Optical Engineering 47͑10͒, 106201 ͑October 2008͒ Spectral reflectance and emissivity of man-made surfaces contaminated with environmental effects John P. Kerekes, MEMBER SPIE Abstract. Spectral remote sensing has evolved considerably from the Rochester Institute of Technology early days of airborne scanners of the 1960’s and the first Landsat mul- Chester F. Carlson Center for Imaging Science tispectral satellite sensors of the 1970’s. Today, airborne and satellite 54 Lomb Memorial Drive hyperspectral sensors provide images in hundreds of contiguous narrow Rochester, New York 14623 spectral channels at spatial resolutions down to meter scale and span- E-mail: [email protected] ning the optical spectral range of 0.4 to 14 ␮m. Spectral reflectance and emissivity databases find use not only in interpreting these images but also during simulation and modeling efforts. However, nearly all existing Kristin-Elke Strackerjan databases have measurements of materials under pristine conditions. Aerospace Engineering Test Establishment The work presented extends these measurements to nonpristine condi- P.O. Box 6550 Station Forces tions, including materials contaminated with sand and rain water. In par- Cold Lake, Alberta ticular, high resolution spectral reflectance and emissivity curves are pre- T9M 2C6 Canada sented for several man-made surfaces ͑asphalt, concrete, roofing shingles, and vehicles͒ under varying amounts of sand and water. The relationship between reflectance and area coverage of the contaminant Carl Salvaggio, MEMBER SPIE is reported and found to be linear or nonlinear, depending on the mate- Rochester Institute of Technology rials and spectral region. In addition, new measurement techniques are Chester F. Carlson Center for Imaging Science presented that overcome limitations of existing instrumentation and labo- 54 Lomb Memorial Drive ratory settings. These measurements enable simulation of optical im- Rochester, New York 14623 ages with objects in the scene attributed with realistic surface reflectance and emissivity spectra. © 2008 Society of Photo-Optical Instrumentation Engineers. ͓DOI: 10.1117/1.3000433͔ Subject terms: spectral reflectance; spectral emissivity; environmental effects; optical simulation; optical modeling; hyperspectral imaging. Paper 080264R received Apr. 5, 2008; revised manuscript received Sep. 5, 2008; accepted for publication Sep. 6, 2008; published online Oct. 22, 2008. 1 Introduction stitute of Technology ͑RIT͒. DIRSIG is a first-principles physics-based simulation model that uses computer-aided Spectral remote sensing has evolved considerably from the design ͑CAD͒ descriptions of natural and man-made ob- early days of airborne scanners of the 1960’s and the first jects and performs ray tracing of all reflected and emitted Landsat multispectral satellite sensors of the 1970’s. Today, radiation to produce radiometrically correct at-sensor spec- airborne and satellite hyperspectral sensors provide images tral radiances for scenes spanning the 0.4- to 14-␮m spec- in hundreds of contiguous narrow spectral channels at spa- tral region. A key aspect of this model is the database of the tial resolutions down to meter scale and spanning the spec- necessary spectral reflectance and emissivity curves that at- ␮ 1–3 tral range of 0.4 to 14 m. These data find applications tribute each facet of objects, as well as areas of the back- in land cover mapping,4 agricultural crop assessment,5 and 6 ground surface. These material spectra are acquired from target detection applications, among others. While many archival databases or measured with field spectrometers to of these applications involve supervised analysis, where the accurately represent the reflectance or emittance character- analyst interprets the data by selecting known training areas istics of real surfaces. within the image and use those as prototypes to analyze the While existing spectral reflectance and emissivity data- rest of image, increasing sophistication of the data has en- 7 bases provide a wide range of materials and some variabil- abled the use of reference spectral libraries in the analysis. ity associated with their sampled nature, the vast majority These spectral reflectance and emissivity databases cap- are for materials measured under ideal or pristine condi- ture the spectral absorption features and spectral trends use- tions. This is desirable from the point of view of capturing ful in mapping materials in the analysis of spectral imagery, the data relevant for that material alone, so it can be used in but they are also useful and, in fact, necessary for the for- spectral matching analysis techniques or to simulate scenes ward simulation and modeling of spectroradiometric under ideal conditions. However, most real spectral images scenes. One such simulation model is digital imaging and ͑ ͒ 8 are of scenes where the objects are observed under nonideal remote sensing image generation DIRSIG , which has conditions such as having been weathered, soiled, or wet been developed over the past 20 years at the Rochester In- due to recent rainfall. This reality of typical scenes led to a recent effort to 0091-3286/2008/$25.00 © 2008 SPIE expand the capability of DIRSIG to simulate more realistic Optical Engineering 106201-1 October 2008/Vol. 47͑10͒ Kerekes, Strackerjan, and Salvaggio: Spectral reflectance and emissivity of man-made surfaces… Fig. 1 Vehicle measurement setup ͑red circle indicates the location of the instruments͒. ͑Color online only.