Michael G. Gartley

Multi-Modal and Multi-Temporal Data Fusion: Outcome of the 2012 GRSS Data Fusion Contest

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.

On the zeros of the q-analogue exponential function

An asymptotic formula for the zeros, zn, of the entire function eq(x) for q<<1 is obtained. As q increases above the first collision point at q1* approximately=0.14, these zeros collide in pairs and then move off into the complex z plane. They move off as (and remain) a complex conjugate pair. The zeros of the ordinary higher derivatives and of the ordinary indefinite integrals of eq(x) vary with q in a similar manner. Properties of eq(z) for z complex and for arbitrary q are deduced. For 0<or=q<1,eq(z) is an entire function of order 0. By the Hadamard-Weierstrass factorization theorem, infinite product representations are obtained for eq(z) and for the reciprocal function eq-1(z). If q not=1, the zeros satisfy the sum rule Sigma n=1infinity (1/zn)=-1.

Topological anomaly detection performance with multispectral polarimetric imagery

Polarimetric imaging has demonstrated utility for increasing contrast of manmade targets above natural background clutter. Manual detection of manmade targets in multispectral polarimetric imagery can be challenging and a subjective process for large datasets. Analyst exploitation may be improved utilizing conventional anomaly detection algorithms such as RX. In this paper we examine the performance of a relatively new approach to anomaly detection, which leverages topology theory, applied to spectral polarimetric imagery. Detection results for manmade targets embedded in a complex natural background will be presented for both the RX and Topological Anomaly Detection (TAD) approaches. We will also present detailed results examining detection sensitivities relative to: (1) the number of spectral bands, (2) utilization of Stokes images versus intensity images, and (3) airborne versus spaceborne measurements.

Impact of BRDF on physics-based modeling as applied to target detection in hyperspectral imagery

Traditional approaches to hyperspectral target detection involve the application of detection algorithms to atmospherically compensated imagery. Rather than compensate the imagery, a more recent approach uses physical models to generate radiance signature spaces. The signature space is actually a representation of what the target might look like to the sensor as the reflectance propagates through the atmosphere. The model takes into account atmospherics, illumination conditions and target reflectivity. It is well known that the directional characteristics of reflectance vary considerably fromspecular to ideally diffuse (i.e., Lambertian). The current physical models assume the world is Lambertian. However, the reflectance properties are a function of wavelength, illumination angle, and viewing angle. The bidirectional reflectance distribution function (BRDF), which is actually a scattering function analogous to the angular scattering coefficient, describes the bidirectional reflectance values of all combinations of input-output angles and wavelength. This paper examines the impact of using the Lambertian assumption as it relates to physics bases material detection. The bidirectional reflectance studied is parameterized, based on laboratory measurements, using the Beard- Maxwell model. This parameterized reflectance is then coupled to the physics-based sensor-reaching radiance model to generate signature spaces. The signature spaces, along with hyperspectral imagery, are used in a target detection scheme where results are assessed through visual analysis.

An Analysis of the Side Slither On-Orbit Calibration Technique Using the DIRSIG Model

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.

Modeling space-based multispectral imaging systems with DIRSIG

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.

A comparison of spatial sampling techniques enabling first principles modeling of a synthetic aperture RADAR imaging platform

Simulation of synthetic aperture radar (SAR) imagery may be approached in many different ways. One method treats a scene as a radar cross section (RCS) map and simply evaluates the radar equation, convolved with a system impulse response to generate simulated SAR imagery. Another approach treats a scene as a series of primitive geometric shapes, for which a closed form solution for the RCS exists (such as boxes, spheres and cylinders), and sums their contribution at the antenna level by again solving the radar equation. We present a ray-tracing approach to SAR image simulation that treats a scene as a series of arbitrarily shaped facetized objects, each facet potentially having a unique radio frequency optical property and time-varying location and orientation. A particle based approach, as compared to a wave based approach, presents a challenge for maintaining coherency of sampled scene points between pulses that allows the reconstruction of an exploitable image from the modeled complex phase history. We present a series of spatial sampling techniques and their relative success at producing accurate phase history data for simulations of spotlight, stripmap and SAR-GMTI collection scenarios.

On the two q-analogue logarithmic functions:

There is a simple, multi-sheet Riemann surface associated with s inverse function for . A principal sheet for can be defined. However, the topology of the Riemann surface for changes each time q increases above the collision point of a pair of the turning points of . There is also a power series representation for . An infinite-product representation for is used to obtain the ordinary natural logarithm and the values of the sum rules for the zeros of . For , where . The values of the sum rules for the q-trigonometric functions, and , are q-deformations of the usual Bernoulli numbers.

Relation between degree of polarization and Pauli color coded image to characterize scattering mechanisms

Polarimetric image classification is sensitive to object orientation and scattering properties. This paper is a preliminary step to bridge the gap between visible wavelength polarimetric imaging and polarimetric SAR (POLSAR) imaging scattering mechanisms. In visible wavelength polarimetric imaging, the degree of linear polarization (DOLP) is widely used to represent the polarized component of the wave scattered from the objects in the scene. For Polarimetric SAR image representation, the Pauli color coding is used, which is based on linear combinations of scattering matrix elements. This paper presents a relation between DOLP and the Pauli decomposition components from the color coded Pauli reconstructed image based on laboratory measurements and first principle physics based image simulations. The objects in the scene are selected in such a way that it captures the three major scattering mechanisms such as the single or odd bounce, double or even bounce and volume scattering. The comparison is done between visible passive polarimetric imaging, active visible polarimetric imaging and active radio frequency POLSAR. The DOLP images are compared with the Pauli Color coded image with |HH-VV|, |HV|, |HH +VV| as the RGB channels. From the images, it is seen that the regions with high DOLP values showed high values of the HH component. This means the Pauli color coded image showed comparatively higher value of HH component for higher DOLP compared to other polarimetric components implying double bounce reflection. The comparison of the scattering mechanisms will help to create a synergy between POLSAR and visible wavelength polarimetric imaging and the idea can be further extended for image fusion.

Spectro-polarimetric bidirectional reflectance distribution function determination of in-scene materials and its use in target detection applications

For sensing systems that characterize the spectro-polarimetric radiance reaching the camera, the origin of the sensed phenomenology is a complex mixture of sources. While some of these sources do not contribute to the polarimetric signature, many do such as the polarization state of the downwelled sky radiance, the target and background p-BRDF(polarimetric bidirectional reflectance distribution function), the polarization state of the upwelled path radiance, and the sensor Mueller matrix transfer function. In this paper we derive portions of the p-BRDF in terms of both the spectral diffuse and polarimetric specular components of the reflectance using an in-scene calibration technique. This process is applied to simulated data, laboratory data, and data from a field collection. Spectra of a car panel for clean and contaminated states derived using laboratory data are injected into a hyperspectral image cube. It is shown how this target can be identified using a target specific tracking vector derived from its polarimetric signature as it moves between spatial locations within a scene.