pixels from different spectral bands and from different sensor chip assemblies all be coregistered to the same grid. For the LEVEL1B_R_GEO product, it is further required that this grid be georeferenced to the Universal Transverse Mercator coordinate system. It is important that an accurate registration is achieved, because most of the higher level products (e.g. ground reflectance) are derived from these LEVEL1B_R products. Initially, a single direct georeferencing approach was pursued for performing the coregistration task. Although this continues to be the primary algorithm for our automated pipeline registration, we found it advantageous to pursue alternative approaches as well. This paper surveys these approaches, and offers lessons learned during the three years we have been addressing the coregistration requirements for MTI imagery at the Los Alamos National Laboratory (LANL).
The Multispectral Thermal Imager (MTI) is a technology test and demonstration satellite whose primary mission involved a finite number of technical objectives. MTI was not designed, or supported, to become a general purpose operational satellite. The role of the MTI science team is to provide a core group of system-expert scientists who perform the scientific development and technical evaluations needed to meet programmatic objectives. Another mission for the team is to develop algorithms to provide atmospheric compensation and quantitative retrieval of surface parameters to a relatively small community of MTI users. Finally, the science team responds and adjusts to unanticipated events in the life of the satellite. Broad or general lessons learned include the value of working closely with the people who perform the calibration of the data as well as those providing archived image and retrieval products. Close interaction between the Los Alamos National Laboratory (LANL) teams was very beneficial to the overall effort as well as the science effort. Secondly, as time goes on we make increasing use of gridded global atmospheric data sets which are products of global weather model data assimilation schemes. The Global Data Assimilation System information is available globally every six hours and the Rapid Update Cycle products are available over much of the North America and its coastal regions every hour. Additionally, we did not anticipate the quantity of validation data or time needed for thorough algorithm validation. Original validation plans called for a small number of intensive validation campaigns soon after launch. One or two intense validation campaigns are needed but are not sufficient to define performance over a range of conditions or for diagnosis of deviations between ground and satellite products. It took more than a year to accumulate a good set of validation data. With regard to the specific programmatic objectives, we feel that we can do a reasonable job on retrieving surface water temperatures well within the 1°C objective under good observing conditions. Before the loss of the onboard calibration system, sea surface retrievals were usually within 0.5°C. After that, the retrievals are usually within 0.8°C during the day and 0.5°C at night. Daytime atmospheric water vapor retrievals have a scatter that was anticipated: within 20%. However, there is error in using the Aerosol Robotic Network retrievals as validation data which may be due to some combination of calibration uncertainties, errors in the ground retrievals, the method of comparison, and incomplete physics. Calibration of top-of-atmosphere radiance measurements to surface reflectance has proven daunting. We are not alone here: it is a difficult problem to solve generally and the main issue is proper compensation for aerosol effects. Getting good reflectance validation data over a number of sites has proven difficult but, when assumptions are met, the algorithm usually performs quite well. Aerosol retrievals for off-nadir views seem to perform better than near-nadir views and the reason for this is under investigation. Land surface temperature retrieval and temperature-emissivity separations are difficult to perform accurately with multispectral sensors. An interactive cloud masking system was implemented for production use. Clouds are so spectrally and spatially variable that users are encouraged to carefully evaluate the delivered mask for their own needs. The same is true for the water mask. This mask is generated from a spectral index that works well for deep, clear water, but there is much variability in water spectral reflectance inland and along coasts. The value of the second-look maneuvers has not yet been fully or systematically evaluated. Early experiences indicated that the original intentions have marginal value for MTI objectives, but potentially important new ideas have been developed. Image registration (the alignment of data from different focal planes) and band-to-band registration has been a difficult problem to solve, at least for mass production of the images in a processing pipeline. The problems, and their solutions, are described in another paper.