͒ scenes that include these types of environmental effects.9 This work presents the results of laboratory and field mea- surements conducted under that research. Section 2 de- scribes characteristics of existing databases and summa- rizes related work in the measurement and modeling of contaminated surfaces. Section 3 presents the measurement techniques used in our research, including some new tech- niques necessary to overcome limitations of the instrumen- tation and laboratory environment. Section 4 presents the results of the field and laboratory measurements, while Sec. 5 provides a summary and discussion of these results. 2 Spectral Databases and Related Work Fig. 2 Laboratory setup for the ASD. The sample under test is Most published spectral reflectance databases contain mea- blurred in this picture, as it rotates on the potter’s wheel and tempo- rally averaged spectra are collected. surements of minerals, soils, or vegetation and were estab- lished for geological, agronomy, or agricultural applica- tions. The ENVI remote sensing software ͑ITT Visual Information Solutions, Boulder, Colorado͒ provides a com- materials for varying amounts of sand and water. Measure- pilation of spectral reflectance libraries, including materials ments in the reflective part of the spectrum mentioned before plus a variety of man-made ͑400 to 2500 nm͒ were made with an Analytical Spectral materials.10–16 However, nearly all of these measurements Devices ͑ASD͒ FieldSpec Pro ͑ASD, Incorporated, Boul- were made in the laboratory or in the field under pristine der, Colorado͒, while the emissive measurements conditions. That is, the material was dry and clean so that ͑8to14␮m͒ were made with a Designs and Prototypes the measured reflectance was representative of solely that ͑D&P͒ Instruments 102F FTIR Spectrometer ͑DP Instru- material. ments, Simsbury, Connecticut͒. The ASD was used with a Such pristine databases are appropriate for material identification or spectral unmixing applications, but they fail to capture the variability in reflectance that can arise in outdoor natural settings, and is necessary for realism in synthetic image generation applications. There has been some work in characterizing the effects of disturbances on soils17 and measurements of the effects of thin layers of water,18 but no previous work could be found that con- ducted a systematic study of the impact on spectra of envi- ronmental contaminants for several man-made surfaces. 3 Measurement Techniques Measurements were made both in the field and in the labo- ratory. The field measurements were made to capture the real-world effects of surface particles ͑road dust͒ and water for a variety of civilian vehicles. The laboratory measure- Fig. 3 Laboratory setup for the D&P ͑the red circle indicates where ments were made under controlled conditions to study the the viewing port for the D&P was situated among the trays͒. ͑Color effects on reflectance and emissivity of select man-made online only.͒ Optical Engineering 106201-2 October 2008/Vol. 47͑10͒ Kerekes, Strackerjan, and Salvaggio: Spectral reflectance and emissivity of man-made surfaces… Table 1 General instrument configuration parameters for the spectrometers used in this study. D&P Instruments Parameter ASD Field Spec Pro Model 102F microFTIR Spectral range 0.35 to 2.5 ␮m 2.0 to 16.0 ␮m Spectral resolution ͑FWHM͒ • 3 nm at 0.7 ␮m4cm−1 at 2 ␮m, 1 sec/scan •10nmat1.4␮m •10nmat2.1␮m Spectral sampling • 1.4 nm from 0.35 to 1.0 ␮m4cm−1 • 2 nm from 1.0 to 2.5 ␮m Noise equivalent delta •1.4ϫ10−6 W/cm2 /sr/␮mat0.7␮m n/a radiance •2.4ϫ10−6 W/cm2 /sr/␮mat1.4␮m •8.8ϫ10−6 W/cm2 /sr/␮mat 2.1 ␮m Lambertian reflectance standard ͑Spectralon™͒, which was The ASD uses a fiber optic bundle to transmit light from used to convert the sample radiance to reflectance. The the input optics to the spectrometer detectors. Separate sub- D&P was used with a gold standard and temperature probe sets of the fibers lead to the different detectors required to to convert the sample radiance to emissivity. General in- span the full spectral range covered by the instrument. strument configuration parameters are listed in Table 1, and Thus, when observing heterogeneous surfaces, this design detailed discussions on the theory and protocols for these can lead to discontinuities in the measured spectrum, since measurements are available on the website for the RIT Op- the different detectors are actually observing different areas tical Properties Laboratory.19 The following describes the on the surface. This problem was overcome by the innova- specific measurement techniques used in this research in tive use of a “potter’s wheel” to rotate the sample under test field and laboratory settings.