Investigation of hyperspectral remote sensing for mapping asphalt road conditions

Abstract

An investigation of hyperspectral remote sensing for mapping asphalt road conditions is undertaken in this study. Hyperspectral data acquired by the GER1500 radiometer and the Compact Airborne Spectrographic Imager (CASI) 550 sensor have been analysed, processed and interpreted. Field radiometer data were used to provide high-quality spectral measurements for developing a spectral library for asphalt, defining potential categories of the asphalt condition and minimizing the dimension of the hyperspectral space. Analysis of spectral signatures indicated that asphalt condition is affected by asphalt age, material quality and road circulation, and that it led to the definition of five potential categories. Two of them indicate asphalt in high distress and surfaces that need rehabilitation. Among several others, the following processing methods were revealed as the most suitable for detecting asphalt condition: Principal Component Analysis (PCA), thresholding of colour transformation images, unsupervised classification Iterative Self-organizing Data Analysis (IsoData), supervised classification Spectral Angle Mapper (SAM) and texture measurements using the Grey-level Co-occurrence Matrix operator. The results indicated that hyperspectral remote sensing is capable of mapping asphalt road conditions with respect to the categorization proposed within this study.