# Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures  
[PERSON]  
1 Department of Electronic Systems, NTNU: Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway; [EMAIL_ADDRESS]  
2  
Milica Orlandic  
either supervised, such as embedded feature selection with support vector machines (EFS-SVM) [9], linear discriminant analysis (LDA) [10], or unsupervised, including principal component analysis (PCA) [11], manifold learning [12], and maximum noise factoring (MNF) [13]. There are a number of recent research studies that address the effect of different feature extraction techniques on HSI analysis [14; 15; 16]. Similar to unsupervised classification, unsupervised feature extraction methods are used in the absence of labeled training data. FE methods, such as PCA and MNF, are widely used unsupervised approaches for dimensionality reduction in HSI images [13]. PCA searches for maximum variance in the data, whereas MNF sorts the components based on the maximum estimated signal-to-noise ratio (SNR) [14]. In [11], PCA transform is applied to the original HSI cube prior to k-means clustering method. In another study, the improved classification accuracy of a spectral angle mapper (SAM) classifier is reported when applying MNF for pre-processing [17]. In addition to linear features, non-linearity is also present in HSI images, due to the nonlinear nature of multipath scattering in the atmosphere [18]. Isometric mapping (ISOMAP) [12], which is an unsupervised manifold learning approach that is based on nonlinear transformation, is applied to improve HSI classification of 1-nearest neighbor classifier. The study presents competitive results in PCA and ISOMAP comparison. A solution for the removal of the noisy or redundant bands as a part of HSI clustering is proposed in [19], where a local band selection approach identifies the effective subset of bands (relevant subspace) that are based on relevancy and redundancy among the spectral bands. The relevancy score of a band to a cluster is obtained using the average Euclidean distance between each cluster member and the cluster centroid along each dimension. Redundancy among the bands is defined by the use of the inter-band distances [19]. Image segmentation is used to incorporate spatial information into the classifiers assigning each image pixel to one class in order to tackle the complex spatial structures in hyperspectral images. Image segmentation, in general, is a process in which an image is partitioned into multiple regions, where pixels within a region have the same label and share the same features [20]. The regions form a segmentation map that can be used as spatial structures for a spectral-spatial classification technique [21]. In recent years, different segment-based clustering HSI classification frameworks [7; 19; 22] have been proposed. The general idea is to initially obtain a segmentation map, followed by applying clustering on the segments refined in the region merging process rather than directly on the HSI pixels. In [23], an HSI segmentation map is obtained using two partition clustering algorithms, namely projected clustering and correlation clustering. Projected clustering algorithms focus on the identification of subspace clusters, where each pixel is assigned to exactly one subspace cluster [24]. Similarly, correlation clustering forms clusters of pixels that are positively or negatively correlated based on a subset of relevant attributes in a high dimensional space [25]. The resulted cluster map is converted to a segmented map by making use of a connected component labelling (CCL) procedure. This procedure assigns different labels to pixels, which are in the same cluster, but disjoint regions. In a separate stage, pixel-level classification while using support vector machines (SVM) is performed on the input HSI.