The topographic normalization of hyperspectral data: implications for the selection of spectral end members and lithologic mapping Jilu Feng, Benoit Rivard * , Arturo Sa ´nchez-Azofeifa Earth Observation Systems Laboratory (EOSL), Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2E3 Received 20 June 2002; received in revised form 27 November 2002; accepted 1 December 2002 Abstract Compact Airborne Spectrographic Imager (CASI) hyperspectral data is used to investigate the effects of topography on the selection of spectral end members, and to assess whether the topographic correction improves the discrimination of rock units for lithologic mapping. A publicly available Digital Elevation Model (DEM), at a scale of 1:50,000, is used to model the radiance variation of the scene as a function of topography, assuming a Lambertian surface. Skylight is estimated and removed from the airborne data using a dark object correction. The CASI data is corrected on a pixel-by-pixel basis to normalize the scene to a uniform solar illumination and viewing geometry. The results show that topography has the effect of expanding end member clusters at times resulting in the overlap of clusters and that the correction process can effectively reduce the variation in detected radiance due to changes in local illumination. When topographic effects are embedded in the hyperspectral data, methods typically used for the selection of end members, such as the convex hull method, can miss end members or result in the selection of nonrepresentative pixels as end members. Thus, end members selected by some conventional methods are very likely ‘‘incomplete’’ or ‘‘nonrepresentative’’ if the topographic effect is embedded in the data. As shown in this study, the topographic correction can reveal hidden end members and achieve a better representation of end members via the statistical center of isolated clusters. D 2003 Elsevier Science Inc. All rights reserved. Keywords: Compact Airborne Spectrographic Imager (CASI); Digital Elevation Model (DEM); End member; Topography; Hyperspectral 1. Introduction For decades, topography has been known to introduce variations in the radiance detected by air and spaceborne sensors (Temps & Coulson, 1977; Smith, Lin, &, Ranson, 1980). The effect of topography on remotely sensed data has been explored by many researchers (Conese, Gilabert, Maselli, & Bottai, 1993; Gu & Gillespie, 1998; Itten & Meyer, 1993; Kimes & Kirchner, 1981; Sandmeier & Itten, 1997) who have attempted to model and reduce the influ- ence of local terrain slope and aspect with the aim of improving land cover identification. Such studies have focused on multispectral data with the goal of looking at the anisotropic effects of topography on surface reflectance. Few examples in the literature explore the effect of top- ography in hyperspectral data sets (Combal & Isaka, 2002), particularly in terms of its influence on the signature of spectral end members (Adams, Smith & Johnson, 1986). In geology, the analysis of hyperspectral data for lithologic mapping and mineral exploration is now becoming a routine (Adams et al., 1986; Boardman & Kruse, 1994) and the use of representative spectra of rock units of interest (generally referred to as end members) is key for referencing targets of interest. End members can be obtained from in situ field measure- ments or, more typically, they are extracted from imagery data due to a number of possible constraints including the remoteness of the study area that may preclude in situ measurements. Consequently, recent research (Bateson & Curtiss, 1996; Boardman, 1993; Ifarraguerri, 1999) has started to focus on the development of methodologies (Kneubu ¨hler, Schaepman, Schla ¨pfer, & Itten, 1998) that can be used to select feasible end members from hyper- spectral data sets. Methodologies such as the Minimum Volume Transform (Craig, 1994), the N-Dimensional Pro- jection (Jimenez & Landgrebe, 1999), Convex Set Theory (Boardman, 1993), and the Maximum Noise Fraction Trans- form (Green, Berman, Switzer, & Craig, 1988; Gordon, 0034-4257/03/$ - see front matter D 2003 Elsevier Science Inc. All rights reserved. doi:10.1016/S0034-4257(03)00002-6 * Corresponding author. Tel.: +1-780-492-0345; fax: +1-780-492-2030. E-mail address: benoit.rivard@ualberta.ca (B. Rivard). www.elsevier.com/locate/rse Remote Sensing of Environment 85 (2003) 221 – 231