Classifiers vs. input variables—The drivers in image classification for land cover mapping

Abstract

The study investigates the performance of image classifiers for landscape-scale land cover mapping and the relevance of ancillary data for the classification success in order to assess and to quantify the importance of these components in image classification. Specifically tested are the performance of maximum likelihood classification (MLC), artificial neural networks (ANN) and discriminant analysis (DA) based on Landsat7 ETM+ spectral data in combination with topographic measures and NDVI. ANN produced high accuracies of more than 75% also with limited input information, while MLC and DA produced comparable results only by incorporating ancillary data into the classification process. The superiority of ANN classification was less pronounced on the level of the single land cover classes. The use of ancillary data generally increased classification accuracy and showed a similar potential for increasing classification accuracy than the selection of the classifier. Therefore, a stronger focus on the development of appropriate and optimised sets of input variables is suggested. Also the definition and selection of land cover classes has shown to be crucial and not to be simply adaptable from existing land cover class schemes. A stronger research focus towards discriminating land cover classes by their typical spectral, topographic or seasonal properties is therefore suggested to advance image classification.

Introduction

Detailed and accurate land cover data are among the most crucial information that are required for large-scale environmental research. The knowledge of the spatial configuration of the Earth's surface is the key for assessing habitat distribution, landscape composition or land use changes and is an essential requirement for landscape modelling and scenario building, particularly in times of global change. The suitability of remote sensing for acquiring land cover data has long been recognised, but the process of generating land cover information from remotely sensed data is still far from being standardised or optimised (Foody, 2002, Lu and Weng, 2007). An extensive variety of multi-spectral image classification methods have been developed, which were recently reviewed by Lu and Weng (2007), though none of the developed classifiers is described as inherently superior to any other, as their performance largely depends on the kind and quality of the input data for the classification and the desired output. Even unsupervised ISODATA classification has been used successfully, for example to extract specific, spectrally distinct features such as forests, fire scars, coastlines or urban areas (Ekercin, 2007, Heinl et al., 2006, Kaya and Curran, 2006, Souza et al., 2003). However, for obtaining thematic land cover data, supervised classification is to be preferred in most cases (Foody, 2001, Jensen, 2005, Kavzoglu, 2009), as desired output classes are already pre-defined and post-classification analyses and class aggregations are not necessarily required. Especially the use of advanced approaches such as artificial neural networks, fuzzy-sets or support vector machines produced levels of accuracy higher than, e.g. the popular maximum likelihood classifier or discriminant analysis (Berberoglu et al., 2007, Dixon and Candade, 2008, Jensen, 2005, Kavzoglu and Mather, 2003, Kavzoglu and Reis, 2008, Pal and Mather, 2005). But only few specific comparisons have been published (Berberoglu et al., 2007, Hardin, 2000, Kavzoglu and Reis, 2008, Paola and Schowengerdt, 1995, Zhang et al., 2007), usually documenting a superiority of the advanced approaches, but also suggesting maximum likelihood classification as better alternative (Carvalho et al., 2004). The use of different numbers and types of land cover classes and sample sizes complicates a quantitative comparison of the results. And despite the often documented inferiority in classification success, maximum likelihood classification is still one of the most widely used classification algorithms (Jensen, 2005), most likely also due to advantages in data handling and processing times (Paola and Schowengerdt, 1995). Therefore, many applied landscape-scale studies and land use/land cover research rely on these standard classification approaches (Brandt and Townsend, 2006, Cushman and Wallin, 2000, Jianchu et al., 2005, Joy et al., 2003, Ruiz-Luna and Berlanga-Robles, 2003). In contrast, advanced approaches are primarily limited to methodological studies for optimising the classification process, often using only very limited sample sizes (Fassnacht et al., 2006, Foody, 2001, Kavzoglu and Mather, 2003, Kavzoglu and Reis, 2008, Ouyang and Ma, 2006, Paola and Schowengerdt, 1997, Yemefack et al., 2006).

Besides the type of image classifier, the use of ancillary data is recognised as being crucial for the performance of image classification. Ancillary data have been used successfully to improve image classification, especially by including topographic measures, NDVI or texture measures in the classification process additionally to the spectral information for separating features with similar spectral properties (Berberoglu et al., 2007, Carpenter et al., 1999, Giannetti et al., 2001, Islam et al., 2008, Joy et al., 2003, Kozak et al., 2008, Lu and Weng, 2007, Saadat et al., 2008, Watanachaturaporn et al., 2008).

Despite extensive research on classifiers and ancillary data since decades, comparisons and applications of image classifiers using standardised samples on landscape-scale are largely missing (Lu and Weng, 2007). To overcome this discrepancy, the present study was conducted mutually both on the performance of different classifiers and on the importance of ancillary data for landscape-scale land cover assessments using pre-defined land cover classes. The present study investigates therefore the effect of a variety of selected and widely accessible input variables and classifiers on classification accuracy overall and on the level of specific land cover classes, and assesses and especially quantifies the importance of these components in image classification. We hypothesize that advanced classification approaches achieve higher overall accuracies compared to standard classifiers with little or no ancillary data, while incorporating ancillary data reduces the importance of the type of classifier. Specifically compared are the performance of maximum likelihood classification, discriminant analysis and artificial neural networks, covering presumably the most widely used hard classifiers and representing parametric and non-parametric classifiers. Ancillary data in the form of topographic measures and NDVI were incorporated step-wise into the classification to document the relevance of these input data. Classification results on the level of land cover classes are discussed in the context of reference data selection and land cover class definition.