Accurate coregistration of images from the Multispectral Thermal Imager (MTI) is needed to properly align bands for spectral analysis and physical retrievals, such as water surface temperature, land-cover classification, or small target identification. After accounting for spacecraft motion, optical distortion, and geometrical perspective, the irregularly-spaced pixels in the images must be resampled to a common grid. What constitutes an optimal resampling depends, to some extent, on the needs of the user. A good resampling trades off radiometric fidelity, contrast preservation for small objects, and cartographic accuracy -- and achieves this compromise without unreasonable computational effort. The standard MTI coregistration product originally used a weighted-area approach to achieve this irregular resampling, which generally over-smoothes the imagery and reduces the contrast of small objects. Recently, other resampling methods have been implemented to improve the final coregistered image. These methods include nearest-neighbor resampling and a tunable, distance-weighted resampling. We will discuss the pros and cons of various resampling methods applied to MTI images, and show results of comparing the contrast of small objects before and after resampling.
Hyperspectral imagery data sets present an interesting challenge to feature extraction algorithm developers. Beyond the immediate problem of dealing with the sheer amount of spectral information per pixel in a hyperspectral image, the remote sensing scientist must explore a complex algorithm space in which both spatial and spectral signatures may be required to identify a feature of interest. Rather than carry out this algorithm exploration by hand, we are interested in developing learning systems that can evolve these algorithms.
We describe a genetic programming/supervised classifier software system, called GENIE, which evolves image processing tools for remotely sensed imagery. Our primary application has been land-cover classification from satellite imagery. GENIE was developed to evolve classification algorithms for multispectral imagery, and the extension to hyperspectral imagery presents a chance to test a genetic programming system by greatly increasing the complexity of the data under analysis, as well as a chance to find interesting spatio-spectral algorithms for hyperspectral imagery. We demonstrate our system on publicly available imagery from the new Hyperion imaging spectrometer onboard the NASA Earth Observing-1 (EO-1) satellite.
In the focal plane of a pushbroom imager, a linear array of pixels is scanned across the scene, building up the image one row at a time. For the Multispectral Thermal Imager (MTI), each of fifteen different spectral bands has its own linear array. These arrays are pushed across the scene together, but since each band's array is at a different position on the focal plane, a separate image is produced for each band. The standard MTI data products (LEVEL1B_R_COREG and LEVEL1B_R_GEO) resample these separate images to a common grid and produce coregistered multispectral image cubes. The coregistration software employs a direct ``dead reckoning' approach. Every pixel in the calibrated image is mapped to an absolute position on the surface of the earth, and these are resampled to produce an undistorted coregistered image of the scene. To do this requires extensive information regarding the satellite position and pointing as a function of time, the precise configuration of the focal plane, and the distortion due to the optics. These must be combined with knowledge about the position and altitude of the target on the rotating ellipsoidal earth. We will discuss the direct approach to MTI coregistration, as well as more recent attempts to tweak the precision of the band-to-band registration using correlations in the imagery itself.
The mission of the Multispectral Thermal Imager (MTI) satellite is to demonstrate the efficacy of highly accurate multispectral imaging for passive characterization of urban and industrial areas, as well as sites of environmental interest. The satellite makes top-of-atmosphere radiance measurements that are subsequently processed into estimates of surface properties such as vegetation health, temperatures, material composition and others. The MTI satellite also provides simultaneous data for atmospheric characterization at high spatial resolution. To utilize these data the MTI science program has several coordinated components, including modeling, comprehensive ground-truth measurements, image acquisition planning, data processing and data interpretation and analysis. Algorithms have been developed to retrieve a multitude of physical quantities and these algorithms are integrated in a processing pipeline architecture that emphasizes automation, flexibility and programmability. In addition, the MTI science team has produced detailed site, system and atmospheric models to aid in system design and data analysis. This paper provides an overview of the MTI research objectives, data products and ground data processing.
We present a detection process capable of directly imaging the transverse amplitude, phase, and Doppler shift of coherent electromagnetic fields. Based on coherent detection principles governing conventional heterodyned RADAR/LADAR systems, Fourier Transform Heterodyne incorporates transverse spatial encoding of the reference local oscillator for image capture. Appropriate selection of spatial encoding functions allows image retrieval by way of classic Fourier manipulations. Of practical interest: (1) imaging may be accomplished with a single element detector/sensor requiring no additional scanning or moving components, (2) as detection is governed by heterodyne principles, near quantum limited performance is achievable, (3) a wide variety of appropriate spatial encoding functions exist that may be adaptively configured in real-time for applications requiring optimal detection, and (4) the concept is general with the applicable electromagnetic spectrum encompassing the RF through optical.