## 1 Introduction  
Remote sensing images have been widely used in environmental monitoring and thematic mapping as they can provide periodic thematic information at various spatial and temporal scales [1; 2; 3; 4]. Image classification is regarded as one of the most important tasks in thematic mapping using remote sensing images [5; 6]. Thematic maps derived from the classification of remote sensing images, such as land-cover and crop/forest type maps, are typically used as inputs for physical and environmental models, thereby affecting the model outputs. Therefore, it is important to generate reliable and accurate thematic maps from remote sensing images [5]. Supervised classification is typically performed to derive various types of thematic maps from remote sensing images [7; 8]. The quality of supervised classification results is sensitive to many factors,such as available remote sensing images, classification methodologies, and training samples [9]. In particular, spatial distributions and the accuracy of supervised classification results depend significantly on the quantity and quality of training samples [9; 10; 11; 12]. Therefore, it is critical to collect training samples that provide useful information to correctly determine decision boundaries between classes of interest. In general, one of the most significant issues frequently encountered in image classification is the mixed pixel effect. A mixed pixel refers to a pixel containing more than one land-cover class [13]. This effect is prominent in the classification of mid/low spatial resolution remote sensing images. The conventional pixel-based supervised classification approach assumes that each training pixel represents spectral signatures of a single class (pure training pixel). When remote sensing images contain many mixed pixels, however, mixed pixels may be selected as training samples, which fail to provide the representative spectral signature of a certain class. Therefore, the mixed-pixel effect should be treated accordingly during classification [14]. To solve the mixed pixel effect problem, spectral unmixing or spectral mixture analysis has been widely applied to extract pure pixels from an image of interest [15; 16; 17; 18]. By applying spectral unmixing, pure pixels (also known as endmembers) are first extracted and then used as training samples for supervised classification [13]. In addition to spectral unmixing, other statistical approaches have been applied to collect representative training samples. For example, [PERSON] [19] selected representative training samples using spectral histogram and boundary analyses with dimension reduction. Conventional descriptive statistics including mean and standard deviation have also been used to extract pure training samples [20]. Despite the promising results, most of the aforementioned studies have focused mainly on the extraction of spectrally pure pixels. As many factors apart from spectral purity need to be considered, it is challenging to collect and select representative training samples. Spatial resolutions of images and the complexity of landscapes to be classified significantly affect training sample selection and consequently, classification performance. [PERSON] and [PERSON] [21] reported that more training samples are required to classify fine spatial resolution images than coarse ones, and that block-based training samples are recommended to classify heterogeneous landscapes. [PERSON] et al. [22] emphasized the impacts of landscape heterogeneity on classification performance, in addition to the impurity of training samples (compositional heterogeneity), when coarse spatial resolution images are used for crop classification. The results from previous studies indicate the necessity of considering the purity of training samples, landscape heterogeneity, and spatial resolutions for the appropriate selection of training samples.