Spectrometry and Hyperspectral Remote Sensing for Road Centerline Extraction and Evaluation of Pavement Condition

Remote sensing may offer quick and economical methods of surveying road centerline geometry and condition. This research uses a combination of 4 m, 224-band AVIRIS hyperspectral imagery, and about 6000 field-gathered spectra, to develop a spectral library of urban materials. It attempts to extract roads of various types, and investigates the spectral signatures associated with different qualities of pavement. In general the methods are successful in identifying roads and distinguishing between principal construction materials (e.g. concrete vs asphalt). To a limited extent it has been possible to automate the process of centerline extraction. However, asphalt pavement is spectrally similar to certain types of composite roofing materials, hence classification is imperfect, and considerable additional research is required to perfect the extraction of road signatures. The extraction technique holds promise in rural areas where there is much less potential for confusion between asphalt and surrounding materials. It is possible to estimate age of pavement, and to the extent that this correlates with pavement health, hyperspectral analysis is useful, but the traditional indicators of pavement health (e.g. cracking and rutting) are sub-meter phenomena and clearly not detectable in 4 m imagery. Finally, this research argues that since much of the discriminating ability of hyperspectral remote sensing is concentrated within a few wave bands, it should be possible to design a sensor of lower spectral resolution (i.e. multispectral), specifically for transportation and urban remote sensing, that could achieve many of these objectives at a much lower cost. INTRODUCTION Geometric centerlines of transportation features are a foundation data set for the management of transportation infrastructure and assets. The quality standards for these data have evolved considerably over the last three decades as applications have become more demanding, with positional accuracy requirements shrinking from tens of meters to a few centimeters. A number of survey technologies have been applied to the centerline problem, ranging from static field survey to GPS and photogrammetry. Each technology has a cost and benefit associated with it, and addresses a niche in the continuum of data quality requirements (Noronha 2001). It would be a simplification to argue that more “accurate” (loosely defined) is necessarily and universally better. Current data models such as UNETRANS (Curtin et al, 2001) link together multiple centerline representations of the same object (e.g. roadway, carriageway and lane), with carriageways inheriting properties of parent roadways, and lanes in turn inheriting properties of carriageways. Geometry and quality requirements can be associated with each level of representation, and in this broader view it becomes clear that both ±20 cm and ±20 m data have their uses, in their respective applications and cost-benefit domains. Remote sensing can assist in the survey of centerlines at all levels. Spatial resolution is clearly pivotal, with 30 m imagery suitable for identifying freeway rights of way, and 5 cm aerial photography appropriate for detailed asset inventory. Spectral resolution is an issue as well: high spectral resolution can often offset inadequacies in spatial resolution, particularly with regard to automated extraction. Multispectral Thematic Mapper (TM) imagery from the Landsat7 missions is inexpensive and widely available for the world. To what extent could imagery of this type, appropriately processed, offer centerline surveyors a source of road geometry or other data relevant for infrastructure management? As with any centerline survey method, the comparative economics of remote sensing depend on the density of roadways and accessibility of the area. The effectiveness of remote sensing further depends on the ability to distinguish between roads and surrounding surface materials. In rural areas where asphalt roads are surrounded by vegetation, the problem is relatively simple, impeded only by foliage and cloud cover. Urban areas present a difficult challenge, however, because of the abundance of artificial materials such as driveways and roofs that have spectral properties substantially similar to those of roads. Because context has much to do with centerline extraction, the road extraction problem has to be considered as part of the wider issue of classification and mapping of urban materials. An important element in this research is the development of a Spectral Library of urban material reflectance. In general this provides a reference against which urban materials can be compared and identified. A library can be developed using either a handheld spectrometer in the field, or unambiguous material signatures (i.e. known to be pure or unmixed) from remotely sensed imagery, with appropriate corrections applied in either case. One value of the library is in cataloguing variations in observed signatures in space and time. Clearly the library is most useful at the highest spectral resolution possible because it can be coarsened to match samples of lower resolution data. The discussion below is centered on hyperspectral data and analysis. The Jet Propulsion Laboratory's 224-band Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has flown a series of annual missions over parts of urban Santa Barbara, producing reliable data in at least three consecutive years. In addition, more than 6,000 additional point spectra have been recorded with a field spectrometer. This paper focuses on three applications of the spectral library: (a) The ability to detect roads in hyperspectral imagery, producing centerline maps. This was discussed above. (b) The ability to sense pavement health. Oxidation, fading and contamination of asphalt affect its appearance over time, and extensive pavement cracking probably affects signatures even at the 4 m pixel level. Remote sensing is unlikely to be as thorough as field survey in evaluating pavement quality, but in terms of cost and turnaround time, there may be significant potential benefits for some applications. (c) Spectral reduction or tuning. The purpose is to identify wavebands that best discriminate between urban materials, particularly pavement. If those bands are sufficiently limited and consistent, it may be possible to design a lower spectral resolution (i.e. multispectral) sensor, optimized for urban/transportation infrastructure mapping, that could achieve comparable results at a far lower cost.