Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery

In this paper, we evaluate the capability of the high spatial resolution airborne Digital Airborne Imaging System (DAIS) imagery for detailed vegetation classification at the alliance level with the aid of ancillary topographic data. Image objects as minimum classification units were generated through the Fractal Net Evolution Approach (FNEA) segmentation using eCognition software. For each object, 52 features were calculated including spectral features, textures, topographic features, and geometric features. After statistically ranking the importance of these features with the classification and regression tree algorithm (CART), the most effective features for classification were used to classify the vegetation. Due to the uneven sample size for each class, we chose a non-parametric (nearest neighbor) classifier. We built a hierarchical classification scheme and selected features for each of the broadest categories to carry out the detailed classification, which significantly improved the accuracy. Pixel-based maximum likelihood classification (MLC) with comparable features was used as a benchmark in evaluating our approach. The objectbased classification approach overcame the problem of saltand-pepper effects found in classification results from traditional pixel-based approaches. The method takes advantage of the rich amount of local spatial information present in the irregularly shaped objects in an image. This classification approach was successfully tested at Point Reyes National Seashore in Northern California to create a comprehensive vegetation inventory. Computer-assisted classification of high spatial resolution remotely sensed imagery has good potential to substitute or augment the present ground-based inventory of National Park lands. Introduction Remote sensing provides a useful source of data from which updated land-cover information can be extracted for assessing and monitoring vegetation changes. In the past several decades, airphoto interpretation has played an important role in detailed vegetation mapping (Sandmann and Lertzman, 2003), while applications of coarser spatial resolution satellite Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery Qian Yu, Peng Gong, Nick Clinton, Greg Biging, Maggi Kelly, and Dave Schirokauer imagery such as Landsat Thematic Mapper (TM) and SPOT High Resolution Visible (HRV) alone have often proven insufficient or inadequate for differentiating species-level vegetation in detailed vegetation studies (Kalliola and Syrjanen, 1991; Harvey and Hill, 2001). Classification accuracy is reported to be only 40 percent or less for thematic information extraction at the species-level with these image types (Czaplewski and Patterson, 2003). However, high spatial resolution remote sensing is becoming increasingly available; airborne and spaceborne multispectral imagery can be obtained at spatial resolutions at or better than 1 m. The utility of high spatial resolution for automated vegetation composition classification needs to be evaluated (Ehlers et al., 2003). High spatial resolution imagery initially thrives on the application of urban-related feature extraction has been used (Jensen and Cowen, 1999; Benediktsson et al., 2003; Herold et al., 2003a), but there has not been as much work in detailed vegetation mapping using high spatial resolution imagery. This preference for urban areas is partly due to the proximity of the spectral signatures for different species and the difficulties in capturing texture features for vegetation (Carleer and Wolff, 2004). While high spatial resolution remote sensing provides more information than coarse resolution imagery for detailed observation on vegetation, increasingly smaller spatial resolution does not necessarily benefit classification performance and accuracy (Marceau et al., 1990; Gong and Howarth, 1992b; Hay et al., 1996; Hsieh et al., 2001). With the increase in spatial resolution, single pixels no longer capture the characteristics of classification targets. The increase in intra-class spectral variability causes a reduction of statistical separability between classes with traditional pixel-based classification approaches. Consequently, classification accuracy is reduced, and the classification results show a salt-and-pepper effect, with individual pixels classified differently from their neighbors. To overcome this so-called H-resolution problem, some pixel-based methods have already been implemented, mainly consisting of three categories: (a) image pre-processing, such as low-pass filter and texture analysis (Gong et al., 1992; Hill and Foody, 1994), (b) contextual classification (Gong and Howarth, 1992a), and (c) post-classification processing, such as mode filtering, morphological filtering, rule-based processing, and probabilistic relaxation (Gong and Howarth, 1989; Shackelford and Davis, 2003; Sun et al., 2003). A common aspect of these methods is that they incorporate spatial information to characterize each class using neighborhood relationships. PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING J u l y 2006 799 Qian Yu, Peng Gong, Nick Clinton, Greg Biging, and Maggi Kelly are with the Department of Environmental Science, Policy and Management, 137 Mulford Hall, University of California, Berkeley, CA 94720-3110 (gong@nature.berkeley.edu). Peng Gong is with the State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, 100101, Beijing, China. Dave Schirokauer is with the Point Reyes National Seashore, Point Reyes, CA 94956. Photogrammetric Engineering & Remote Sensing Vol. 72, No. 7, July 2006, pp. 799–811. 0099-1112/06/7207–0799/$3.00/0 © 2006 American Society for Photogrammetry and Remote Sensing 04-153 6/9/06 9:57 AM Page 799